![](\"images/NST_LOSS.png\")
\n",
- "\n",
- "**Exercise:** Compute the \"content cost\" using TensorFlow. \n",
- "\n",
- "**Instructions**: The 3 steps to implement this function are:\n",
- "1. Retrieve dimensions from a_G: \n",
- " - To retrieve dimensions from a tensor X, use: `X.get_shape().as_list()`\n",
- "2. Unroll a_C and a_G as explained in the picture above\n",
- " - If you are stuck, take a look at [Hint1](https://www.tensorflow.org/versions/r1.3/api_docs/python/tf/transpose) and [Hint2](https://www.tensorflow.org/versions/r1.2/api_docs/python/tf/reshape).\n",
- "3. Compute the content cost:\n",
- " - If you are stuck, take a look at [Hint3](https://www.tensorflow.org/api_docs/python/tf/reduce_sum), [Hint4](https://www.tensorflow.org/api_docs/python/tf/square) and [Hint5](https://www.tensorflow.org/api_docs/python/tf/subtract)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "# GRADED FUNCTION: compute_content_cost\n",
- "\n",
- "def compute_content_cost(a_C, a_G):\n",
- " \"\"\"\n",
- " Computes the content cost\n",
- " \n",
- " Arguments:\n",
- " a_C -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image C \n",
- " a_G -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image G\n",
- " \n",
- " Returns: \n",
- " J_content -- scalar that you compute using equation 1 above.\n",
- " \"\"\"\n",
- " \n",
- " ### START CODE HERE ###\n",
- " # Retrieve dimensions from a_G (≈1 line)\n",
- " m, n_H, n_W, n_C = a_G.get_shape().as_list()\n",
- " \n",
- " # Reshape a_C and a_G (≈2 lines)\n",
- " a_C_unrolled = tf.transpose(tf.reshape(a_C, [n_H*n_W,n_C]))\n",
- " a_G_unrolled = tf.transpose(tf.reshape(a_G, [n_H*n_W,n_C]))\n",
- " \n",
- " # compute the cost with tensorflow (≈1 line)\n",
- " J_content = tf.reduce_sum(tf.square(tf.subtract(a_C_unrolled,a_G_unrolled)))/(4*n_H*n_W*n_C)\n",
- " ### END CODE HERE ###\n",
- " \n",
- " return J_content"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "J_content = 6.76559\n"
- ]
- }
- ],
- "source": [
- "tf.reset_default_graph()\n",
- "\n",
- "with tf.Session() as test:\n",
- " tf.set_random_seed(1)\n",
- " a_C = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)\n",
- " a_G = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)\n",
- " \n",
- " J_content = compute_content_cost(a_C, a_G)\n",
- " print(\"J_content = \" + str(J_content.eval()))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Expected Output**:\n",
- "\n",
- "| \n", - " **J_content**\n", - " | \n", - "\n", - " 6.76559\n", - " | \n", - "
![](\"images/NST_GM.png\")
\n",
- "\n",
- "The result is a matrix of dimension $(n_C,n_C)$ where $n_C$ is the number of filters. The value $G_{ij}$ measures how similar the activations of filter $i$ are to the activations of filter $j$. \n",
- "\n",
- "One important part of the gram matrix is that the diagonal elements such as $G_{ii}$ also measures how active filter $i$ is. For example, suppose filter $i$ is detecting vertical textures in the image. Then $G_{ii}$ measures how common vertical textures are in the image as a whole: If $G_{ii}$ is large, this means that the image has a lot of vertical texture. \n",
- "\n",
- "By capturing the prevalence of different types of features ($G_{ii}$), as well as how much different features occur together ($G_{ij}$), the Style matrix $G$ measures the style of an image. \n",
- "\n",
- "**Exercise**:\n",
- "Using TensorFlow, implement a function that computes the Gram matrix of a matrix A. The formula is: The gram matrix of A is $G_A = AA^T$. If you are stuck, take a look at [Hint 1](https://www.tensorflow.org/api_docs/python/tf/matmul) and [Hint 2](https://www.tensorflow.org/api_docs/python/tf/transpose)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "# GRADED FUNCTION: gram_matrix\n",
- "\n",
- "def gram_matrix(A):\n",
- " \"\"\"\n",
- " Argument:\n",
- " A -- matrix of shape (n_C, n_H*n_W)\n",
- " \n",
- " Returns:\n",
- " GA -- Gram matrix of A, of shape (n_C, n_C)\n",
- " \"\"\"\n",
- " \n",
- " ### START CODE HERE ### (≈1 line)\n",
- " GA = tf.matmul(A, tf.transpose(A))\n",
- " ### END CODE HERE ###\n",
- " \n",
- " return GA"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "GA = [[ 6.42230511 -4.42912197 -2.09668207]\n",
- " [ -4.42912197 19.46583748 19.56387138]\n",
- " [ -2.09668207 19.56387138 20.6864624 ]]\n"
- ]
- }
- ],
- "source": [
- "tf.reset_default_graph()\n",
- "\n",
- "with tf.Session() as test:\n",
- " tf.set_random_seed(1)\n",
- " A = tf.random_normal([3, 2*1], mean=1, stddev=4)\n",
- " GA = gram_matrix(A)\n",
- " \n",
- " print(\"GA = \" + str(GA.eval()))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Expected Output**:\n",
- "\n",
- "| \n", - " **GA**\n", - " | \n", - "\n",
- " [[ 6.42230511 -4.42912197 -2.09668207] \n", - " [ -4.42912197 19.46583748 19.56387138] \n", - " [ -2.09668207 19.56387138 20.6864624 ]]\n", - " | \n",
- "
| \n", - " **J_style_layer**\n", - " | \n", - "\n", - " 9.19028\n", - " | \n", - "
| \n", - " **J**\n", - " | \n", - "\n", - " 35.34667875478276\n", - " | \n", - "
| \n", - " **Iteration 0 : **\n", - " | \n", - "\n",
- " total cost = 5.05035e+09 \n", - " content cost = 7877.67 \n", - " style cost = 1.26257e+08\n", - " | \n",
- "
![](\"images/louvre_generated.png\")
\n",
- "\n",
- "We didn't want you to wait too long to see an initial result, and so had set the hyperparameters accordingly. To get the best looking results, running the optimization algorithm longer (and perhaps with a smaller learning rate) might work better. After completing and submitting this assignment, we encourage you to come back and play more with this notebook, and see if you can generate even better looking images. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here are few other examples:\n",
- "\n",
- "- The beautiful ruins of the ancient city of Persepolis (Iran) with the style of Van Gogh (The Starry Night)\n",
- "![](\"images/perspolis_vangogh.png\")
\n",
- "\n",
- "- The tomb of Cyrus the great in Pasargadae with the style of a Ceramic Kashi from Ispahan.\n",
- "![](\"images/pasargad_kashi.png\")
\n",
- "\n",
- "- A scientific study of a turbulent fluid with the style of a abstract blue fluid painting.\n",
- "![](\"images/circle_abstract.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 5 - Test with your own image (Optional/Ungraded)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, you can also rerun the algorithm on your own images! \n",
- "\n",
- "To do so, go back to part 4 and change the content image and style image with your own pictures. In detail, here's what you should do:\n",
- "\n",
- "1. Click on \"File -> Open\" in the upper tab of the notebook\n",
- "2. Go to \"/images\" and upload your images (requirement: (WIDTH = 300, HEIGHT = 225)), rename them \"my_content.png\" and \"my_style.png\" for example.\n",
- "3. Change the code in part (3.4) from :\n",
- "```python\n",
- "content_image = scipy.misc.imread(\"images/louvre.jpg\")\n",
- "style_image = scipy.misc.imread(\"images/claude-monet.jpg\")\n",
- "```\n",
- "to:\n",
- "```python\n",
- "content_image = scipy.misc.imread(\"images/my_content.jpg\")\n",
- "style_image = scipy.misc.imread(\"images/my_style.jpg\")\n",
- "```\n",
- "4. Rerun the cells (you may need to restart the Kernel in the upper tab of the notebook).\n",
- "\n",
- "You can also tune your hyperparameters: \n",
- "- Which layers are responsible for representing the style? STYLE_LAYERS\n",
- "- How many iterations do you want to run the algorithm? num_iterations\n",
- "- What is the relative weighting between content and style? alpha/beta"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 6 - Conclusion\n",
- "\n",
- "Great job on completing this assignment! You are now able to use Neural Style Transfer to generate artistic images. This is also your first time building a model in which the optimization algorithm updates the pixel values rather than the neural network's parameters. Deep learning has many different types of models and this is only one of them! \n",
- "\n",
- "\n",
- "What you should remember:\n",
- "- Neural Style Transfer is an algorithm that given a content image C and a style image S can generate an artistic image\n",
- "- It uses representations (hidden layer activations) based on a pretrained ConvNet. \n",
- "- The content cost function is computed using one hidden layer's activations.\n",
- "- The style cost function for one layer is computed using the Gram matrix of that layer's activations. The overall style cost function is obtained using several hidden layers.\n",
- "- Optimizing the total cost function results in synthesizing new images. \n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This was the final programming exercise of this course. Congratulations--you've finished all the programming exercises of this course on Convolutional Networks! We hope to also see you in Course 5, on Sequence models! \n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": true
- },
- "source": [
- "### References:\n",
- "\n",
- "The Neural Style Transfer algorithm was due to Gatys et al. (2015). Harish Narayanan and Github user \"log0\" also have highly readable write-ups from which we drew inspiration. The pre-trained network used in this implementation is a VGG network, which is due to Simonyan and Zisserman (2015). Pre-trained weights were from the work of the MathConvNet team. \n",
- "\n",
- "- Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, (2015). A Neural Algorithm of Artistic Style (https://arxiv.org/abs/1508.06576) \n",
- "- Harish Narayanan, Convolutional neural networks for artistic style transfer. https://harishnarayanan.org/writing/artistic-style-transfer/\n",
- "- Log0, TensorFlow Implementation of \"A Neural Algorithm of Artistic Style\". http://www.chioka.in/tensorflow-implementation-neural-algorithm-of-artistic-style\n",
- "- Karen Simonyan and Andrew Zisserman (2015). Very deep convolutional networks for large-scale image recognition (https://arxiv.org/pdf/1409.1556.pdf)\n",
- "- MatConvNet. http://www.vlfeat.org/matconvnet/pretrained/\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "coursera": {
- "course_slug": "convolutional-neural-networks",
- "graded_item_id": "owWbQ",
- "launcher_item_id": "lEthw"
- },
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.0"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/Art+Generation+with+Neural+Style+Transfer+-+v2.py b/Art+Generation+with+Neural+Style+Transfer+-+v2.py
deleted file mode 100644
index 61b0d95..0000000
--- a/Art+Generation+with+Neural+Style+Transfer+-+v2.py
+++ /dev/null
@@ -1,754 +0,0 @@
-
-# coding: utf-8
-
-# # Deep Learning & Art: Neural Style Transfer
-#
-# Welcome to the second assignment of this week. In this assignment, you will learn about Neural Style Transfer. This algorithm was created by Gatys et al. (2015) (https://arxiv.org/abs/1508.06576).
-#
-# **In this assignment, you will:**
-# - Implement the neural style transfer algorithm
-# - Generate novel artistic images using your algorithm
-#
-# Most of the algorithms you've studied optimize a cost function to get a set of parameter values. In Neural Style Transfer, you'll optimize a cost function to get pixel values!
-
-# In[1]:
-
-import os
-import sys
-import scipy.io
-import scipy.misc
-import matplotlib.pyplot as plt
-from matplotlib.pyplot import imshow
-from PIL import Image
-from nst_utils import *
-import numpy as np
-import tensorflow as tf
-
-get_ipython().magic('matplotlib inline')
-
-
-# ## 1 - Problem Statement
-#
-# Neural Style Transfer (NST) is one of the most fun techniques in deep learning. As seen below, it merges two images, namely, a "content" image (C) and a "style" image (S), to create a "generated" image (G). The generated image G combines the "content" of the image C with the "style" of image S.
-#
-# In this example, you are going to generate an image of the Louvre museum in Paris (content image C), mixed with a painting by Claude Monet, a leader of the impressionist movement (style image S).
-# 
-#
-# Let's see how you can do this.
-
-# ## 2 - Transfer Learning
-#
-# Neural Style Transfer (NST) uses a previously trained convolutional network, and builds on top of that. The idea of using a network trained on a different task and applying it to a new task is called transfer learning.
-#
-# Following the original NST paper (https://arxiv.org/abs/1508.06576), we will use the VGG network. Specifically, we'll use VGG-19, a 19-layer version of the VGG network. This model has already been trained on the very large ImageNet database, and thus has learned to recognize a variety of low level features (at the earlier layers) and high level features (at the deeper layers).
-#
-# Run the following code to load parameters from the VGG model. This may take a few seconds.
-
-# In[2]:
-
-model = load_vgg_model("pretrained-model/imagenet-vgg-verydeep-19.mat")
-print(model)
-
-
-# The model is stored in a python dictionary where each variable name is the key and the corresponding value is a tensor containing that variable's value. To run an image through this network, you just have to feed the image to the model. In TensorFlow, you can do so using the [tf.assign](https://www.tensorflow.org/api_docs/python/tf/assign) function. In particular, you will use the assign function like this:
-# ```python
-# model["input"].assign(image)
-# ```
-# This assigns the image as an input to the model. After this, if you want to access the activations of a particular layer, say layer `4_2` when the network is run on this image, you would run a TensorFlow session on the correct tensor `conv4_2`, as follows:
-# ```python
-# sess.run(model["conv4_2"])
-# ```
-
-# ## 3 - Neural Style Transfer
-#
-# We will build the NST algorithm in three steps:
-#
-# - Build the content cost function $J_{content}(C,G)$
-# - Build the style cost function $J_{style}(S,G)$
-# - Put it together to get $J(G) = \alpha J_{content}(C,G) + \beta J_{style}(S,G)$.
-#
-# ### 3.1 - Computing the content cost
-#
-# In our running example, the content image C will be the picture of the Louvre Museum in Paris. Run the code below to see a picture of the Louvre.
-
-# In[3]:
-
-content_image = scipy.misc.imread("images/louvre.jpg")
-imshow(content_image)
-
-
-# The content image (C) shows the Louvre museum's pyramid surrounded by old Paris buildings, against a sunny sky with a few clouds.
-#
-# ** 3.1.1 - How do you ensure the generated image G matches the content of the image C?**
-#
-# As we saw in lecture, the earlier (shallower) layers of a ConvNet tend to detect lower-level features such as edges and simple textures, and the later (deeper) layers tend to detect higher-level features such as more complex textures as well as object classes.
-#
-# We would like the "generated" image G to have similar content as the input image C. Suppose you have chosen some layer's activations to represent the content of an image. In practice, you'll get the most visually pleasing results if you choose a layer in the middle of the network--neither too shallow nor too deep. (After you have finished this exercise, feel free to come back and experiment with using different layers, to see how the results vary.)
-#
-# So, suppose you have picked one particular hidden layer to use. Now, set the image C as the input to the pretrained VGG network, and run forward propagation. Let $a^{(C)}$ be the hidden layer activations in the layer you had chosen. (In lecture, we had written this as $a^{[l](C)}$, but here we'll drop the superscript $[l]$ to simplify the notation.) This will be a $n_H \times n_W \times n_C$ tensor. Repeat this process with the image G: Set G as the input, and run forward progation. Let $$a^{(G)}$$ be the corresponding hidden layer activation. We will define as the content cost function as:
-#
-# $$J_{content}(C,G) = \frac{1}{4 \times n_H \times n_W \times n_C}\sum _{ \text{all entries}} (a^{(C)} - a^{(G)})^2\tag{1} $$
-#
-# Here, $n_H, n_W$ and $n_C$ are the height, width and number of channels of the hidden layer you have chosen, and appear in a normalization term in the cost. For clarity, note that $a^{(C)}$ and $a^{(G)}$ are the volumes corresponding to a hidden layer's activations. In order to compute the cost $J_{content}(C,G)$, it might also be convenient to unroll these 3D volumes into a 2D matrix, as shown below. (Technically this unrolling step isn't needed to compute $J_{content}$, but it will be good practice for when you do need to carry out a similar operation later for computing the style const $J_{style}$.)
-#
-# 
-#
-# **Exercise:** Compute the "content cost" using TensorFlow.
-#
-# **Instructions**: The 3 steps to implement this function are:
-# 1. Retrieve dimensions from a_G:
-# - To retrieve dimensions from a tensor X, use: `X.get_shape().as_list()`
-# 2. Unroll a_C and a_G as explained in the picture above
-# - If you are stuck, take a look at [Hint1](https://www.tensorflow.org/versions/r1.3/api_docs/python/tf/transpose) and [Hint2](https://www.tensorflow.org/versions/r1.2/api_docs/python/tf/reshape).
-# 3. Compute the content cost:
-# - If you are stuck, take a look at [Hint3](https://www.tensorflow.org/api_docs/python/tf/reduce_sum), [Hint4](https://www.tensorflow.org/api_docs/python/tf/square) and [Hint5](https://www.tensorflow.org/api_docs/python/tf/subtract).
-
-# In[4]:
-
-# GRADED FUNCTION: compute_content_cost
-
-def compute_content_cost(a_C, a_G):
- """
- Computes the content cost
-
- Arguments:
- a_C -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image C
- a_G -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image G
-
- Returns:
- J_content -- scalar that you compute using equation 1 above.
- """
-
- ### START CODE HERE ###
- # Retrieve dimensions from a_G (≈1 line)
- m, n_H, n_W, n_C = a_G.get_shape().as_list()
-
- # Reshape a_C and a_G (≈2 lines)
- a_C_unrolled = tf.transpose(tf.reshape(a_C, [n_H*n_W,n_C]))
- a_G_unrolled = tf.transpose(tf.reshape(a_G, [n_H*n_W,n_C]))
-
- # compute the cost with tensorflow (≈1 line)
- J_content = tf.reduce_sum(tf.square(tf.subtract(a_C_unrolled,a_G_unrolled)))/(4*n_H*n_W*n_C)
- ### END CODE HERE ###
-
- return J_content
-
-
-# In[5]:
-
-tf.reset_default_graph()
-
-with tf.Session() as test:
- tf.set_random_seed(1)
- a_C = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)
- a_G = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)
-
- J_content = compute_content_cost(a_C, a_G)
- print("J_content = " + str(J_content.eval()))
-
-
-# **Expected Output**:
-#
-# | -# **J_content** -# | -#-# 6.76559 -# | -#
-#
-# The result is a matrix of dimension $(n_C,n_C)$ where $n_C$ is the number of filters. The value $G_{ij}$ measures how similar the activations of filter $i$ are to the activations of filter $j$.
-#
-# One important part of the gram matrix is that the diagonal elements such as $G_{ii}$ also measures how active filter $i$ is. For example, suppose filter $i$ is detecting vertical textures in the image. Then $G_{ii}$ measures how common vertical textures are in the image as a whole: If $G_{ii}$ is large, this means that the image has a lot of vertical texture.
-#
-# By capturing the prevalence of different types of features ($G_{ii}$), as well as how much different features occur together ($G_{ij}$), the Style matrix $G$ measures the style of an image.
-#
-# **Exercise**:
-# Using TensorFlow, implement a function that computes the Gram matrix of a matrix A. The formula is: The gram matrix of A is $G_A = AA^T$. If you are stuck, take a look at [Hint 1](https://www.tensorflow.org/api_docs/python/tf/matmul) and [Hint 2](https://www.tensorflow.org/api_docs/python/tf/transpose).
-
-# In[7]:
-
-# GRADED FUNCTION: gram_matrix
-
-def gram_matrix(A):
- """
- Argument:
- A -- matrix of shape (n_C, n_H*n_W)
-
- Returns:
- GA -- Gram matrix of A, of shape (n_C, n_C)
- """
-
- ### START CODE HERE ### (≈1 line)
- GA = tf.matmul(A, tf.transpose(A))
- ### END CODE HERE ###
-
- return GA
-
-
-# In[8]:
-
-tf.reset_default_graph()
-
-with tf.Session() as test:
- tf.set_random_seed(1)
- A = tf.random_normal([3, 2*1], mean=1, stddev=4)
- GA = gram_matrix(A)
-
- print("GA = " + str(GA.eval()))
-
-
-# **Expected Output**:
-#
-# | -# **GA** -# | -#
-# [[ 6.42230511 -4.42912197 -2.09668207] -# [ -4.42912197 19.46583748 19.56387138] -# [ -2.09668207 19.56387138 20.6864624 ]] -# |
-#
| -# **J_style_layer** -# | -#-# 9.19028 -# | -#
| -# **J** -# | -#-# 35.34667875478276 -# | -#
| -# **Iteration 0 : ** -# | -#
-# total cost = 5.05035e+09 -# content cost = 7877.67 -# style cost = 1.26257e+08 -# |
-#
-#
-# We didn't want you to wait too long to see an initial result, and so had set the hyperparameters accordingly. To get the best looking results, running the optimization algorithm longer (and perhaps with a smaller learning rate) might work better. After completing and submitting this assignment, we encourage you to come back and play more with this notebook, and see if you can generate even better looking images.
-
-# Here are few other examples:
-#
-# - The beautiful ruins of the ancient city of Persepolis (Iran) with the style of Van Gogh (The Starry Night)
-# 
-#
-# - The tomb of Cyrus the great in Pasargadae with the style of a Ceramic Kashi from Ispahan.
-# 
-#
-# - A scientific study of a turbulent fluid with the style of a abstract blue fluid painting.
-# 
-
-# ## 5 - Test with your own image (Optional/Ungraded)
-
-# Finally, you can also rerun the algorithm on your own images!
-#
-# To do so, go back to part 4 and change the content image and style image with your own pictures. In detail, here's what you should do:
-#
-# 1. Click on "File -> Open" in the upper tab of the notebook
-# 2. Go to "/images" and upload your images (requirement: (WIDTH = 300, HEIGHT = 225)), rename them "my_content.png" and "my_style.png" for example.
-# 3. Change the code in part (3.4) from :
-# ```python
-# content_image = scipy.misc.imread("images/louvre.jpg")
-# style_image = scipy.misc.imread("images/claude-monet.jpg")
-# ```
-# to:
-# ```python
-# content_image = scipy.misc.imread("images/my_content.jpg")
-# style_image = scipy.misc.imread("images/my_style.jpg")
-# ```
-# 4. Rerun the cells (you may need to restart the Kernel in the upper tab of the notebook).
-#
-# You can also tune your hyperparameters:
-# - Which layers are responsible for representing the style? STYLE_LAYERS
-# - How many iterations do you want to run the algorithm? num_iterations
-# - What is the relative weighting between content and style? alpha/beta
-
-# ## 6 - Conclusion
-#
-# Great job on completing this assignment! You are now able to use Neural Style Transfer to generate artistic images. This is also your first time building a model in which the optimization algorithm updates the pixel values rather than the neural network's parameters. Deep learning has many different types of models and this is only one of them!
-#
-#
-# What you should remember:
-# - Neural Style Transfer is an algorithm that given a content image C and a style image S can generate an artistic image
-# - It uses representations (hidden layer activations) based on a pretrained ConvNet.
-# - The content cost function is computed using one hidden layer's activations.
-# - The style cost function for one layer is computed using the Gram matrix of that layer's activations. The overall style cost function is obtained using several hidden layers.
-# - Optimizing the total cost function results in synthesizing new images.
-#
-#
-#
-
-# This was the final programming exercise of this course. Congratulations--you've finished all the programming exercises of this course on Convolutional Networks! We hope to also see you in Course 5, on Sequence models!
-#
-
-# ### References:
-#
-# The Neural Style Transfer algorithm was due to Gatys et al. (2015). Harish Narayanan and Github user "log0" also have highly readable write-ups from which we drew inspiration. The pre-trained network used in this implementation is a VGG network, which is due to Simonyan and Zisserman (2015). Pre-trained weights were from the work of the MathConvNet team.
-#
-# - Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, (2015). A Neural Algorithm of Artistic Style (https://arxiv.org/abs/1508.06576)
-# - Harish Narayanan, Convolutional neural networks for artistic style transfer. https://harishnarayanan.org/writing/artistic-style-transfer/
-# - Log0, TensorFlow Implementation of "A Neural Algorithm of Artistic Style". http://www.chioka.in/tensorflow-implementation-neural-algorithm-of-artistic-style
-# - Karen Simonyan and Andrew Zisserman (2015). Very deep convolutional networks for large-scale image recognition (https://arxiv.org/pdf/1409.1556.pdf)
-# - MatConvNet. http://www.vlfeat.org/matconvnet/pretrained/
-#
-
-# In[ ]:
-
-
-
diff --git a/Autonomous+driving+application+-+Car+detection+-+v1.ipynb b/Autonomous+driving+application+-+Car+detection+-+v1.ipynb
deleted file mode 100644
index 55dc89d..0000000
--- a/Autonomous+driving+application+-+Car+detection+-+v1.ipynb
+++ /dev/null
@@ -1,1408 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Autonomous driving - Car detection\n",
- "\n",
- "Welcome to your week 3 programming assignment. You will learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: Redmon et al., 2016 (https://arxiv.org/abs/1506.02640) and Redmon and Farhadi, 2016 (https://arxiv.org/abs/1612.08242). \n",
- "\n",
- "**You will learn to**:\n",
- "- Use object detection on a car detection dataset\n",
- "- Deal with bounding boxes\n",
- "\n",
- "Run the following cell to load the packages and dependencies that are going to be useful for your journey!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- }
- ],
- "source": [
- "import argparse\n",
- "import os\n",
- "import matplotlib.pyplot as plt\n",
- "from matplotlib.pyplot import imshow\n",
- "import scipy.io\n",
- "import scipy.misc\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "import PIL\n",
- "import tensorflow as tf\n",
- "from keras import backend as K\n",
- "from keras.layers import Input, Lambda, Conv2D\n",
- "from keras.models import load_model, Model\n",
- "from yolo_utils import read_classes, read_anchors, generate_colors, preprocess_image, draw_boxes, scale_boxes\n",
- "from yad2k.models.keras_yolo import yolo_head, yolo_boxes_to_corners, preprocess_true_boxes, yolo_loss, yolo_body\n",
- "\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Important Note**: As you can see, we import Keras's backend as K. This means that to use a Keras function in this notebook, you will need to write: `K.function(...)`."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1 - Problem Statement\n",
- "\n",
- "You are working on a self-driving car. As a critical component of this project, you'd like to first build a car detection system. To collect data, you've mounted a camera to the hood (meaning the front) of the car, which takes pictures of the road ahead every few seconds while you drive around. \n",
- "\n",
- "We would like to especially thank [drive.ai](https://www.drive.ai/) for providing this dataset! Drive.ai is a company building the brains of self-driving vehicles.\n", - "
![](\"nb_images/driveai.png\")
\n",
- "\n",
- "You've gathered all these images into a folder and have labelled them by drawing bounding boxes around every car you found. Here's an example of what your bounding boxes look like.\n",
- "\n",
- "![](\"nb_images/box_label.png\")
\n",
- "![](\"nb_images/architecture.png\")
\n",
- "![](\"nb_images/flatten.png\")
\n",
- "![](\"nb_images/probability_extraction.png\")
\n",
- "![](\"nb_images/proba_map.png\")
\n",
- "![](\"nb_images/anchor_map.png\")
\n",
- "| \n", - " **scores[2]**\n", - " | \n", - "\n", - " 10.7506\n", - " | \n", - "
| \n", - " **boxes[2]**\n", - " | \n", - "\n", - " [ 8.42653275 3.27136683 -0.5313437 -4.94137383]\n", - " | \n", - "
| \n", - " **classes[2]**\n", - " | \n", - "\n", - " 7\n", - " | \n", - "
| \n", - " **scores.shape**\n", - " | \n", - "\n", - " (?,)\n", - " | \n", - "
| \n", - " **boxes.shape**\n", - " | \n", - "\n", - " (?, 4)\n", - " | \n", - "
| \n", - " **classes.shape**\n", - " | \n", - "\n", - " (?,)\n", - " | \n", - "
![](\"nb_images/non-max-suppression.png\")
\n",
- "![](\"nb_images/iou.png\")
\n",
- "| \n", - " **iou = **\n", - " | \n", - "\n", - " 0.14285714285714285\n", - " | \n", - "
| \n", - " **scores[2]**\n", - " | \n", - "\n", - " 6.9384\n", - " | \n", - "
| \n", - " **boxes[2]**\n", - " | \n", - "\n", - " [-5.299932 3.13798141 4.45036697 0.95942086]\n", - " | \n", - "
| \n", - " **classes[2]**\n", - " | \n", - "\n", - " -2.24527\n", - " | \n", - "
| \n", - " **scores.shape**\n", - " | \n", - "\n", - " (10,)\n", - " | \n", - "
| \n", - " **boxes.shape**\n", - " | \n", - "\n", - " (10, 4)\n", - " | \n", - "
| \n", - " **classes.shape**\n", - " | \n", - "\n", - " (10,)\n", - " | \n", - "
| \n", - " **scores[2]**\n", - " | \n", - "\n", - " 138.791\n", - " | \n", - "
| \n", - " **boxes[2]**\n", - " | \n", - "\n", - " [ 1292.32971191 -278.52166748 3876.98925781 -835.56494141]\n", - " | \n", - "
| \n", - " **classes[2]**\n", - " | \n", - "\n", - " 54\n", - " | \n", - "
| \n", - " **scores.shape**\n", - " | \n", - "\n", - " (10,)\n", - " | \n", - "
| \n", - " **boxes.shape**\n", - " | \n", - "\n", - " (10, 4)\n", - " | \n", - "
| \n", - " **classes.shape**\n", - " | \n", - "\n", - " (10,)\n", - " | \n", - "
| \n", - " **Found 7 boxes for test.jpg**\n", - " | \n", - "|
| \n", - " **car**\n", - " | \n", - "\n", - " 0.60 (925, 285) (1045, 374)\n", - " | \n", - "
| \n", - " **car**\n", - " | \n", - "\n", - " 0.66 (706, 279) (786, 350)\n", - " | \n", - "
| \n", - " **bus**\n", - " | \n", - "\n", - " 0.67 (5, 266) (220, 407)\n", - " | \n", - "
| \n", - " **car**\n", - " | \n", - "\n", - " 0.70 (947, 324) (1280, 705)\n", - " | \n", - "
| \n", - " **car**\n", - " | \n", - "\n", - " 0.74 (159, 303) (346, 440)\n", - " | \n", - "
| \n", - " **car**\n", - " | \n", - "\n", - " 0.80 (761, 282) (942, 412)\n", - " | \n", - "
| \n", - " **car**\n", - " | \n", - "\n", - " 0.89 (367, 300) (745, 648)\n", - " | \n", - "
Thanks [drive.ai](https://www.drive.ai/) for providing this dataset!
![\"Creative](\"https://i.creativecommons.org/l/by/4.0/88x31.png\")
The Drive.ai Sample Dataset (provided by drive.ai) is licensed under a Creative Commons Attribution 4.0 International License. We are especially grateful to Brody Huval, Chih Hu and Rahul Patel for collecting and providing this dataset. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "coursera": { - "course_slug": "convolutional-neural-networks", - "graded_item_id": "OMdut", - "launcher_item_id": "bbBOL" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Autonomous+driving+application+-+Car+detection+-+v1.py b/Autonomous+driving+application+-+Car+detection+-+v1.py deleted file mode 100644 index 5b0c3e5..0000000 --- a/Autonomous+driving+application+-+Car+detection+-+v1.py +++ /dev/null @@ -1,851 +0,0 @@ - -# coding: utf-8 - -# # Autonomous driving - Car detection -# -# Welcome to your week 3 programming assignment. You will learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: Redmon et al., 2016 (https://arxiv.org/abs/1506.02640) and Redmon and Farhadi, 2016 (https://arxiv.org/abs/1612.08242). -# -# **You will learn to**: -# - Use object detection on a car detection dataset -# - Deal with bounding boxes -# -# Run the following cell to load the packages and dependencies that are going to be useful for your journey! - -# In[3]: - -import argparse -import os -import matplotlib.pyplot as plt -from matplotlib.pyplot import imshow -import scipy.io -import scipy.misc -import numpy as np -import pandas as pd -import PIL -import tensorflow as tf -from keras import backend as K -from keras.layers import Input, Lambda, Conv2D -from keras.models import load_model, Model -from yolo_utils import read_classes, read_anchors, generate_colors, preprocess_image, draw_boxes, scale_boxes -from yad2k.models.keras_yolo import yolo_head, yolo_boxes_to_corners, preprocess_true_boxes, yolo_loss, yolo_body - -get_ipython().magic('matplotlib inline') - - -# **Important Note**: As you can see, we import Keras's backend as K. This means that to use a Keras function in this notebook, you will need to write: `K.function(...)`. - -# ## 1 - Problem Statement -# -# You are working on a self-driving car. As a critical component of this project, you'd like to first build a car detection system. To collect data, you've mounted a camera to the hood (meaning the front) of the car, which takes pictures of the road ahead every few seconds while you drive around. -# -#
We would like to especially thank [drive.ai](https://www.drive.ai/) for providing this dataset! Drive.ai is a company building the brains of self-driving vehicles. -#
-#
-# You've gathered all these images into a folder and have labelled them by drawing bounding boxes around every car you found. Here's an example of what your bounding boxes look like.
-#
-# 
-# 
-# 
-# 
-# 
-# 
-# | -# **scores[2]** -# | -#-# 10.7506 -# | -#
| -# **boxes[2]** -# | -#-# [ 8.42653275 3.27136683 -0.5313437 -4.94137383] -# | -#
| -# **classes[2]** -# | -#-# 7 -# | -#
| -# **scores.shape** -# | -#-# (?,) -# | -#
| -# **boxes.shape** -# | -#-# (?, 4) -# | -#
| -# **classes.shape** -# | -#-# (?,) -# | -#
-# 
-# | -# **iou = ** -# | -#-# 0.14285714285714285 -# | -#
| -# **scores[2]** -# | -#-# 6.9384 -# | -#
| -# **boxes[2]** -# | -#-# [-5.299932 3.13798141 4.45036697 0.95942086] -# | -#
| -# **classes[2]** -# | -#-# -2.24527 -# | -#
| -# **scores.shape** -# | -#-# (10,) -# | -#
| -# **boxes.shape** -# | -#-# (10, 4) -# | -#
| -# **classes.shape** -# | -#-# (10,) -# | -#
| -# **scores[2]** -# | -#-# 138.791 -# | -#
| -# **boxes[2]** -# | -#-# [ 1292.32971191 -278.52166748 3876.98925781 -835.56494141] -# | -#
| -# **classes[2]** -# | -#-# 54 -# | -#
| -# **scores.shape** -# | -#-# (10,) -# | -#
| -# **boxes.shape** -# | -#-# (10, 4) -# | -#
| -# **classes.shape** -# | -#-# (10,) -# | -#
| -# **Found 7 boxes for test.jpg** -# | -#|
| -# **car** -# | -#-# 0.60 (925, 285) (1045, 374) -# | -#
| -# **car** -# | -#-# 0.66 (706, 279) (786, 350) -# | -#
| -# **bus** -# | -#-# 0.67 (5, 266) (220, 407) -# | -#
| -# **car** -# | -#-# 0.70 (947, 324) (1280, 705) -# | -#
| -# **car** -# | -#-# 0.74 (159, 303) (346, 440) -# | -#
| -# **car** -# | -#-# 0.80 (761, 282) (942, 412) -# | -#
| -# **car** -# | -#-# 0.89 (367, 300) (745, 648) -# | -#
Thanks [drive.ai](https://www.drive.ai/) for providing this dataset!
The Drive.ai Sample Dataset (provided by drive.ai) is licensed under a Creative Commons Attribution 4.0 International License. We are especially grateful to Brody Huval, Chih Hu and Rahul Patel for collecting and providing this dataset. - -# In[ ]: - - - diff --git a/Building+your+Deep+Neural+Network+-+Step+by+Step+v5.py b/Building+your+Deep+Neural+Network+-+Step+by+Step+v5.py deleted file mode 100644 index 08eee6a..0000000 --- a/Building+your+Deep+Neural+Network+-+Step+by+Step+v5.py +++ /dev/null @@ -1,1067 +0,0 @@ - -# coding: utf-8 - -# # Building your Deep Neural Network: Step by Step -# -# Welcome to your week 4 assignment (part 1 of 2)! You have previously trained a 2-layer Neural Network (with a single hidden layer). This week, you will build a deep neural network, with as many layers as you want! -# -# - In this notebook, you will implement all the functions required to build a deep neural network. -# - In the next assignment, you will use these functions to build a deep neural network for image classification. -# -# **After this assignment you will be able to:** -# - Use non-linear units like ReLU to improve your model -# - Build a deeper neural network (with more than 1 hidden layer) -# - Implement an easy-to-use neural network class -# -# **Notation**: -# - Superscript $[l]$ denotes a quantity associated with the $l^{th}$ layer. -# - Example: $a^{[L]}$ is the $L^{th}$ layer activation. $W^{[L]}$ and $b^{[L]}$ are the $L^{th}$ layer parameters. -# - Superscript $(i)$ denotes a quantity associated with the $i^{th}$ example. -# - Example: $x^{(i)}$ is the $i^{th}$ training example. -# - Lowerscript $i$ denotes the $i^{th}$ entry of a vector. -# - Example: $a^{[l]}_i$ denotes the $i^{th}$ entry of the $l^{th}$ layer's activations). -# -# Let's get started! - -# ## 1 - Packages -# -# Let's first import all the packages that you will need during this assignment. -# - [numpy](www.numpy.org) is the main package for scientific computing with Python. -# - [matplotlib](http://matplotlib.org) is a library to plot graphs in Python. -# - dnn_utils provides some necessary functions for this notebook. -# - testCases provides some test cases to assess the correctness of your functions -# - np.random.seed(1) is used to keep all the random function calls consistent. It will help us grade your work. Please don't change the seed. - -# In[15]: - -import numpy as np -import h5py -import matplotlib.pyplot as plt -from testCases_v3 import * -from dnn_utils_v2 import sigmoid, sigmoid_backward, relu, relu_backward - -get_ipython().magic('matplotlib inline') -plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots -plt.rcParams['image.interpolation'] = 'nearest' -plt.rcParams['image.cmap'] = 'gray' - -get_ipython().magic('load_ext autoreload') -get_ipython().magic('autoreload 2') - -np.random.seed(1) - - -# ## 2 - Outline of the Assignment -# -# To build your neural network, you will be implementing several "helper functions". These helper functions will be used in the next assignment to build a two-layer neural network and an L-layer neural network. Each small helper function you will implement will have detailed instructions that will walk you through the necessary steps. Here is an outline of this assignment, you will: -# -# - Initialize the parameters for a two-layer network and for an $L$-layer neural network. -# - Implement the forward propagation module (shown in purple in the figure below). -# - Complete the LINEAR part of a layer's forward propagation step (resulting in $Z^{[l]}$). -# - We give you the ACTIVATION function (relu/sigmoid). -# - Combine the previous two steps into a new [LINEAR->ACTIVATION] forward function. -# - Stack the [LINEAR->RELU] forward function L-1 time (for layers 1 through L-1) and add a [LINEAR->SIGMOID] at the end (for the final layer $L$). This gives you a new L_model_forward function. -# - Compute the loss. -# - Implement the backward propagation module (denoted in red in the figure below). -# - Complete the LINEAR part of a layer's backward propagation step. -# - We give you the gradient of the ACTIVATE function (relu_backward/sigmoid_backward) -# - Combine the previous two steps into a new [LINEAR->ACTIVATION] backward function. -# - Stack [LINEAR->RELU] backward L-1 times and add [LINEAR->SIGMOID] backward in a new L_model_backward function -# - Finally update the parameters. -# -#
-# -# -# -# **Note** that for every forward function, there is a corresponding backward function. That is why at every step of your forward module you will be storing some values in a cache. The cached values are useful for computing gradients. In the backpropagation module you will then use the cache to calculate the gradients. This assignment will show you exactly how to carry out each of these steps. - -# ## 3 - Initialization -# -# You will write two helper functions that will initialize the parameters for your model. The first function will be used to initialize parameters for a two layer model. The second one will generalize this initialization process to $L$ layers. -# -# ### 3.1 - 2-layer Neural Network -# -# **Exercise**: Create and initialize the parameters of the 2-layer neural network. -# -# **Instructions**: -# - The model's structure is: *LINEAR -> RELU -> LINEAR -> SIGMOID*. -# - Use random initialization for the weight matrices. Use `np.random.randn(shape)*0.01` with the correct shape. -# - Use zero initialization for the biases. Use `np.zeros(shape)`. - -# In[16]: - -# GRADED FUNCTION: initialize_parameters - -def initialize_parameters(n_x, n_h, n_y): - """ - Argument: - n_x -- size of the input layer - n_h -- size of the hidden layer - n_y -- size of the output layer - - Returns: - parameters -- python dictionary containing your parameters: - W1 -- weight matrix of shape (n_h, n_x) - b1 -- bias vector of shape (n_h, 1) - W2 -- weight matrix of shape (n_y, n_h) - b2 -- bias vector of shape (n_y, 1) - """ - - np.random.seed(1) - - ### START CODE HERE ### (≈ 4 lines of code) - W1 = np.random.randn(n_h,n_x) * 0.01 - b1 = np.zeros((n_h,1)) - W2 = np.random.randn(n_y,n_h)*0.01 - b2 = np.zeros((n_y,1)) - ### END CODE HERE ### - - assert(W1.shape == (n_h, n_x)) - assert(b1.shape == (n_h, 1)) - assert(W2.shape == (n_y, n_h)) - assert(b2.shape == (n_y, 1)) - - parameters = {"W1": W1, - "b1": b1, - "W2": W2, - "b2": b2} - - return parameters - - -# In[17]: - -parameters = initialize_parameters(3,2,1) -print("W1 = " + str(parameters["W1"])) -print("b1 = " + str(parameters["b1"])) -print("W2 = " + str(parameters["W2"])) -print("b2 = " + str(parameters["b2"])) - - -# **Expected output**: -# -#
| **W1** | -#[[ 0.01624345 -0.00611756 -0.00528172] -# [-0.01072969 0.00865408 -0.02301539]] | -#
| **b1** | -#[[ 0.] -# [ 0.]] | -#
| **W2** | -#[[ 0.01744812 -0.00761207]] | -#
| **b2** | -#[[ 0.]] | -#
| -# | **Shape of W** | -#**Shape of b** | -#**Activation** | -#**Shape of Activation** | -#
| **Layer 1** | -#$(n^{[1]},12288)$ | -#$(n^{[1]},1)$ | -#$Z^{[1]} = W^{[1]} X + b^{[1]} $ | -# -#$(n^{[1]},209)$ | -#
| **Layer 2** | -#$(n^{[2]}, n^{[1]})$ | -#$(n^{[2]},1)$ | -#$Z^{[2]} = W^{[2]} A^{[1]} + b^{[2]}$ | -#$(n^{[2]}, 209)$ | -#
| $\vdots$ | -#$\vdots$ | -#$\vdots$ | -#$\vdots$ | -#$\vdots$ | -#
| **Layer L-1** | -#$(n^{[L-1]}, n^{[L-2]})$ | -#$(n^{[L-1]}, 1)$ | -#$Z^{[L-1]} = W^{[L-1]} A^{[L-2]} + b^{[L-1]}$ | -#$(n^{[L-1]}, 209)$ | -#
| **Layer L** | -#$(n^{[L]}, n^{[L-1]})$ | -#$(n^{[L]}, 1)$ | -#$Z^{[L]} = W^{[L]} A^{[L-1]} + b^{[L]}$ | -#$(n^{[L]}, 209)$ | -#
| **W1** | -#[[ 0.01788628 0.0043651 0.00096497 -0.01863493 -0.00277388] -# [-0.00354759 -0.00082741 -0.00627001 -0.00043818 -0.00477218] -# [-0.01313865 0.00884622 0.00881318 0.01709573 0.00050034] -# [-0.00404677 -0.0054536 -0.01546477 0.00982367 -0.01101068]] | -#
| **b1** | -#[[ 0.] -# [ 0.] -# [ 0.] -# [ 0.]] | -#
| **W2** | -#[[-0.01185047 -0.0020565 0.01486148 0.00236716] -# [-0.01023785 -0.00712993 0.00625245 -0.00160513] -# [-0.00768836 -0.00230031 0.00745056 0.01976111]] | -#
| **b2** | -#[[ 0.] -# [ 0.] -# [ 0.]] | -#
| **Z** | -#[[ 3.26295337 -1.23429987]] | -#
| **With sigmoid: A ** | -#[[ 0.96890023 0.11013289]] | -#
| **With ReLU: A ** | -#[[ 3.43896131 0. ]] | -#
-# -# -# **Exercise**: Implement the forward propagation of the above model. -# -# **Instruction**: In the code below, the variable `AL` will denote $A^{[L]} = \sigma(Z^{[L]}) = \sigma(W^{[L]} A^{[L-1]} + b^{[L]})$. (This is sometimes also called `Yhat`, i.e., this is $\hat{Y}$.) -# -# **Tips**: -# - Use the functions you had previously written -# - Use a for loop to replicate [LINEAR->RELU] (L-1) times -# - Don't forget to keep track of the caches in the "caches" list. To add a new value `c` to a `list`, you can use `list.append(c)`. - -# In[26]: - -# GRADED FUNCTION: L_model_forward - -def L_model_forward(X, parameters): - """ - Implement forward propagation for the [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID computation - - Arguments: - X -- data, numpy array of shape (input size, number of examples) - parameters -- output of initialize_parameters_deep() - - Returns: - AL -- last post-activation value - caches -- list of caches containing: - every cache of linear_relu_forward() (there are L-1 of them, indexed from 0 to L-2) - the cache of linear_sigmoid_forward() (there is one, indexed L-1) - """ - - caches = [] - A = X - L = len(parameters) // 2 # number of layers in the neural network - - # Implement [LINEAR -> RELU]*(L-1). Add "cache" to the "caches" list. - for l in range(1, L): - A_prev = A - ### START CODE HERE ### (≈ 2 lines of code) - A, cache = linear_activation_forward(A_prev, parameters['W'+str(l)],parameters['b'+str(l)] , activation = "sigmoid") - caches.append(cache) - ### END CODE HERE ### - - # Implement LINEAR -> SIGMOID. Add "cache" to the "caches" list. - ### START CODE HERE ### (≈ 2 lines of code) - AL, cache = linear_activation_forward(A, parameters['W'+ str(L)],parameters['b'+str(L)] , activation = "relu") - caches.append(cache) - ### END CODE HERE ### - - assert(AL.shape == (1,X.shape[1])) - - return AL, caches - - -# In[27]: - -X, parameters = L_model_forward_test_case_2hidden() -AL, caches = L_model_forward(X, parameters) -print("AL = " + str(AL)) -print("Length of caches list = " + str(len(caches))) - - -#
| **AL** | -#[[ 0.03921668 0.70498921 0.19734387 0.04728177]] | -#
| **Length of caches list ** | -#3 | -#
| **cost** | -#0.41493159961539694 | -#
-# *The purple blocks represent the forward propagation, and the red blocks represent the backward propagation.*
-# | **dA_prev** | -#[[ 0.51822968 -0.19517421] -# [-0.40506361 0.15255393] -# [ 2.37496825 -0.89445391]] | -#
| **dW** | -#[[-0.10076895 1.40685096 1.64992505]] | -#
| **db** | -#[[ 0.50629448]] | -#
| dA_prev | -#[[ 0.11017994 0.01105339] -# [ 0.09466817 0.00949723] -# [-0.05743092 -0.00576154]] | -# -#
| dW | -#[[ 0.10266786 0.09778551 -0.01968084]] | -#
| db | -#[[-0.05729622]] | -#
| dA_prev | -#[[ 0.44090989 0. ] -# [ 0.37883606 0. ] -# [-0.2298228 0. ]] | -# -#
| dW | -#[[ 0.44513824 0.37371418 -0.10478989]] | -#
| db | -#[[-0.20837892]] | -#
-# | dW1 | -#[[ 0.41010002 0.07807203 0.13798444 0.10502167] -# [ 0. 0. 0. 0. ] -# [ 0.05283652 0.01005865 0.01777766 0.0135308 ]] | -#
| db1 | -#[[-0.22007063] -# [ 0. ] -# [-0.02835349]] | -#
| dA1 | -#[[ 0.12913162 -0.44014127] -# [-0.14175655 0.48317296] -# [ 0.01663708 -0.05670698]] | -# -#
| W1 | -#[[-0.59562069 -0.09991781 -2.14584584 1.82662008] -# [-1.76569676 -0.80627147 0.51115557 -1.18258802] -# [-1.0535704 -0.86128581 0.68284052 2.20374577]] | -#
| b1 | -#[[-0.04659241] -# [-1.28888275] -# [ 0.53405496]] | -#
| W2 | -#[[-0.55569196 0.0354055 1.32964895]] | -#
| b2 | -#[[-0.84610769]] | -#
![](\"images/SIGNS.png\")
\n",
- "\n",
- "The next cell will show you an example of a labelled image in the dataset. Feel free to change the value of `index` below and re-run to see different examples. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "y = 4\n"
- ]
- },
- {
- "data": {
- "image/png": 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- "text/plain": [
- "| \n", - " X = Tensor(\"Placeholder:0\", shape=(?, 64, 64, 3), dtype=float32)\n", - "\n", - " | \n", - "
| \n", - " Y = Tensor(\"Placeholder_1:0\", shape=(?, 6), dtype=float32)\n", - "\n", - " | \n", - "
| \n", - " W1 = \n", - " | \n", - "\n",
- "[ 0.00131723 0.14176141 -0.04434952 0.09197326 0.14984085 -0.03514394 \n", - " -0.06847463 0.05245192]\n", - " | \n",
- "
| \n", - " W2 = \n", - " | \n", - "\n",
- "[-0.08566415 0.17750949 0.11974221 0.16773748 -0.0830943 -0.08058 \n", - " -0.00577033 -0.14643836 0.24162132 -0.05857408 -0.19055021 0.1345228 \n", - " -0.22779644 -0.1601823 -0.16117483 -0.10286498]\n", - " | \n",
- "
| \n", - " Z3 =\n", - " | \n", - "\n",
- " [[-0.44670227 -1.57208765 -1.53049231 -2.31013036 -1.29104376 0.46852064] \n", - " [-0.17601591 -1.57972014 -1.4737016 -2.61672091 -1.00810647 0.5747785 ]]\n", - " | \n",
- "
| \n", - " cost =\n", - " | \n", - " \n", - "\n", - " 2.91034\n", - " | \n", - "
| \n", - " **Cost after epoch 0 =**\n", - " | \n", - "\n", - "\n", - " 1.917929\n", - " | \n", - "
| \n", - " **Cost after epoch 5 =**\n", - " | \n", - "\n", - "\n", - " 1.506757\n", - " | \n", - "
| \n", - " **Train Accuracy =**\n", - " | \n", - "\n", - "\n", - " 0.940741\n", - " | \n", - "
| \n", - " **Test Accuracy =**\n", - " | \n", - "\n", - "\n", - " 0.783333\n", - " | \n", - "
-#
-# The next cell will show you an example of a labelled image in the dataset. Feel free to change the value of `index` below and re-run to see different examples.
-
-# In[3]:
-
-# Example of a picture
-index = 5
-plt.imshow(X_train_orig[index])
-print ("y = " + str(np.squeeze(Y_train_orig[:, index])))
-
-
-# In Course 2, you had built a fully-connected network for this dataset. But since this is an image dataset, it is more natural to apply a ConvNet to it.
-#
-# To get started, let's examine the shapes of your data.
-
-# In[4]:
-
-X_train = X_train_orig/255.
-X_test = X_test_orig/255.
-Y_train = convert_to_one_hot(Y_train_orig, 6).T
-Y_test = convert_to_one_hot(Y_test_orig, 6).T
-print ("number of training examples = " + str(X_train.shape[0]))
-print ("number of test examples = " + str(X_test.shape[0]))
-print ("X_train shape: " + str(X_train.shape))
-print ("Y_train shape: " + str(Y_train.shape))
-print ("X_test shape: " + str(X_test.shape))
-print ("Y_test shape: " + str(Y_test.shape))
-conv_layers = {}
-
-
-# ### 1.1 - Create placeholders
-#
-# TensorFlow requires that you create placeholders for the input data that will be fed into the model when running the session.
-#
-# **Exercise**: Implement the function below to create placeholders for the input image X and the output Y. You should not define the number of training examples for the moment. To do so, you could use "None" as the batch size, it will give you the flexibility to choose it later. Hence X should be of dimension **[None, n_H0, n_W0, n_C0]** and Y should be of dimension **[None, n_y]**. [Hint](https://www.tensorflow.org/api_docs/python/tf/placeholder).
-
-# In[7]:
-
-# GRADED FUNCTION: create_placeholders
-
-def create_placeholders(n_H0, n_W0, n_C0, n_y):
- """
- Creates the placeholders for the tensorflow session.
-
- Arguments:
- n_H0 -- scalar, height of an input image
- n_W0 -- scalar, width of an input image
- n_C0 -- scalar, number of channels of the input
- n_y -- scalar, number of classes
-
- Returns:
- X -- placeholder for the data input, of shape [None, n_H0, n_W0, n_C0] and dtype "float"
- Y -- placeholder for the input labels, of shape [None, n_y] and dtype "float"
- """
-
- ### START CODE HERE ### (≈2 lines)
- X = tf.placeholder(tf.float32, [None,n_H0,n_W0, n_C0])
- Y = tf.placeholder(tf.float32, [None,n_y])
- ### END CODE HERE ###
-
- return X, Y
-
-
-# In[8]:
-
-X, Y = create_placeholders(64, 64, 3, 6)
-print ("X = " + str(X))
-print ("Y = " + str(Y))
-
-
-# **Expected Output**
-#
-# | -# X = Tensor("Placeholder:0", shape=(?, 64, 64, 3), dtype=float32) -# -# | -#
| -# Y = Tensor("Placeholder_1:0", shape=(?, 6), dtype=float32) -# -# | -#
| -# W1 = -# | -#
-# [ 0.00131723 0.14176141 -0.04434952 0.09197326 0.14984085 -0.03514394 -# -0.06847463 0.05245192] -# |
-#
| -# W2 = -# | -#
-# [-0.08566415 0.17750949 0.11974221 0.16773748 -0.0830943 -0.08058 -# -0.00577033 -0.14643836 0.24162132 -0.05857408 -0.19055021 0.1345228 -# -0.22779644 -0.1601823 -0.16117483 -0.10286498] -# |
-#
| -# Z3 = -# | -#
-# [[-0.44670227 -1.57208765 -1.53049231 -2.31013036 -1.29104376 0.46852064] -# [-0.17601591 -1.57972014 -1.4737016 -2.61672091 -1.00810647 0.5747785 ]] -# |
-#
| -# cost = -# | -# -#-# 2.91034 -# | -#
| -# **Cost after epoch 0 =** -# | -# -#-# 1.917929 -# | -#
| -# **Cost after epoch 5 =** -# | -# -#-# 1.506757 -# | -#
| -# **Train Accuracy =** -# | -# -#-# 0.940741 -# | -#
| -# **Test Accuracy =** -# | -# -#-# 0.783333 -# | -#
-#
-# **Note** that for every forward function, there is its corresponding backward equivalent. Hence, at every step of your forward module you will store some parameters in a cache. These parameters are used to compute gradients during backpropagation.
-
-# ## 3 - Convolutional Neural Networks
-#
-# Although programming frameworks make convolutions easy to use, they remain one of the hardest concepts to understand in Deep Learning. A convolution layer transforms an input volume into an output volume of different size, as shown below.
-#
-# 
-#
-# In this part, you will build every step of the convolution layer. You will first implement two helper functions: one for zero padding and the other for computing the convolution function itself.
-
-# ### 3.1 - Zero-Padding
-#
-# Zero-padding adds zeros around the border of an image:
-#
-# 
-# Image (3 channels, RGB) with a padding of 2.
| -# **x.shape**: -# | -#-# (4, 3, 3, 2) -# | -#
| -# **x_pad.shape**: -# | -#-# (4, 7, 7, 2) -# | -#
| -# **x[1,1]**: -# | -#-# [[ 0.90085595 -0.68372786] -# [-0.12289023 -0.93576943] -# [-0.26788808 0.53035547]] -# | -#
| -# **x_pad[1,1]**: -# | -#-# [[ 0. 0.] -# [ 0. 0.] -# [ 0. 0.] -# [ 0. 0.] -# [ 0. 0.] -# [ 0. 0.] -# [ 0. 0.]] -# | -#
-# with a filter of 2x2 and a stride of 1 (stride = amount you move the window each time you slide)
| -# **Z** -# | -#-# -6.99908945068 -# | -#
-# This figure shows only a single channel.
| -# **Z's mean** -# | -#-# 0.0489952035289 -# | -#
| -# **Z[3,2,1]** -# | -#-# [-0.61490741 -6.7439236 -2.55153897 1.75698377 3.56208902 0.53036437 -# 5.18531798 8.75898442] -# | -#
| -# **cache_conv[0][1][2][3]** -# | -#-# [-0.20075807 0.18656139 0.41005165] -# | -#
-# 
-# | -# -# |
-# 
-# | -# |
| -# A = -# | -#-# [[[[ 1.74481176 0.86540763 1.13376944]]] -# -# -# [[[ 1.13162939 1.51981682 2.18557541]]]] -# -# | -#
| -# A = -# | -#-# [[[[ 0.02105773 -0.20328806 -0.40389855]]] -# -# -# [[[-0.22154621 0.51716526 0.48155844]]]] -# -# | -#
| -# **dA_mean** -# | -#-# 1.45243777754 -# | -#
| -# **dW_mean** -# | -#-# 1.72699145831 -# | -#
| -# **db_mean** -# | -#-# 7.83923256462 -# | -#
| -# -# **x =** -# | -# -#
-#
-# [[ 1.62434536 -0.61175641 -0.52817175] -# [-1.07296862 0.86540763 -2.3015387 ]] -# -# |
-#
| -# **mask =** -# | -#
-# [[ True False False] -# [False False False]] -# |
-#
| -# distributed_value = -# | -#
-# [[ 0.5 0.5]
-# |
-#
| -# -# **mean of dA =** -# | -# -#-# -# 0.145713902729 -# -# | -#
| -# **dA_prev[1,1] =** -# | -#
-# [[ 0. 0. ] -# [ 5.05844394 -1.68282702] -# [ 0. 0. ]] -# |
-#
| -# -# **mean of dA =** -# | -# -#-# -# 0.145713902729 -# -# | -#
| -# **dA_prev[1,1] =** -# | -#
-# [[ 0.08485462 0.2787552 ] -# [ 1.26461098 -0.25749373] -# [ 1.17975636 -0.53624893]] -# |
-#
-#
-# 
-# The model can be summarized as: ***INPUT -> LINEAR -> RELU -> LINEAR -> SIGMOID -> OUTPUT***.
-# The model can be summarized as: ***[LINEAR -> RELU] $\times$ (L-1) -> LINEAR -> SIGMOID***
| **Cost after iteration 0** | -#0.6930497356599888 | -#
| **Cost after iteration 100** | -#0.6464320953428849 | -#
| **...** | -#... | -#
| **Cost after iteration 2400** | -#0.048554785628770206 | -#
| **Accuracy** | -#1.0 | -#
| **Accuracy** | -#0.72 | -#
| **Cost after iteration 0** | -#0.771749 | -#
| **Cost after iteration 100** | -#0.672053 | -#
| **...** | -#... | -#
| **Cost after iteration 2400** | -#0.092878 | -#
| -# **Train Accuracy** -# | -#-# 0.985645933014 -# | -#
| **Test Accuracy** | -#0.8 | -#
![](\"images/pixel_comparison.png\"){kind=tracking}
\n",
- "![](\"images/distance_kiank.png\")
\n",
- "By computing a distance between two encodings and thresholding, you can determine if the two pictures represent the same person
![](\"images/triplet_comparison.png\")
\n",
- "\n", - "
In the next part, we will call the pictures from left to right: Anchor (A), Positive (P), Negative (N)
![](\"images/f_x.png\")
\n",
- "\n",
- "\n",
- "\n",
- "Training will use triplets of images $(A, P, N)$: \n",
- "\n",
- "- A is an \"Anchor\" image--a picture of a person. \n",
- "- P is a \"Positive\" image--a picture of the same person as the Anchor image.\n",
- "- N is a \"Negative\" image--a picture of a different person than the Anchor image.\n",
- "\n",
- "These triplets are picked from our training dataset. We will write $(A^{(i)}, P^{(i)}, N^{(i)})$ to denote the $i$-th training example. \n",
- "\n",
- "You'd like to make sure that an image $A^{(i)}$ of an individual is closer to the Positive $P^{(i)}$ than to the Negative image $N^{(i)}$) by at least a margin $\\alpha$:\n",
- "\n",
- "$$\\mid \\mid f(A^{(i)}) - f(P^{(i)}) \\mid \\mid_2^2 + \\alpha < \\mid \\mid f(A^{(i)}) - f(N^{(i)}) \\mid \\mid_2^2$$\n",
- "\n",
- "You would thus like to minimize the following \"triplet cost\":\n",
- "\n",
- "$$\\mathcal{J} = \\sum^{m}_{i=1} \\large[ \\small \\underbrace{\\mid \\mid f(A^{(i)}) - f(P^{(i)}) \\mid \\mid_2^2}_\\text{(1)} - \\underbrace{\\mid \\mid f(A^{(i)}) - f(N^{(i)}) \\mid \\mid_2^2}_\\text{(2)} + \\alpha \\large ] \\small_+ \\tag{3}$$\n",
- "\n",
- "Here, we are using the notation \"$[z]_+$\" to denote $max(z,0)$. \n",
- "\n",
- "Notes:\n",
- "- The term (1) is the squared distance between the anchor \"A\" and the positive \"P\" for a given triplet; you want this to be small. \n",
- "- The term (2) is the squared distance between the anchor \"A\" and the negative \"N\" for a given triplet, you want this to be relatively large, so it thus makes sense to have a minus sign preceding it. \n",
- "- $\\alpha$ is called the margin. It is a hyperparameter that you should pick manually. We will use $\\alpha = 0.2$. \n",
- "\n",
- "Most implementations also normalize the encoding vectors to have norm equal one (i.e., $\\mid \\mid f(img)\\mid \\mid_2$=1); you won't have to worry about that here.\n",
- "\n",
- "**Exercise**: Implement the triplet loss as defined by formula (3). Here are the 4 steps:\n",
- "1. Compute the distance between the encodings of \"anchor\" and \"positive\": $\\mid \\mid f(A^{(i)}) - f(P^{(i)}) \\mid \\mid_2^2$\n",
- "2. Compute the distance between the encodings of \"anchor\" and \"negative\": $\\mid \\mid f(A^{(i)}) - f(N^{(i)}) \\mid \\mid_2^2$\n",
- "3. Compute the formula per training example: $ \\mid \\mid f(A^{(i)}) - f(P^{(i)}) \\mid - \\mid \\mid f(A^{(i)}) - f(N^{(i)}) \\mid \\mid_2^2 + \\alpha$\n",
- "3. Compute the full formula by taking the max with zero and summing over the training examples:\n",
- "$$\\mathcal{J} = \\sum^{m}_{i=1} \\large[ \\small \\mid \\mid f(A^{(i)}) - f(P^{(i)}) \\mid \\mid_2^2 - \\mid \\mid f(A^{(i)}) - f(N^{(i)}) \\mid \\mid_2^2+ \\alpha \\large ] \\small_+ \\tag{3}$$\n",
- "\n",
- "Useful functions: `tf.reduce_sum()`, `tf.square()`, `tf.subtract()`, `tf.add()`, `tf.maximum()`.\n",
- "For steps 1 and 2, you will need to sum over the entries of $\\mid \\mid f(A^{(i)}) - f(P^{(i)}) \\mid \\mid_2^2$ and $\\mid \\mid f(A^{(i)}) - f(N^{(i)}) \\mid \\mid_2^2$ while for step 4 you will need to sum over the training examples."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "# GRADED FUNCTION: triplet_loss\n",
- "\n",
- "def triplet_loss(y_true, y_pred, alpha = 0.2):\n",
- " \"\"\"\n",
- " Implementation of the triplet loss as defined by formula (3)\n",
- " \n",
- " Arguments:\n",
- " y_true -- true labels, required when you define a loss in Keras, you don't need it in this function.\n",
- " y_pred -- python list containing three objects:\n",
- " anchor -- the encodings for the anchor images, of shape (None, 128)\n",
- " positive -- the encodings for the positive images, of shape (None, 128)\n",
- " negative -- the encodings for the negative images, of shape (None, 128)\n",
- " \n",
- " Returns:\n",
- " loss -- real number, value of the loss\n",
- " \"\"\"\n",
- " \n",
- " anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]\n",
- " \n",
- " ### START CODE HERE ### (≈ 4 lines)\n",
- " # Step 1: Compute the (encoding) distance between the anchor and the positive, you will need to sum over axis=-1\n",
- " pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor,positive)),axis=None)\n",
- " # Step 2: Compute the (encoding) distance between the anchor and the negative, you will need to sum over axis=-1\n",
- " neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor,negative)),axis=None)\n",
- " # Step 3: subtract the two previous distances and add alpha.\n",
- " basic_loss = tf.add(tf.subtract(pos_dist, neg_dist),alpha)\n",
- " # Step 4: Take the maximum of basic_loss and 0.0. Sum over the training examples.\n",
- " loss = tf.reduce_sum(tf.maximum(basic_loss,0))\n",
- " ### END CODE HERE ###\n",
- " \n",
- " return loss"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "loss = 350.026\n"
- ]
- }
- ],
- "source": [
- "with tf.Session() as test:\n",
- " tf.set_random_seed(1)\n",
- " y_true = (None, None, None)\n",
- " y_pred = (tf.random_normal([3, 128], mean=6, stddev=0.1, seed = 1),\n",
- " tf.random_normal([3, 128], mean=1, stddev=1, seed = 1),\n",
- " tf.random_normal([3, 128], mean=3, stddev=4, seed = 1))\n",
- " loss = triplet_loss(y_true, y_pred)\n",
- " \n",
- " print(\"loss = \" + str(loss.eval()))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Expected Output**:\n",
- "\n",
- "| \n", - " **loss**\n", - " | \n", - "\n", - " 528.143\n", - " | \n", - "
![](\"images/distance_matrix.png\")
\n",
- "\n", - "
Example of distance outputs between three individuals' encodings
![](\"images/camera_0.jpg\")
"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "It's younes, welcome home!\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "(0.65939283, True)"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "verify(\"images/camera_0.jpg\", \"younes\", database, FRmodel)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": true
- },
- "source": [
- "**Expected Output**:\n",
- "\n",
- "| \n", - " **It's younes, welcome home!**\n", - " | \n", - "\n", - " (0.65939283, True)\n", - " | \n", - "
![](\"images/camera_2.jpg\")
"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "It's not kian, please go away\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "(0.86224014, False)"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "verify(\"images/camera_2.jpg\", \"kian\", database, FRmodel)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Expected Output**:\n",
- "\n",
- "| \n", - " **It's not kian, please go away**\n", - " | \n", - "\n", - " (0.86224014, False)\n", - " | \n", - "
| \n", - " **it's younes, the distance is 0.659393**\n", - " | \n", - "\n", - " (0.65939283, 'younes')\n", - " | \n", - "
-# 
-# By computing a distance between two encodings and thresholding, you can determine if the two pictures represent the same person
-# -#
In the next part, we will call the pictures from left to right: Anchor (A), Positive (P), Negative (N)
-#
-#
-#
-# Training will use triplets of images $(A, P, N)$:
-#
-# - A is an "Anchor" image--a picture of a person.
-# - P is a "Positive" image--a picture of the same person as the Anchor image.
-# - N is a "Negative" image--a picture of a different person than the Anchor image.
-#
-# These triplets are picked from our training dataset. We will write $(A^{(i)}, P^{(i)}, N^{(i)})$ to denote the $i$-th training example.
-#
-# You'd like to make sure that an image $A^{(i)}$ of an individual is closer to the Positive $P^{(i)}$ than to the Negative image $N^{(i)}$) by at least a margin $\alpha$:
-#
-# $$\mid \mid f(A^{(i)}) - f(P^{(i)}) \mid \mid_2^2 + \alpha < \mid \mid f(A^{(i)}) - f(N^{(i)}) \mid \mid_2^2$$
-#
-# You would thus like to minimize the following "triplet cost":
-#
-# $$\mathcal{J} = \sum^{m}_{i=1} \large[ \small \underbrace{\mid \mid f(A^{(i)}) - f(P^{(i)}) \mid \mid_2^2}_\text{(1)} - \underbrace{\mid \mid f(A^{(i)}) - f(N^{(i)}) \mid \mid_2^2}_\text{(2)} + \alpha \large ] \small_+ \tag{3}$$
-#
-# Here, we are using the notation "$[z]_+$" to denote $max(z,0)$.
-#
-# Notes:
-# - The term (1) is the squared distance between the anchor "A" and the positive "P" for a given triplet; you want this to be small.
-# - The term (2) is the squared distance between the anchor "A" and the negative "N" for a given triplet, you want this to be relatively large, so it thus makes sense to have a minus sign preceding it.
-# - $\alpha$ is called the margin. It is a hyperparameter that you should pick manually. We will use $\alpha = 0.2$.
-#
-# Most implementations also normalize the encoding vectors to have norm equal one (i.e., $\mid \mid f(img)\mid \mid_2$=1); you won't have to worry about that here.
-#
-# **Exercise**: Implement the triplet loss as defined by formula (3). Here are the 4 steps:
-# 1. Compute the distance between the encodings of "anchor" and "positive": $\mid \mid f(A^{(i)}) - f(P^{(i)}) \mid \mid_2^2$
-# 2. Compute the distance between the encodings of "anchor" and "negative": $\mid \mid f(A^{(i)}) - f(N^{(i)}) \mid \mid_2^2$
-# 3. Compute the formula per training example: $ \mid \mid f(A^{(i)}) - f(P^{(i)}) \mid - \mid \mid f(A^{(i)}) - f(N^{(i)}) \mid \mid_2^2 + \alpha$
-# 3. Compute the full formula by taking the max with zero and summing over the training examples:
-# $$\mathcal{J} = \sum^{m}_{i=1} \large[ \small \mid \mid f(A^{(i)}) - f(P^{(i)}) \mid \mid_2^2 - \mid \mid f(A^{(i)}) - f(N^{(i)}) \mid \mid_2^2+ \alpha \large ] \small_+ \tag{3}$$
-#
-# Useful functions: `tf.reduce_sum()`, `tf.square()`, `tf.subtract()`, `tf.add()`, `tf.maximum()`.
-# For steps 1 and 2, you will need to sum over the entries of $\mid \mid f(A^{(i)}) - f(P^{(i)}) \mid \mid_2^2$ and $\mid \mid f(A^{(i)}) - f(N^{(i)}) \mid \mid_2^2$ while for step 4 you will need to sum over the training examples.
-
-# In[21]:
-
-# GRADED FUNCTION: triplet_loss
-
-def triplet_loss(y_true, y_pred, alpha = 0.2):
- """
- Implementation of the triplet loss as defined by formula (3)
-
- Arguments:
- y_true -- true labels, required when you define a loss in Keras, you don't need it in this function.
- y_pred -- python list containing three objects:
- anchor -- the encodings for the anchor images, of shape (None, 128)
- positive -- the encodings for the positive images, of shape (None, 128)
- negative -- the encodings for the negative images, of shape (None, 128)
-
- Returns:
- loss -- real number, value of the loss
- """
-
- anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]
-
- ### START CODE HERE ### (≈ 4 lines)
- # Step 1: Compute the (encoding) distance between the anchor and the positive, you will need to sum over axis=-1
- pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor,positive)),axis=None)
- # Step 2: Compute the (encoding) distance between the anchor and the negative, you will need to sum over axis=-1
- neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor,negative)),axis=None)
- # Step 3: subtract the two previous distances and add alpha.
- basic_loss = tf.add(tf.subtract(pos_dist, neg_dist),alpha)
- # Step 4: Take the maximum of basic_loss and 0.0. Sum over the training examples.
- loss = tf.reduce_sum(tf.maximum(basic_loss,0))
- ### END CODE HERE ###
-
- return loss
-
-
-# In[22]:
-
-with tf.Session() as test:
- tf.set_random_seed(1)
- y_true = (None, None, None)
- y_pred = (tf.random_normal([3, 128], mean=6, stddev=0.1, seed = 1),
- tf.random_normal([3, 128], mean=1, stddev=1, seed = 1),
- tf.random_normal([3, 128], mean=3, stddev=4, seed = 1))
- loss = triplet_loss(y_true, y_pred)
-
- print("loss = " + str(loss.eval()))
-
-
-# **Expected Output**:
-#
-# | -# **loss** -# | -#-# 528.143 -# | -#
-# -#
Example of distance outputs between three individuals' encodings
-
-# In[13]:
-
-verify("images/camera_0.jpg", "younes", database, FRmodel)
-
-
-# **Expected Output**:
-#
-# | -# **It's younes, welcome home!** -# | -#-# (0.65939283, True) -# | -#
-
-# In[14]:
-
-verify("images/camera_2.jpg", "kian", database, FRmodel)
-
-
-# **Expected Output**:
-#
-# | -# **It's not kian, please go away** -# | -#-# (0.86224014, False) -# | -#
| -# **it's younes, the distance is 0.659393** -# | -#-# (0.65939283, 'younes') -# | -#
-# | ** J ** | -#8 | -#
| ** dtheta ** | -#2 | -#
| ** difference ** | -#2.9193358103083e-10 | -#
-# *LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID*
-# You will need these functions in gradient_check_n()
| ** There is a mistake in the backward propagation!** | -#difference = 0.285093156781 | -#
| -# **W1** -# | -#-# [[ 0. 0. 0.] -# [ 0. 0. 0.]] -# | -#
| -# **b1** -# | -#-# [[ 0.] -# [ 0.]] -# | -#
| -# **W2** -# | -#-# [[ 0. 0.]] -# | -#
| -# **b2** -# | -#-# [[ 0.]] -# | -#
| -# **W1** -# | -#-# [[ 17.88628473 4.36509851 0.96497468] -# [-18.63492703 -2.77388203 -3.54758979]] -# | -#
| -# **b1** -# | -#-# [[ 0.] -# [ 0.]] -# | -#
| -# **W2** -# | -#-# [[-0.82741481 -6.27000677]] -# | -#
| -# **b2** -# | -#-# [[ 0.]] -# | -#
| -# **W1** -# | -#-# [[ 1.78862847 0.43650985] -# [ 0.09649747 -1.8634927 ] -# [-0.2773882 -0.35475898] -# [-0.08274148 -0.62700068]] -# | -#
| -# **b1** -# | -#-# [[ 0.] -# [ 0.] -# [ 0.] -# [ 0.]] -# | -#
| -# **W2** -# | -#-# [[-0.03098412 -0.33744411 -0.92904268 0.62552248]] -# | -#
| -# **b2** -# | -#-# [[ 0.]] -# | -#
| -# **Model** -# | -#-# **Train accuracy** -# | -#-# **Problem/Comment** -# | -# -#-# 3-layer NN with zeros initialization -# | -#-# 50% -# | -#-# fails to break symmetry -# | -#
| -# 3-layer NN with large random initialization -# | -#-# 83% -# | -#-# too large weights -# | -#
| -# 3-layer NN with He initialization -# | -#-# 99% -# | -#-# recommended method -# | -#
| **m_train** | -#209 | -#
| **m_test** | -#50 | -#
| **num_px** | -#64 | -#
| **train_set_x_flatten shape** | -#(12288, 209) | -#
| **train_set_y shape** | -#(1, 209) | -#
| **test_set_x_flatten shape** | -#(12288, 50) | -#
| **test_set_y shape** | -#(1, 50) | -#
| **sanity check after reshaping** | -#[17 31 56 22 33] | -#
-#
-# **Mathematical expression of the algorithm**:
-#
-# For one example $x^{(i)}$:
-# $$z^{(i)} = w^T x^{(i)} + b \tag{1}$$
-# $$\hat{y}^{(i)} = a^{(i)} = sigmoid(z^{(i)})\tag{2}$$
-# $$ \mathcal{L}(a^{(i)}, y^{(i)}) = - y^{(i)} \log(a^{(i)}) - (1-y^{(i)} ) \log(1-a^{(i)})\tag{3}$$
-#
-# The cost is then computed by summing over all training examples:
-# $$ J = \frac{1}{m} \sum_{i=1}^m \mathcal{L}(a^{(i)}, y^{(i)})\tag{6}$$
-#
-# **Key steps**:
-# In this exercise, you will carry out the following steps:
-# - Initialize the parameters of the model
-# - Learn the parameters for the model by minimizing the cost
-# - Use the learned parameters to make predictions (on the test set)
-# - Analyse the results and conclude
-
-# ## 4 - Building the parts of our algorithm ##
-#
-# The main steps for building a Neural Network are:
-# 1. Define the model structure (such as number of input features)
-# 2. Initialize the model's parameters
-# 3. Loop:
-# - Calculate current loss (forward propagation)
-# - Calculate current gradient (backward propagation)
-# - Update parameters (gradient descent)
-#
-# You often build 1-3 separately and integrate them into one function we call `model()`.
-#
-# ### 4.1 - Helper functions
-#
-# **Exercise**: Using your code from "Python Basics", implement `sigmoid()`. As you've seen in the figure above, you need to compute $sigmoid( w^T x + b) = \frac{1}{1 + e^{-(w^T x + b)}}$ to make predictions. Use np.exp().
-
-# In[8]:
-
-# GRADED FUNCTION: sigmoid
-
-def sigmoid(z):
- """
- Compute the sigmoid of z
-
- Arguments:
- z -- A scalar or numpy array of any size.
-
- Return:
- s -- sigmoid(z)
- """
-
- ### START CODE HERE ### (≈ 1 line of code)
- s = 1 / (1 + np.exp(-z))
- ### END CODE HERE ###
-
- return s
-
-
-# In[9]:
-
-print ("sigmoid([0, 2]) = " + str(sigmoid(np.array([0,2]))))
-
-
-# **Expected Output**:
-#
-# | **sigmoid([0, 2])** | -#[ 0.5 0.88079708] | -#
| ** w ** | -#[[ 0.] -# [ 0.]] | -#
| ** b ** | -#0 | -#
| ** dw ** | -#[[ 0.99845601] -# [ 2.39507239]] | -#
| ** db ** | -#0.00145557813678 | -#
| ** cost ** | -#5.801545319394553 | -#
| **w** | -#[[ 0.19033591] -# [ 0.12259159]] | -#
| **b** | -#1.92535983008 | -#
| **dw** | -#[[ 0.67752042] -# [ 1.41625495]] | -#
| **db** | -#0.219194504541 | -#
| -# **predictions** -# | -#-# [[ 1. 1. 0.]] -# | -#
| **Cost after iteration 0 ** | -#0.693147 | -#
| |
-# |
-#
| **Train Accuracy** | -#99.04306220095694 % | -#
| **Test Accuracy** | -#70.0 % | -#
-# At each step of the training, you update your parameters following a certain direction to try to get to the lowest possible point.
| **W1** | -#[[ 1.63535156 -0.62320365 -0.53718766] -# [-1.07799357 0.85639907 -2.29470142]] | -#
| **b1** | -#[[ 1.74604067] -# [-0.75184921]] | -#
| **W2** | -#[[ 0.32171798 -0.25467393 1.46902454] -# [-2.05617317 -0.31554548 -0.3756023 ] -# [ 1.1404819 -1.09976462 -0.1612551 ]] | -#
| **b2** | -#[[-0.88020257] -# [ 0.02561572] -# [ 0.57539477]] | -#
-# "+" denotes a minimum of the cost. SGD leads to many oscillations to reach convergence. But each step is a lot faster to compute for SGD than for GD, as it uses only one training example (vs. the whole batch for GD).
-# "+" denotes a minimum of the cost. Using mini-batches in your optimization algorithm often leads to faster optimization.
-#
-# - **Partition**: Partition the shuffled (X, Y) into mini-batches of size `mini_batch_size` (here 64). Note that the number of training examples is not always divisible by `mini_batch_size`. The last mini batch might be smaller, but you don't need to worry about this. When the final mini-batch is smaller than the full `mini_batch_size`, it will look like this:
-#
-# 
-#
-# **Exercise**: Implement `random_mini_batches`. We coded the shuffling part for you. To help you with the partitioning step, we give you the following code that selects the indexes for the $1^{st}$ and $2^{nd}$ mini-batches:
-# ```python
-# first_mini_batch_X = shuffled_X[:, 0 : mini_batch_size]
-# second_mini_batch_X = shuffled_X[:, mini_batch_size : 2 * mini_batch_size]
-# ...
-# ```
-#
-# Note that the last mini-batch might end up smaller than `mini_batch_size=64`. Let $\lfloor s \rfloor$ represents $s$ rounded down to the nearest integer (this is `math.floor(s)` in Python). If the total number of examples is not a multiple of `mini_batch_size=64` then there will be $\lfloor \frac{m}{mini\_batch\_size}\rfloor$ mini-batches with a full 64 examples, and the number of examples in the final mini-batch will be ($m-mini_\_batch_\_size \times \lfloor \frac{m}{mini\_batch\_size}\rfloor$).
-
-# In[10]:
-
-# GRADED FUNCTION: random_mini_batches
-
-def random_mini_batches(X, Y, mini_batch_size = 64, seed = 0):
- """
- Creates a list of random minibatches from (X, Y)
-
- Arguments:
- X -- input data, of shape (input size, number of examples)
- Y -- true "label" vector (1 for blue dot / 0 for red dot), of shape (1, number of examples)
- mini_batch_size -- size of the mini-batches, integer
-
- Returns:
- mini_batches -- list of synchronous (mini_batch_X, mini_batch_Y)
- """
-
- np.random.seed(seed) # To make your "random" minibatches the same as ours
- m = X.shape[1] # number of training examples
- mini_batches = []
-
- # Step 1: Shuffle (X, Y)
- permutation = list(np.random.permutation(m))
- shuffled_X = X[:, permutation]
- shuffled_Y = Y[:, permutation].reshape((1,m))
-
- # Step 2: Partition (shuffled_X, shuffled_Y). Minus the end case.
- num_complete_minibatches = math.floor(m/mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning
- for k in range(0, num_complete_minibatches):
- ### START CODE HERE ### (approx. 2 lines)
- mini_batch_X = shuffled_X[:, k*mini_batch_size : (k+1)* mini_batch_size]
- mini_batch_Y = shuffled_Y[:, k*mini_batch_size : (k+1) * mini_batch_size]
- ### END CODE HERE ###
- mini_batch = (mini_batch_X, mini_batch_Y)
- mini_batches.append(mini_batch)
-
- # Handling the end case (last mini-batch < mini_batch_size)
- if m % mini_batch_size != 0:
- ### START CODE HERE ### (approx. 2 lines)
- mini_batch_X = shuffled_X[:, math.floor(m / mini_batch_size) * mini_batch_size: m]
- mini_batch_Y = shuffled_Y[:, math.floor(m / mini_batch_size) * mini_batch_size: m]
- ### END CODE HERE ###
- mini_batch = (mini_batch_X, mini_batch_Y)
- mini_batches.append(mini_batch)
-
- return mini_batches
-
-
-# In[11]:
-
-X_assess, Y_assess, mini_batch_size = random_mini_batches_test_case()
-mini_batches = random_mini_batches(X_assess, Y_assess, mini_batch_size)
-
-print ("shape of the 1st mini_batch_X: " + str(mini_batches[0][0].shape))
-print ("shape of the 2nd mini_batch_X: " + str(mini_batches[1][0].shape))
-print ("shape of the 3rd mini_batch_X: " + str(mini_batches[2][0].shape))
-print ("shape of the 1st mini_batch_Y: " + str(mini_batches[0][1].shape))
-print ("shape of the 2nd mini_batch_Y: " + str(mini_batches[1][1].shape))
-print ("shape of the 3rd mini_batch_Y: " + str(mini_batches[2][1].shape))
-print ("mini batch sanity check: " + str(mini_batches[0][0][0][0:3]))
-
-
-# **Expected Output**:
-#
-# | **shape of the 1st mini_batch_X** | -#(12288, 64) | -#
| **shape of the 2nd mini_batch_X** | -#(12288, 64) | -#
| **shape of the 3rd mini_batch_X** | -#(12288, 20) | -#
| **shape of the 1st mini_batch_Y** | -#(1, 64) | -#
| **shape of the 2nd mini_batch_Y** | -#(1, 64) | -#
| **shape of the 3rd mini_batch_Y** | -#(1, 20) | -#
| **mini batch sanity check** | -#[ 0.90085595 -0.7612069 0.2344157 ] | -#
-# | **v["dW1"]** | -#[[ 0. 0. 0.] -# [ 0. 0. 0.]] | -#
| **v["db1"]** | -#[[ 0.] -# [ 0.]] | -#
| **v["dW2"]** | -#[[ 0. 0. 0.] -# [ 0. 0. 0.] -# [ 0. 0. 0.]] | -#
| **v["db2"]** | -#[[ 0.] -# [ 0.] -# [ 0.]] | -#
| **W1** | -#[[ 1.62544598 -0.61290114 -0.52907334] -# [-1.07347112 0.86450677 -2.30085497]] | -#
| **b1** | -#[[ 1.74493465] -# [-0.76027113]] | -#
| **W2** | -#[[ 0.31930698 -0.24990073 1.4627996 ] -# [-2.05974396 -0.32173003 -0.38320915] -# [ 1.13444069 -1.0998786 -0.1713109 ]] | -#
| **b2** | -#[[-0.87809283] -# [ 0.04055394] -# [ 0.58207317]] | -#
| **v["dW1"]** | -#[[-0.11006192 0.11447237 0.09015907] -# [ 0.05024943 0.09008559 -0.06837279]] | -#
| **v["db1"]** | -#[[-0.01228902] -# [-0.09357694]] | -#
| **v["dW2"]** | -#[[-0.02678881 0.05303555 -0.06916608] -# [-0.03967535 -0.06871727 -0.08452056] -# [-0.06712461 -0.00126646 -0.11173103]] | -#
| **v["db2"]** | -#[[ 0.02344157] -# [ 0.16598022] -# [ 0.07420442]] | -#
| **v["dW1"]** | -#[[ 0. 0. 0.] -# [ 0. 0. 0.]] | -#
| **v["db1"]** | -#[[ 0.] -# [ 0.]] | -#
| **v["dW2"]** | -#[[ 0. 0. 0.] -# [ 0. 0. 0.] -# [ 0. 0. 0.]] | -#
| **v["db2"]** | -#[[ 0.] -# [ 0.] -# [ 0.]] | -#
| **s["dW1"]** | -#[[ 0. 0. 0.] -# [ 0. 0. 0.]] | -#
| **s["db1"]** | -#[[ 0.] -# [ 0.]] | -#
| **s["dW2"]** | -#[[ 0. 0. 0.] -# [ 0. 0. 0.] -# [ 0. 0. 0.]] | -#
| **s["db2"]** | -#[[ 0.] -# [ 0.] -# [ 0.]] | -#
| **W1** | -#[[ 1.63178673 -0.61919778 -0.53561312] -# [-1.08040999 0.85796626 -2.29409733]] | -#
| **b1** | -#[[ 1.75225313] -# [-0.75376553]] | -#
| **W2** | -#[[ 0.32648046 -0.25681174 1.46954931] -# [-2.05269934 -0.31497584 -0.37661299] -# [ 1.14121081 -1.09245036 -0.16498684]] | -#
| **b2** | -#[[-0.88529978] -# [ 0.03477238] -# [ 0.57537385]] | -#
| **v["dW1"]** | -#[[-0.11006192 0.11447237 0.09015907] -# [ 0.05024943 0.09008559 -0.06837279]] | -#
| **v["db1"]** | -#[[-0.01228902] -# [-0.09357694]] | -#
| **v["dW2"]** | -#[[-0.02678881 0.05303555 -0.06916608] -# [-0.03967535 -0.06871727 -0.08452056] -# [-0.06712461 -0.00126646 -0.11173103]] | -#
| **v["db2"]** | -#[[ 0.02344157] -# [ 0.16598022] -# [ 0.07420442]] | -#
| **s["dW1"]** | -#[[ 0.00121136 0.00131039 0.00081287] -# [ 0.0002525 0.00081154 0.00046748]] | -#
| **s["db1"]** | -#[[ 1.51020075e-05] -# [ 8.75664434e-04]] | -#
| **s["dW2"]** | -#[[ 7.17640232e-05 2.81276921e-04 4.78394595e-04] -# [ 1.57413361e-04 4.72206320e-04 7.14372576e-04] -# [ 4.50571368e-04 1.60392066e-07 1.24838242e-03]] | -#
| **s["db2"]** | -#[[ 5.49507194e-05] -# [ 2.75494327e-03] -# [ 5.50629536e-04]] | -#
| -# **optimization method** -# | -#-# **accuracy** -# | -#-# **cost shape** -# | -# -#-# Gradient descent -# | -#-# 79.7% -# | -#-# oscillations -# | -#
| -# Momentum -# | -#-# 79.7% -# | -#-# oscillations -# | -#
| -# Adam -# | -#-# 94% -# | -#-# smoother -# | -#
| **shape of X** | -#(2, 400) | -#
| **shape of Y** | -#(1, 400) | -#
| **m** | -#400 | -#
| **Accuracy** | -#47% | -#
-#
-# **Mathematically**:
-#
-# For one example $x^{(i)}$:
-# $$z^{[1] (i)} = W^{[1]} x^{(i)} + b^{[1] (i)}\tag{1}$$
-# $$a^{[1] (i)} = \tanh(z^{[1] (i)})\tag{2}$$
-# $$z^{[2] (i)} = W^{[2]} a^{[1] (i)} + b^{[2] (i)}\tag{3}$$
-# $$\hat{y}^{(i)} = a^{[2] (i)} = \sigma(z^{ [2] (i)})\tag{4}$$
-# $$y^{(i)}_{prediction} = \begin{cases} 1 & \mbox{if } a^{[2](i)} > 0.5 \\ 0 & \mbox{otherwise } \end{cases}\tag{5}$$
-#
-# Given the predictions on all the examples, you can also compute the cost $J$ as follows:
-# $$J = - \frac{1}{m} \sum\limits_{i = 0}^{m} \large\left(\small y^{(i)}\log\left(a^{[2] (i)}\right) + (1-y^{(i)})\log\left(1- a^{[2] (i)}\right) \large \right) \small \tag{6}$$
-#
-# **Reminder**: The general methodology to build a Neural Network is to:
-# 1. Define the neural network structure ( # of input units, # of hidden units, etc).
-# 2. Initialize the model's parameters
-# 3. Loop:
-# - Implement forward propagation
-# - Compute loss
-# - Implement backward propagation to get the gradients
-# - Update parameters (gradient descent)
-#
-# You often build helper functions to compute steps 1-3 and then merge them into one function we call `nn_model()`. Once you've built `nn_model()` and learnt the right parameters, you can make predictions on new data.
-
-# ### 4.1 - Defining the neural network structure ####
-#
-# **Exercise**: Define three variables:
-# - n_x: the size of the input layer
-# - n_h: the size of the hidden layer (set this to 4)
-# - n_y: the size of the output layer
-#
-# **Hint**: Use shapes of X and Y to find n_x and n_y. Also, hard code the hidden layer size to be 4.
-
-# In[29]:
-
-# GRADED FUNCTION: layer_sizes
-
-def layer_sizes(X, Y):
- """
- Arguments:
- X -- input dataset of shape (input size, number of examples)
- Y -- labels of shape (output size, number of examples)
-
- Returns:
- n_x -- the size of the input layer
- n_h -- the size of the hidden layer
- n_y -- the size of the output layer
- """
- ### START CODE HERE ### (≈ 3 lines of code)
- n_x = X.shape[0] # size of input layer
- n_h = 4
- n_y = Y.shape[0] # size of output layer
- ### END CODE HERE ###
- return (n_x, n_h, n_y)
-
-
-# In[30]:
-
-X_assess, Y_assess = layer_sizes_test_case()
-(n_x, n_h, n_y) = layer_sizes(X_assess, Y_assess)
-print("The size of the input layer is: n_x = " + str(n_x))
-print("The size of the hidden layer is: n_h = " + str(n_h))
-print("The size of the output layer is: n_y = " + str(n_y))
-
-
-# **Expected Output** (these are not the sizes you will use for your network, they are just used to assess the function you've just coded).
-#
-# | **n_x** | -#5 | -#
| **n_h** | -#4 | -#
| **n_y** | -#2 | -#
| **W1** | -#[[-0.00416758 -0.00056267] -# [-0.02136196 0.01640271] -# [-0.01793436 -0.00841747] -# [ 0.00502881 -0.01245288]] | -#
| **b1** | -#[[ 0.] -# [ 0.] -# [ 0.] -# [ 0.]] | -#
| **W2** | -#[[-0.01057952 -0.00909008 0.00551454 0.02292208]] | -#
| **b2** | -#[[ 0.]] | -#
| 0.262818640198 0.091999045227 -1.30766601287 0.212877681719 | -#
| **cost** | -#0.693058761... | -#
-#
-#
-#
-# - Tips:
-# - To compute dZ1 you'll need to compute $g^{[1]'}(Z^{[1]})$. Since $g^{[1]}(.)$ is the tanh activation function, if $a = g^{[1]}(z)$ then $g^{[1]'}(z) = 1-a^2$. So you can compute
-# $g^{[1]'}(Z^{[1]})$ using `(1 - np.power(A1, 2))`.
-
-# In[44]:
-
-# GRADED FUNCTION: backward_propagation
-
-def backward_propagation(parameters, cache, X, Y):
- """
- Implement the backward propagation using the instructions above.
-
- Arguments:
- parameters -- python dictionary containing our parameters
- cache -- a dictionary containing "Z1", "A1", "Z2" and "A2".
- X -- input data of shape (2, number of examples)
- Y -- "true" labels vector of shape (1, number of examples)
-
- Returns:
- grads -- python dictionary containing your gradients with respect to different parameters
- """
- m = X.shape[1]
-
- # First, retrieve W1 and W2 from the dictionary "parameters".
- ### START CODE HERE ### (≈ 2 lines of code)
- W1 = parameters["W1"]
- W2 = parameters["W2"]
- ### END CODE HERE ###
-
- # Retrieve also A1 and A2 from dictionary "cache".
- ### START CODE HERE ### (≈ 2 lines of code)
- A1 = cache["A1"]
- A2 = cache["A2"]
- ### END CODE HERE ###
-
- # Backward propagation: calculate dW1, db1, dW2, db2.
- ### START CODE HERE ### (≈ 6 lines of code, corresponding to 6 equations on slide above)
- dZ2 = A2 - Y
- dW2 = (1/m)*np.dot(dZ2,A1.T)
- db2 = (1/m)*np.sum(dZ2, axis=1, keepdims=True)
- dZ1 = np.dot(W2.T,dZ2) * (1 - np.power(A1,2))
- dW1 = (1/m)*np.dot(dZ1,X.T)
- db1 = (1/m)*np.sum(dZ1, axis=1, keepdims=True)
- ### END CODE HERE ###
-
- grads = {"dW1": dW1,
- "db1": db1,
- "dW2": dW2,
- "db2": db2}
-
- return grads
-
-
-# In[45]:
-
-parameters, cache, X_assess, Y_assess = backward_propagation_test_case()
-
-grads = backward_propagation(parameters, cache, X_assess, Y_assess)
-print ("dW1 = "+ str(grads["dW1"]))
-print ("db1 = "+ str(grads["db1"]))
-print ("dW2 = "+ str(grads["dW2"]))
-print ("db2 = "+ str(grads["db2"]))
-
-
-# **Expected output**:
-#
-#
-#
-# | **dW1** | -#[[ 0.00301023 -0.00747267] -# [ 0.00257968 -0.00641288] -# [-0.00156892 0.003893 ] -# [-0.00652037 0.01618243]] | -#
| **db1** | -#[[ 0.00176201] -# [ 0.00150995] -# [-0.00091736] -# [-0.00381422]] | -#
| **dW2** | -#[[ 0.00078841 0.01765429 -0.00084166 -0.01022527]] | -#
| **db2** | -#[[-0.16655712]] | -#
-#
-#
-
-# In[46]:
-
-# GRADED FUNCTION: update_parameters
-
-def update_parameters(parameters, grads, learning_rate = 1.2):
- """
- Updates parameters using the gradient descent update rule given above
-
- Arguments:
- parameters -- python dictionary containing your parameters
- grads -- python dictionary containing your gradients
-
- Returns:
- parameters -- python dictionary containing your updated parameters
- """
- # Retrieve each parameter from the dictionary "parameters"
- ### START CODE HERE ### (≈ 4 lines of code)
- W1 = parameters["W1"]
- b1 = parameters["b1"]
- W2 = parameters["W2"]
- b2 = parameters["b2"]
- ### END CODE HERE ###
-
- # Retrieve each gradient from the dictionary "grads"
- ### START CODE HERE ### (≈ 4 lines of code)
- dW1 = grads["dW1"]
- db1 = grads["db1"]
- dW2 = grads["dW2"]
- db2 = grads["db2"]
- ## END CODE HERE ###
-
- # Update rule for each parameter
- ### START CODE HERE ### (≈ 4 lines of code)
- W1 = W1 - learning_rate * dW1
- b1 = b1 - learning_rate * db1
- W2 = W2 - learning_rate * dW2
- b2 = b2 - learning_rate * db2
- ### END CODE HERE ###
-
- parameters = {"W1": W1,
- "b1": b1,
- "W2": W2,
- "b2": b2}
-
- return parameters
-
-
-# In[47]:
-
-parameters, grads = update_parameters_test_case()
-parameters = update_parameters(parameters, grads)
-
-print("W1 = " + str(parameters["W1"]))
-print("b1 = " + str(parameters["b1"]))
-print("W2 = " + str(parameters["W2"]))
-print("b2 = " + str(parameters["b2"]))
-
-
-# **Expected Output**:
-#
-#
-# | **W1** | -#[[-0.00643025 0.01936718] -# [-0.02410458 0.03978052] -# [-0.01653973 -0.02096177] -# [ 0.01046864 -0.05990141]] | -#
| **b1** | -#[[ -1.02420756e-06] -# [ 1.27373948e-05] -# [ 8.32996807e-07] -# [ -3.20136836e-06]] | -#
| **W2** | -#[[-0.01041081 -0.04463285 0.01758031 0.04747113]] | -#
| **b2** | -#[[ 0.00010457]] | -#
| -# **cost after iteration 0** -# | -#-# 0.692739 -# | -#
|
-# |
-#
-# |
-#
| **W1** | -#[[-0.65848169 1.21866811] -# [-0.76204273 1.39377573] -# [ 0.5792005 -1.10397703] -# [ 0.76773391 -1.41477129]] | -#
| **b1** | -#[[ 0.287592 ] -# [ 0.3511264 ] -# [-0.2431246 ] -# [-0.35772805]] | -#
| **W2** | -#[[-2.45566237 -3.27042274 2.00784958 3.36773273]] | -#
| **b2** | -#[[ 0.20459656]] | -#
| **predictions mean** | -#0.666666666667 | -#
| **Cost after iteration 9000** | -#0.218607 | -#
| **Accuracy** | -#90% | -#
-#
-# To refer to a function belonging to a specific package you could call it using package_name.function(). Run the code below to see an example with math.exp().
-
-# In[10]:
-
-# GRADED FUNCTION: basic_sigmoid
-
-import math
-
-def basic_sigmoid(x):
- """
- Compute sigmoid of x.
-
- Arguments:
- x -- A scalar
-
- Return:
- s -- sigmoid(x)
- """
-
- ### START CODE HERE ### (≈ 1 line of code)
- s = 1/(1 + math.exp(-x))
- ### END CODE HERE ###
-
- return s
-
-
-# In[11]:
-
-basic_sigmoid(3)
-
-
-# **Expected Output**:
-# | ** basic_sigmoid(3) ** | -#0.9525741268224334 | -#
| **sigmoid([1,2,3])** | -#array([ 0.73105858, 0.88079708, 0.95257413]) | -#
| **sigmoid_derivative([1,2,3])** | -#[ 0.19661193 0.10499359 0.04517666] | -#
-#
-# **Exercise**: Implement `image2vector()` that takes an input of shape (length, height, 3) and returns a vector of shape (length\*height\*3, 1). For example, if you would like to reshape an array v of shape (a, b, c) into a vector of shape (a*b,c) you would do:
-# ``` python
-# v = v.reshape((v.shape[0]*v.shape[1], v.shape[2])) # v.shape[0] = a ; v.shape[1] = b ; v.shape[2] = c
-# ```
-# - Please don't hardcode the dimensions of image as a constant. Instead look up the quantities you need with `image.shape[0]`, etc.
-
-# In[38]:
-
-# GRADED FUNCTION: image2vector
-def image2vector(image):
- """
- Argument:
- image -- a numpy array of shape (length, height, depth)
-
- Returns:
- v -- a vector of shape (length*height*depth, 1)
- """
-
- ### START CODE HERE ### (≈ 1 line of code)
- v = image.reshape(image.shape[0]*image.shape[1]*image.shape[2],1)
- ### END CODE HERE ###
-
- return v
-
-
-# In[39]:
-
-# This is a 3 by 3 by 2 array, typically images will be (num_px_x, num_px_y,3) where 3 represents the RGB values
-image = np.array([[[ 0.67826139, 0.29380381],
- [ 0.90714982, 0.52835647],
- [ 0.4215251 , 0.45017551]],
-
- [[ 0.92814219, 0.96677647],
- [ 0.85304703, 0.52351845],
- [ 0.19981397, 0.27417313]],
-
- [[ 0.60659855, 0.00533165],
- [ 0.10820313, 0.49978937],
- [ 0.34144279, 0.94630077]]])
-
-print ("image2vector(image) = " + str(image2vector(image)))
-
-
-# **Expected Output**:
-#
-#
-# | **image2vector(image)** | -#[[ 0.67826139] -# [ 0.29380381] -# [ 0.90714982] -# [ 0.52835647] -# [ 0.4215251 ] -# [ 0.45017551] -# [ 0.92814219] -# [ 0.96677647] -# [ 0.85304703] -# [ 0.52351845] -# [ 0.19981397] -# [ 0.27417313] -# [ 0.60659855] -# [ 0.00533165] -# [ 0.10820313] -# [ 0.49978937] -# [ 0.34144279] -# [ 0.94630077]] | -#
| **normalizeRows(x)** | -#[[ 0. 0.6 0.8 ] -# [ 0.13736056 0.82416338 0.54944226]] | -#
| **softmax(x)** | -#[[ 9.80897665e-01 8.94462891e-04 1.79657674e-02 1.21052389e-04 -# 1.21052389e-04] -# [ 8.78679856e-01 1.18916387e-01 8.01252314e-04 8.01252314e-04 -# 8.01252314e-04]] | -#
| **L1** | -#1.1 | -#
| **L2** | -#0.43 | -#
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import torchvision\n",
+ "\n",
+ "def imshow(inp, title=None, ax=None, figsize=(5, 5)):\n",
+ " \"\"\"Imshow for Tensor.\"\"\"\n",
+ " inp = inp.numpy().transpose((1, 2, 0))\n",
+ " mean = np.array([0.485, 0.456, 0.406])\n",
+ " std = np.array([0.229, 0.224, 0.225])\n",
+ " inp = std * inp + mean\n",
+ " inp = np.clip(inp, 0, 1)\n",
+ " if ax is None:\n",
+ " fig, ax = plt.subplots(1, figsize=figsize)\n",
+ " ax.imshow(inp)\n",
+ " ax.set_xticks([])\n",
+ " ax.set_yticks([])\n",
+ " if title is not None:\n",
+ " ax.set_title(title)\n",
+ "\n",
+ "# Get a batch of training data\n",
+ "inputs, classes = next(iter(dataloaders['train']))\n",
+ "\n",
+ "# Make a grid from batch\n",
+ "out = torchvision.utils.make_grid(inputs, nrow=4)\n",
+ "\n",
+ "fig, ax = plt.subplots(1, figsize=(10, 10))\n",
+ "imshow(out, title=[class_names[x] for x in classes], ax=ax)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nkofpspKrVNf"
+ },
+ "source": [
+ "### Support Function for Model Training\n",
+ "\n",
+ "Below is a generic function for model training.\n",
+ "This function also\n",
+ "\n",
+ "- Schedules the learning rate\n",
+ "- Saves the best model\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "YXPlVBc4rVNg"
+ },
+ "outputs": [],
+ "source": [
+ "def train_model(model, criterion, optimizer, scheduler, num_epochs=25, device='cpu'):\n",
+ " \"\"\"\n",
+ " Support function for model training.\n",
+ "\n",
+ " Args:\n",
+ " model: Model to be trained\n",
+ " criterion: Optimization criterion (loss)\n",
+ " optimizer: Optimizer to use for training\n",
+ " scheduler: Instance of ``torch.optim.lr_scheduler``\n",
+ " num_epochs: Number of epochs\n",
+ " device: Device to run the training on. Must be 'cpu' or 'cuda'\n",
+ " \"\"\"\n",
+ " since = time.time()\n",
+ "\n",
+ " best_model_wts = copy.deepcopy(model.state_dict())\n",
+ " best_acc = 0.0\n",
+ "\n",
+ " for epoch in range(num_epochs):\n",
+ " print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
+ " print('-' * 10)\n",
+ "\n",
+ " # Each epoch has a training and validation phase\n",
+ " for phase in ['train', 'val']:\n",
+ " if phase == 'train':\n",
+ " model.train() # Set model to training mode\n",
+ " else:\n",
+ " model.eval() # Set model to evaluate mode\n",
+ "\n",
+ " running_loss = 0.0\n",
+ " running_corrects = 0\n",
+ "\n",
+ " # Iterate over data.\n",
+ " for inputs, labels in dataloaders[phase]:\n",
+ " inputs = inputs.to(device)\n",
+ " labels = labels.to(device)\n",
+ "\n",
+ " # zero the parameter gradients\n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " # forward\n",
+ " # track history if only in train\n",
+ " with torch.set_grad_enabled(phase == 'train'):\n",
+ " outputs = model(inputs)\n",
+ " _, preds = torch.max(outputs, 1)\n",
+ " loss = criterion(outputs, labels)\n",
+ "\n",
+ " # backward + optimize only if in training phase\n",
+ " if phase == 'train':\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " # statistics\n",
+ " running_loss += loss.item() * inputs.size(0)\n",
+ " running_corrects += torch.sum(preds == labels.data)\n",
+ " if phase == 'train':\n",
+ " scheduler.step()\n",
+ "\n",
+ " epoch_loss = running_loss / dataset_sizes[phase]\n",
+ " epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
+ "\n",
+ " print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
+ " phase, epoch_loss, epoch_acc))\n",
+ "\n",
+ " # deep copy the model\n",
+ " if phase == 'val' and epoch_acc > best_acc:\n",
+ " best_acc = epoch_acc\n",
+ " best_model_wts = copy.deepcopy(model.state_dict())\n",
+ "\n",
+ " print()\n",
+ "\n",
+ " time_elapsed = time.time() - since\n",
+ " print('Training complete in {:.0f}m {:.0f}s'.format(\n",
+ " time_elapsed // 60, time_elapsed % 60))\n",
+ " print('Best val Acc: {:4f}'.format(best_acc))\n",
+ "\n",
+ " # load best model weights\n",
+ " model.load_state_dict(best_model_wts)\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_iK7budzrVNi"
+ },
+ "source": [
+ "### Support Function for Visualizing the Model Predictions\n",
+ "\n",
+ "Generic function to display predictions for a few images\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "id": "SFJ7b7dmrVNi"
+ },
+ "outputs": [],
+ "source": [
+ " def visualize_model(model, rows=3, cols=3):\n",
+ " was_training = model.training\n",
+ " model.eval()\n",
+ " current_row = current_col = 0\n",
+ " fig, ax = plt.subplots(rows, cols, figsize=(cols*2, rows*2))\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " for idx, (imgs, lbls) in enumerate(dataloaders['val']):\n",
+ " imgs = imgs.cpu()\n",
+ " lbls = lbls.cpu()\n",
+ "\n",
+ " outputs = model(imgs)\n",
+ " _, preds = torch.max(outputs, 1)\n",
+ "\n",
+ " for jdx in range(imgs.size()[0]):\n",
+ " imshow(imgs.data[jdx], ax=ax[current_row, current_col])\n",
+ " ax[current_row, current_col].axis('off')\n",
+ " ax[current_row, current_col].set_title('predicted: {}'.format(class_names[preds[jdx]]))\n",
+ "\n",
+ " current_col += 1\n",
+ " if current_col >= cols:\n",
+ " current_row += 1\n",
+ " current_col = 0\n",
+ " if current_row >= rows:\n",
+ " model.train(mode=was_training)\n",
+ " return\n",
+ " model.train(mode=was_training)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JqNqqR3LrVNk"
+ },
+ "source": [
+ "## Part 1. Training a Custom Classifier based on a Quantized Feature Extractor \n",
+ "\n",
+ "In this section you will use a “frozen” quantized feature extractor, and\n",
+ "train a custom classifier head on top of it. Unlike floating point\n",
+ "models, you don’t need to set requires_grad=False for the quantized\n",
+ "model, as it has no trainable parameters. Please, refer to the[documentation](https://pytorch.org/docs/stable/quantization.html) for\n",
+ "more details.\n",
+ "\n",
+ "Load a pretrained model: for this exercise you will be using [ResNet-18](https://pytorch.org/hub/pytorch_vision_resnet/).\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 104,
+ "referenced_widgets": [
+ "4a86c4d7da86454a805c4f387ae59c23",
+ "d83391fe1fd2435db90fc489bca2da75",
+ "ef452931f3924b148dc17ffa8b8e3c4e",
+ "f77cce8e6fff4526ae6e71dea57cd23d",
+ "f76b06e54a9a47d0999bb28f24f10b07",
+ "5cab679de098436ca9d51354a149b7f3",
+ "e26863e4d2d146ae9a7d7057d8dd6b74",
+ "2dca87c0834b4236975dde1b1e72169b"
+ ]
+ },
+ "id": "F7Mws9H9rVNl",
+ "outputId": "02915e90-013d-4672-e951-bd98bed762a7"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/yehanghan/miniconda3/envs/py39/lib/python3.9/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n",
+ " warnings.warn(\n",
+ "/home/yehanghan/miniconda3/envs/py39/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNet18_QuantizedWeights.IMAGENET1K_FBGEMM_V1`. You can also use `weights=ResNet18_QuantizedWeights.DEFAULT` to get the most up-to-date weights.\n",
+ " warnings.warn(msg)\n",
+ "Downloading: \"https://download.pytorch.org/models/quantized/resnet18_fbgemm_16fa66dd.pth\" to /home/yehanghan/.cache/torch/hub/checkpoints/resnet18_fbgemm_16fa66dd.pth\n",
+ "100%|██████████| 11.2M/11.2M [00:01<00:00, 8.45MB/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torchvision.models.quantization as models\n",
+ "\n",
+ "# We will need the number of filters in the `fc` for future use.\n",
+ "# Here the size of each output sample is set to 2.\n",
+ "# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).\n",
+ "model_fe = models.resnet18(pretrained=True, progress=True, quantize=True)\n",
+ "num_ftrs = model_fe.fc.in_features"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XcNwufxZrVNm"
+ },
+ "source": [
+ "At this point you need to modify the pretrained model. The model\n",
+ "has the quantize/dequantize blocks in the beginning and the end. However,\n",
+ "because you will only use the feature extractor, the dequantizatioin layer has\n",
+ "to move right before the linear layer (the head). The easiest way to do that\n",
+ "is to wrap the model in the ``nn.Sequential`` module.\n",
+ "\n",
+ "The first step is to isolate the feature extractor in the ResNet\n",
+ "model. Although in this example you are tasked to use all layers except\n",
+ "``fc`` as the feature extractor, in reality, you can take as many parts\n",
+ "as you need. This would be useful in case you would like to replace some\n",
+ "of the convolutional layers as well."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WhhPurYJrVNn"
+ },
+ "source": [
+ "**Note:** When separating the feature extractor from the rest of a quantized\n",
+ " model, you have to manually place the quantizer/dequantized in the\n",
+ " beginning and the end of the parts you want to keep quantized.\n",
+ "\n",
+ "The function below creates a model with a custom head."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "id": "SM_KR35FrVNn",
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "from torch import nn\n",
+ "\n",
+ "def create_combined_model(model_fe):\n",
+ " # Step 1. Isolate the feature extractor.\n",
+ " model_fe_features = nn.Sequential(\n",
+ " model_fe.quant, # Quantize the input\n",
+ " model_fe.conv1,\n",
+ " model_fe.bn1,\n",
+ " model_fe.relu,\n",
+ " model_fe.maxpool,\n",
+ " model_fe.layer1,\n",
+ " model_fe.layer2,\n",
+ " model_fe.layer3,\n",
+ " model_fe.layer4,\n",
+ " model_fe.avgpool,\n",
+ " model_fe.dequant, # Dequantize the output\n",
+ " )\n",
+ "\n",
+ " # Step 2. Create a new \"head\"\n",
+ " new_head = nn.Sequential(\n",
+ " nn.Dropout(p=0.5),\n",
+ " nn.Linear(num_ftrs, 2),\n",
+ " )\n",
+ "\n",
+ " # Step 3. Combine, and don't forget the quant stubs.\n",
+ " new_model = nn.Sequential(\n",
+ " model_fe_features,\n",
+ " nn.Flatten(1),\n",
+ " new_head,\n",
+ " )\n",
+ " return new_model\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nyIn2_MarVNp"
+ },
+ "source": [
+ "**Warning:** Currently the quantized models can only be run on CPU.\n",
+ " However, it is possible to send the non-quantized parts of the model to a GPU."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "id": "i_01GgyErVNq"
+ },
+ "outputs": [],
+ "source": [
+ "import torch.optim as optim\n",
+ "new_model = create_combined_model(model_fe)\n",
+ "new_model = new_model.to('cpu')\n",
+ "\n",
+ "criterion = nn.CrossEntropyLoss()\n",
+ "\n",
+ "# Note that we are only training the head.\n",
+ "optimizer_ft = optim.SGD(new_model.parameters(), lr=0.01, momentum=0.9)\n",
+ "\n",
+ "# Decay LR by a factor of 0.1 every 7 epochs\n",
+ "exp_lr_scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZarnvYk_rVNr"
+ },
+ "source": [
+ "### Train and evaluate\n",
+ "\n",
+ "This step takes around 15-25 min on CPU. Because the quantized model can\n",
+ "only run on the CPU, you cannot run the training on GPU.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "kMr1jz7MrVNs",
+ "outputId": "768b32c0-d7b9-4cc1-a519-9f9a63b2ffa3",
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 0/24\n",
+ "----------\n",
+ "train Loss: 0.5962 Acc: 0.7623\n",
+ "val Loss: 0.9077 Acc: 0.8235\n",
+ "\n",
+ "Epoch 1/24\n",
+ "----------\n",
+ "train Loss: 0.6166 Acc: 0.8852\n",
+ "val Loss: 0.3317 Acc: 0.9477\n",
+ "\n",
+ "Epoch 2/24\n",
+ "----------\n",
+ "train Loss: 0.3941 Acc: 0.9221\n",
+ "val Loss: 0.3614 Acc: 0.9281\n",
+ "\n",
+ "Epoch 3/24\n",
+ "----------\n",
+ "train Loss: 0.3568 Acc: 0.9139\n",
+ "val Loss: 0.4759 Acc: 0.9412\n",
+ "\n",
+ "Epoch 4/24\n",
+ "----------\n",
+ "train Loss: 0.3577 Acc: 0.9385\n",
+ "val Loss: 0.5076 Acc: 0.9216\n",
+ "\n",
+ "Epoch 5/24\n",
+ "----------\n",
+ "train Loss: 0.4154 Acc: 0.9139\n",
+ "val Loss: 0.5509 Acc: 0.9281\n",
+ "\n",
+ "Epoch 6/24\n",
+ "----------\n",
+ "train Loss: 0.6665 Acc: 0.9016\n",
+ "val Loss: 0.7157 Acc: 0.9020\n",
+ "\n",
+ "Epoch 7/24\n",
+ "----------\n",
+ "train Loss: 0.3930 Acc: 0.9262\n",
+ "val Loss: 0.4285 Acc: 0.9346\n",
+ "\n",
+ "Epoch 8/24\n",
+ "----------\n",
+ "train Loss: 0.4369 Acc: 0.9098\n",
+ "val Loss: 0.3819 Acc: 0.9346\n",
+ "\n",
+ "Epoch 9/24\n",
+ "----------\n",
+ "train Loss: 0.2015 Acc: 0.9549\n",
+ "val Loss: 0.3788 Acc: 0.9346\n",
+ "\n",
+ "Epoch 10/24\n",
+ "----------\n",
+ "train Loss: 0.1352 Acc: 0.9467\n",
+ "val Loss: 0.4000 Acc: 0.9346\n",
+ "\n",
+ "Epoch 11/24\n",
+ "----------\n",
+ "train Loss: 0.1680 Acc: 0.9713\n",
+ "val Loss: 0.4085 Acc: 0.9346\n",
+ "\n",
+ "Epoch 12/24\n",
+ "----------\n",
+ "train Loss: 0.2494 Acc: 0.9549\n",
+ "val Loss: 0.3744 Acc: 0.9477\n",
+ "\n",
+ "Epoch 13/24\n",
+ "----------\n",
+ "train Loss: 0.1993 Acc: 0.9590\n",
+ "val Loss: 0.4280 Acc: 0.9281\n",
+ "\n",
+ "Epoch 14/24\n",
+ "----------\n",
+ "train Loss: 0.2632 Acc: 0.9385\n",
+ "val Loss: 0.4216 Acc: 0.9281\n",
+ "\n",
+ "Epoch 15/24\n",
+ "----------\n",
+ "train Loss: 0.3534 Acc: 0.9344\n",
+ "val Loss: 0.4114 Acc: 0.9281\n",
+ "\n",
+ "Epoch 16/24\n",
+ "----------\n",
+ "train Loss: 0.2371 Acc: 0.9631\n",
+ "val Loss: 0.3965 Acc: 0.9346\n",
+ "\n",
+ "Epoch 17/24\n",
+ "----------\n",
+ "train Loss: 0.1156 Acc: 0.9631\n",
+ "val Loss: 0.3886 Acc: 0.9346\n",
+ "\n",
+ "Epoch 18/24\n",
+ "----------\n",
+ "train Loss: 0.2315 Acc: 0.9467\n",
+ "val Loss: 0.3831 Acc: 0.9346\n",
+ "\n",
+ "Epoch 19/24\n",
+ "----------\n",
+ "train Loss: 0.1650 Acc: 0.9590\n",
+ "val Loss: 0.3797 Acc: 0.9412\n",
+ "\n",
+ "Epoch 20/24\n",
+ "----------\n",
+ "train Loss: 0.1673 Acc: 0.9713\n",
+ "val Loss: 0.3788 Acc: 0.9412\n",
+ "\n",
+ "Epoch 21/24\n",
+ "----------\n",
+ "train Loss: 0.2714 Acc: 0.9508\n",
+ "val Loss: 0.3785 Acc: 0.9412\n",
+ "\n",
+ "Epoch 22/24\n",
+ "----------\n",
+ "train Loss: 0.1801 Acc: 0.9549\n",
+ "val Loss: 0.3783 Acc: 0.9412\n",
+ "\n",
+ "Epoch 23/24\n",
+ "----------\n",
+ "train Loss: 0.2443 Acc: 0.9385\n",
+ "val Loss: 0.3785 Acc: 0.9412\n",
+ "\n",
+ "Epoch 24/24\n",
+ "----------\n",
+ "train Loss: 0.1774 Acc: 0.9549\n",
+ "val Loss: 0.3784 Acc: 0.9412\n",
+ "\n",
+ "Training complete in 1m 57s\n",
+ "Best val Acc: 0.947712\n"
+ ]
+ }
+ ],
+ "source": [
+ "new_model = train_model(new_model, criterion, optimizer_ft, exp_lr_scheduler,\n",
+ " num_epochs=25, device='cpu')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 441
+ },
+ "id": "3SD1z15crVNu",
+ "outputId": "08022bd6-a40d-49c3-a7e5-78b6faee194c"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "visualize_model(new_model)\n",
+ "plt.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JHK0ccqp6vUY"
+ },
+ "source": [
+ "## Part 2. Finetuning the Quantizable Model\n",
+ "\n",
+ "\n",
+ "In this part, we fine tune the feature extractor used for transfer\n",
+ "learning, and quantize the feature extractor. Note that in both part 1\n",
+ "and 2, the feature extractor is quantized. The difference is that in\n",
+ "part 1, we use a pretrained quantized model. In this part, we create a\n",
+ "quantized feature extractor after fine tuning on the data-set of\n",
+ "interest, so this is a way to get better accuracy with transfer learning\n",
+ "while having the benefits of quantization. Note that in our specific\n",
+ "example, the training set is really small (120 images) so the benefits\n",
+ "of fine tuning the entire model is not apparent. However, the procedure\n",
+ "shown here will improve accuracy for transfer learning with larger\n",
+ "datasets.\n",
+ "\n",
+ "The pretrained feature extractor must be quantizable.\n",
+ "To make sure it is quantizable, perform the following steps:\n",
+ "\n",
+ " 1. Fuse ``(Conv, BN, ReLU)``, ``(Conv, BN)``, and ``(Conv, ReLU)`` using\n",
+ " ``torch.quantization.fuse_modules``.\n",
+ " 2. Connect the feature extractor with a custom head.\n",
+ " This requires dequantizing the output of the feature extractor.\n",
+ " 3. Insert fake-quantization modules at appropriate locations\n",
+ " in the feature extractor to mimic quantization during training.\n",
+ "\n",
+ "For step (1), we use models from ``torchvision/models/quantization``, which\n",
+ "have a member method ``fuse_model``. This function fuses all the ``conv``,\n",
+ "``bn``, and ``relu`` modules. For custom models, this would require calling\n",
+ "the ``torch.quantization.fuse_modules`` API with the list of modules to fuse\n",
+ "manually.\n",
+ "\n",
+ "Step (2) is performed by the ``create_combined_model`` function\n",
+ "used in the previous section.\n",
+ "\n",
+ "Step (3) is achieved by using ``torch.quantization.prepare_qat``, which\n",
+ "inserts fake-quantization modules.\n",
+ "\n",
+ "\n",
+ "As step (4), you can start \"finetuning\" the model, and after that convert\n",
+ "it to a fully quantized version (Step 5).\n",
+ "\n",
+ "To convert the fine tuned model into a quantized model you can call the\n",
+ "``torch.quantization.convert`` function (in our case only\n",
+ "the feature extractor is quantized).\n",
+ "\n",
+ "**Note:** Because of the random initialization your results might differ from\n",
+ " the results shown in this tutorial."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ZBZG35ZQ59rq"
+ },
+ "outputs": [],
+ "source": [
+ "# notice `quantize=False`\n",
+ "model = models.resnet18(pretrained=True, progress=True, quantize=False)\n",
+ "num_ftrs = model.fc.in_features\n",
+ "\n",
+ "# Step 1\n",
+ "model.train()\n",
+ "model.fuse_model()\n",
+ "# Step 2\n",
+ "model_ft = create_combined_model(model)\n",
+ "model_ft[0].qconfig = torch.quantization.default_qat_qconfig # Use default QAT configuration\n",
+ "# Step 3\n",
+ "model_ft = torch.quantization.prepare_qat(model_ft, inplace=True)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0wpc9W-0HFfQ"
+ },
+ "source": [
+ "### Finetuning the model\n",
+ "\n",
+ "In the current tutorial the whole model is fine tuned. In\n",
+ "general, this will lead to higher accuracy. However, due to the small\n",
+ "training set used here, we end up overfitting to the training set.\n",
+ "\n",
+ "\n",
+ "Step 4. Fine tune the model\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "QGMVVrcsHJMc",
+ "outputId": "e50e5825-7a40-471a-b542-68f187830e1d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 0/24\n",
+ "----------\n",
+ "train Loss: 0.5984 Acc: 0.6762\n",
+ "val Loss: 0.3433 Acc: 0.8758\n",
+ "\n",
+ "Epoch 1/24\n",
+ "----------\n",
+ "train Loss: 0.3330 Acc: 0.8525\n",
+ "val Loss: 0.2332 Acc: 0.9216\n",
+ "\n",
+ "Epoch 2/24\n",
+ "----------\n",
+ "train Loss: 0.2361 Acc: 0.9180\n",
+ "val Loss: 0.2004 Acc: 0.9346\n",
+ "\n",
+ "Epoch 3/24\n",
+ "----------\n",
+ "train Loss: 0.1446 Acc: 0.9590\n",
+ "val Loss: 0.1987 Acc: 0.9281\n",
+ "\n",
+ "Epoch 4/24\n",
+ "----------\n",
+ "train Loss: 0.1869 Acc: 0.9385\n",
+ "val Loss: 0.1946 Acc: 0.9216\n",
+ "\n",
+ "Epoch 5/24\n",
+ "----------\n",
+ "train Loss: 0.1221 Acc: 0.9713\n",
+ "val Loss: 0.2129 Acc: 0.9216\n",
+ "\n",
+ "Epoch 6/24\n",
+ "----------\n",
+ "train Loss: 0.0993 Acc: 0.9672\n",
+ "val Loss: 0.1961 Acc: 0.9281\n",
+ "\n",
+ "Epoch 7/24\n",
+ "----------\n",
+ "train Loss: 0.0896 Acc: 0.9754\n",
+ "val Loss: 0.2083 Acc: 0.9281\n",
+ "\n",
+ "Epoch 8/24\n",
+ "----------\n",
+ "train Loss: 0.1083 Acc: 0.9672\n",
+ "val Loss: 0.1921 Acc: 0.9412\n",
+ "\n",
+ "Epoch 9/24\n",
+ "----------\n",
+ "train Loss: 0.0977 Acc: 0.9672\n",
+ "val Loss: 0.2668 Acc: 0.9020\n",
+ "\n",
+ "Epoch 10/24\n",
+ "----------\n",
+ "train Loss: 0.0800 Acc: 0.9877\n",
+ "val Loss: 0.2195 Acc: 0.9085\n",
+ "\n",
+ "Epoch 11/24\n",
+ "----------\n",
+ "train Loss: 0.1071 Acc: 0.9631\n",
+ "val Loss: 0.1794 Acc: 0.9412\n",
+ "\n",
+ "Epoch 12/24\n",
+ "----------\n",
+ "train Loss: 0.0750 Acc: 0.9672\n",
+ "val Loss: 0.1997 Acc: 0.9216\n",
+ "\n",
+ "Epoch 13/24\n",
+ "----------\n",
+ "train Loss: 0.0595 Acc: 0.9877\n",
+ "val Loss: 0.1926 Acc: 0.9281\n",
+ "\n",
+ "Epoch 14/24\n",
+ "----------\n",
+ "train Loss: 0.0645 Acc: 0.9877\n",
+ "val Loss: 0.1942 Acc: 0.9281\n",
+ "\n",
+ "Epoch 15/24\n",
+ "----------\n",
+ "train Loss: 0.0538 Acc: 0.9836\n",
+ "val Loss: 0.1833 Acc: 0.9346\n",
+ "\n",
+ "Epoch 16/24\n",
+ "----------\n",
+ "train Loss: 0.0850 Acc: 0.9754\n",
+ "val Loss: 0.1851 Acc: 0.9412\n",
+ "\n",
+ "Epoch 17/24\n",
+ "----------\n",
+ "train Loss: 0.0779 Acc: 0.9754\n",
+ "val Loss: 0.2034 Acc: 0.9216\n",
+ "\n",
+ "Epoch 18/24\n",
+ "----------\n",
+ "train Loss: 0.0781 Acc: 0.9713\n",
+ "val Loss: 0.1997 Acc: 0.9150\n",
+ "\n",
+ "Epoch 19/24\n",
+ "----------\n",
+ "train Loss: 0.0523 Acc: 0.9918\n",
+ "val Loss: 0.2107 Acc: 0.9150\n",
+ "\n",
+ "Epoch 20/24\n",
+ "----------\n",
+ "train Loss: 0.0597 Acc: 0.9836\n",
+ "val Loss: 0.1816 Acc: 0.9281\n",
+ "\n",
+ "Epoch 21/24\n",
+ "----------\n",
+ "train Loss: 0.0749 Acc: 0.9754\n",
+ "val Loss: 0.1803 Acc: 0.9346\n",
+ "\n",
+ "Epoch 22/24\n",
+ "----------\n",
+ "train Loss: 0.0479 Acc: 0.9959\n",
+ "val Loss: 0.1882 Acc: 0.9346\n",
+ "\n",
+ "Epoch 23/24\n",
+ "----------\n",
+ "train Loss: 0.0853 Acc: 0.9631\n",
+ "val Loss: 0.1851 Acc: 0.9346\n",
+ "\n",
+ "Epoch 24/24\n",
+ "----------\n",
+ "train Loss: 0.0570 Acc: 0.9918\n",
+ "val Loss: 0.1847 Acc: 0.9281\n",
+ "\n",
+ "Training complete in 2m 44s\n",
+ "Best val Acc: 0.941176\n"
+ ]
+ }
+ ],
+ "source": [
+ "for param in model_ft.parameters():\n",
+ " param.requires_grad = True\n",
+ "\n",
+ "model_ft.to(device) # We can fine-tune on GPU if available\n",
+ "\n",
+ "criterion = nn.CrossEntropyLoss()\n",
+ "\n",
+ "# Note that we are training everything, so the learning rate is lower\n",
+ "# Notice the smaller learning rate\n",
+ "optimizer_ft = optim.SGD(model_ft.parameters(), lr=1e-3, momentum=0.9, weight_decay=0.1)\n",
+ "\n",
+ "# Decay LR by a factor of 0.3 every several epochs\n",
+ "exp_lr_scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=5, gamma=0.3)\n",
+ "\n",
+ "model_ft_tuned = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
+ " num_epochs=25, device=device)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4A9WvbUlHIzT"
+ },
+ "source": [
+ "Step 5. Convert to quantized model\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "jWm3bw8zHO1T"
+ },
+ "outputs": [],
+ "source": [
+ "from torch.quantization import convert\n",
+ "model_ft_tuned.cpu()\n",
+ "\n",
+ "model_quantized_and_trained = convert(model_ft_tuned, inplace=False)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jAVbFinzgXYM"
+ },
+ "source": [
+ "Lets see how the quantized model performs on a few images\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "nD9AwylKHR7k"
+ },
+ "outputs": [],
+ "source": [
+ "visualize_model(model_quantized_and_trained)\n",
+ "\n",
+ "plt.ioff()\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "name": " Quantized Transfer Learning",
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diff --git a/README.md b/README.md
deleted file mode 100644
index 0025691..0000000
--- a/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-# Deeplearning.ai
-This is project contains assignment code for Deeplearning.ai
diff --git a/Regularization.py b/Regularization.py
deleted file mode 100644
index 97f9163..0000000
--- a/Regularization.py
+++ /dev/null
@@ -1,683 +0,0 @@
-
-# coding: utf-8
-
-# # Regularization
-#
-# Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that **overfitting can be a serious problem**, if the training dataset is not big enough. Sure it does well on the training set, but the learned network **doesn't generalize to new examples** that it has never seen!
-#
-# **You will learn to:** Use regularization in your deep learning models.
-#
-# Let's first import the packages you are going to use.
-
-# In[2]:
-
-# import packages
-import numpy as np
-import matplotlib.pyplot as plt
-from reg_utils import sigmoid, relu, plot_decision_boundary, initialize_parameters, load_2D_dataset, predict_dec
-from reg_utils import compute_cost, predict, forward_propagation, backward_propagation, update_parameters
-import sklearn
-import sklearn.datasets
-import scipy.io
-from testCases import *
-
-get_ipython().magic('matplotlib inline')
-plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots
-plt.rcParams['image.interpolation'] = 'nearest'
-plt.rcParams['image.cmap'] = 'gray'
-
-
-# **Problem Statement**: You have just been hired as an AI expert by the French Football Corporation. They would like you to recommend positions where France's goal keeper should kick the ball so that the French team's players can then hit it with their head.
-#
-# 
-# **Figure 1** : **Football field**
The goal keeper kicks the ball in the air, the players of each team are fighting to hit the ball with their head
-#
-
-# Of course, because you changed the cost, you have to change backward propagation as well! All the gradients have to be computed with respect to this new cost.
-#
-# **Exercise**: Implement the changes needed in backward propagation to take into account regularization. The changes only concern dW1, dW2 and dW3. For each, you have to add the regularization term's gradient ($\frac{d}{dW} ( \frac{1}{2}\frac{\lambda}{m} W^2) = \frac{\lambda}{m} W$).
-
-# In[9]:
-
-# GRADED FUNCTION: backward_propagation_with_regularization
-
-def backward_propagation_with_regularization(X, Y, cache, lambd):
- """
- Implements the backward propagation of our baseline model to which we added an L2 regularization.
-
- Arguments:
- X -- input dataset, of shape (input size, number of examples)
- Y -- "true" labels vector, of shape (output size, number of examples)
- cache -- cache output from forward_propagation()
- lambd -- regularization hyperparameter, scalar
-
- Returns:
- gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables
- """
-
- m = X.shape[1]
- (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3) = cache
-
- dZ3 = A3 - Y
-
- ### START CODE HERE ### (approx. 1 line)
- dW3 = 1./m * np.dot(dZ3, A2.T) + (lambd/m)*W3
- ### END CODE HERE ###
- db3 = 1./m * np.sum(dZ3, axis=1, keepdims = True)
-
- dA2 = np.dot(W3.T, dZ3)
- dZ2 = np.multiply(dA2, np.int64(A2 > 0))
- ### START CODE HERE ### (approx. 1 line)
- dW2 = 1./m * np.dot(dZ2, A1.T) + (lambd/m)*W2
- ### END CODE HERE ###
- db2 = 1./m * np.sum(dZ2, axis=1, keepdims = True)
-
- dA1 = np.dot(W2.T, dZ2)
- dZ1 = np.multiply(dA1, np.int64(A1 > 0))
- ### START CODE HERE ### (approx. 1 line)
- dW1 = 1./m * np.dot(dZ1, X.T) + (lambd/m)*W1
- ### END CODE HERE ###
- db1 = 1./m * np.sum(dZ1, axis=1, keepdims = True)
-
- gradients = {"dZ3": dZ3, "dW3": dW3, "db3": db3,"dA2": dA2,
- "dZ2": dZ2, "dW2": dW2, "db2": db2, "dA1": dA1,
- "dZ1": dZ1, "dW1": dW1, "db1": db1}
-
- return gradients
-
-
-# In[10]:
-
-X_assess, Y_assess, cache = backward_propagation_with_regularization_test_case()
-
-grads = backward_propagation_with_regularization(X_assess, Y_assess, cache, lambd = 0.7)
-print ("dW1 = "+ str(grads["dW1"]))
-print ("dW2 = "+ str(grads["dW2"]))
-print ("dW3 = "+ str(grads["dW3"]))
-
-
-# **Expected Output**:
-#
-#
-#
-
-# Let's now run the model with L2 regularization $(\lambda = 0.7)$. The `model()` function will call:
-# - `compute_cost_with_regularization` instead of `compute_cost`
-# - `backward_propagation_with_regularization` instead of `backward_propagation`
-
-# In[11]:
-
-parameters = model(train_X, train_Y, lambd = 0.7)
-print ("On the train set:")
-predictions_train = predict(train_X, train_Y, parameters)
-print ("On the test set:")
-predictions_test = predict(test_X, test_Y, parameters)
-
-
-# Congrats, the test set accuracy increased to 93%. You have saved the French football team!
-#
-# You are not overfitting the training data anymore. Let's plot the decision boundary.
-
-# In[12]:
-
-plt.title("Model with L2-regularization")
-axes = plt.gca()
-axes.set_xlim([-0.75,0.40])
-axes.set_ylim([-0.75,0.65])
-plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)
-
-
-# **Observations**:
-# - The value of $\lambda$ is a hyperparameter that you can tune using a dev set.
-# - L2 regularization makes your decision boundary smoother. If $\lambda$ is too large, it is also possible to "oversmooth", resulting in a model with high bias.
-#
-# **What is L2-regularization actually doing?**:
-#
-# L2-regularization relies on the assumption that a model with small weights is simpler than a model with large weights. Thus, by penalizing the square values of the weights in the cost function you drive all the weights to smaller values. It becomes too costly for the cost to have large weights! This leads to a smoother model in which the output changes more slowly as the input changes.
-#
-#
-# **What you should remember** -- the implications of L2-regularization on:
-# - The cost computation:
-# - A regularization term is added to the cost
-# - The backpropagation function:
-# - There are extra terms in the gradients with respect to weight matrices
-# - Weights end up smaller ("weight decay"):
-# - Weights are pushed to smaller values.
-
-# ## 3 - Dropout
-#
-# Finally, **dropout** is a widely used regularization technique that is specific to deep learning.
-# **It randomly shuts down some neurons in each iteration.** Watch these two videos to see what this means!
-#
-#
-#
-#
-#
-#
-#
-#
-# Figure 2 : Drop-out on the second hidden layer.
At each iteration, you shut down (= set to zero) each neuron of a layer with probability $1 - keep\_prob$ or keep it with probability $keep\_prob$ (50% here). The dropped neurons don't contribute to the training in both the forward and backward propagations of the iteration.
-#
-#
-#
-# Figure 3 : Drop-out on the first and third hidden layers.
$1^{st}$ layer: we shut down on average 40% of the neurons. $3^{rd}$ layer: we shut down on average 20% of the neurons.
-#
-
-# ### 3.2 - Backward propagation with dropout
-#
-# **Exercise**: Implement the backward propagation with dropout. As before, you are training a 3 layer network. Add dropout to the first and second hidden layers, using the masks $D^{[1]}$ and $D^{[2]}$ stored in the cache.
-#
-# **Instruction**:
-# Backpropagation with dropout is actually quite easy. You will have to carry out 2 Steps:
-# 1. You had previously shut down some neurons during forward propagation, by applying a mask $D^{[1]}$ to `A1`. In backpropagation, you will have to shut down the same neurons, by reapplying the same mask $D^{[1]}$ to `dA1`.
-# 2. During forward propagation, you had divided `A1` by `keep_prob`. In backpropagation, you'll therefore have to divide `dA1` by `keep_prob` again (the calculus interpretation is that if $A^{[1]}$ is scaled by `keep_prob`, then its derivative $dA^{[1]}$ is also scaled by the same `keep_prob`).
-#
-
-# In[23]:
-
-# GRADED FUNCTION: backward_propagation_with_dropout
-
-def backward_propagation_with_dropout(X, Y, cache, keep_prob):
- """
- Implements the backward propagation of our baseline model to which we added dropout.
-
- Arguments:
- X -- input dataset, of shape (2, number of examples)
- Y -- "true" labels vector, of shape (output size, number of examples)
- cache -- cache output from forward_propagation_with_dropout()
- keep_prob - probability of keeping a neuron active during drop-out, scalar
-
- Returns:
- gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables
- """
-
- m = X.shape[1]
- (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3) = cache
-
- dZ3 = A3 - Y
- dW3 = 1./m * np.dot(dZ3, A2.T)
- db3 = 1./m * np.sum(dZ3, axis=1, keepdims = True)
- dA2 = np.dot(W3.T, dZ3)
- ### START CODE HERE ### (≈ 2 lines of code)
- dA2 = dA2 * D2 # Step 1: Apply mask D2 to shut down the same neurons as during the forward propagation
- dA2 = dA2 / keep_prob # Step 2: Scale the value of neurons that haven't been shut down
- ### END CODE HERE ###
- dZ2 = np.multiply(dA2, np.int64(A2 > 0))
- dW2 = 1./m * np.dot(dZ2, A1.T)
- db2 = 1./m * np.sum(dZ2, axis=1, keepdims = True)
-
- dA1 = np.dot(W2.T, dZ2)
- ### START CODE HERE ### (≈ 2 lines of code)
- dA1 = dA1 * D1 # Step 1: Apply mask D1 to shut down the same neurons as during the forward propagation
- dA1 = dA1 / keep_prob # Step 2: Scale the value of neurons that haven't been shut down
- ### END CODE HERE ###
- dZ1 = np.multiply(dA1, np.int64(A1 > 0))
- dW1 = 1./m * np.dot(dZ1, X.T)
- db1 = 1./m * np.sum(dZ1, axis=1, keepdims = True)
-
- gradients = {"dZ3": dZ3, "dW3": dW3, "db3": db3,"dA2": dA2,
- "dZ2": dZ2, "dW2": dW2, "db2": db2, "dA1": dA1,
- "dZ1": dZ1, "dW1": dW1, "db1": db1}
-
- return gradients
-
-
-# In[24]:
-
-X_assess, Y_assess, cache = backward_propagation_with_dropout_test_case()
-
-gradients = backward_propagation_with_dropout(X_assess, Y_assess, cache, keep_prob = 0.8)
-
-print ("dA1 = " + str(gradients["dA1"]))
-print ("dA2 = " + str(gradients["dA2"]))
-
-
-# **Expected Output**:
-#
-#
-#
-
-# Let's now run the model with dropout (`keep_prob = 0.86`). It means at every iteration you shut down each neurons of layer 1 and 2 with 24% probability. The function `model()` will now call:
-# - `forward_propagation_with_dropout` instead of `forward_propagation`.
-# - `backward_propagation_with_dropout` instead of `backward_propagation`.
-
-# In[25]:
-
-parameters = model(train_X, train_Y, keep_prob = 0.86, learning_rate = 0.3)
-
-print ("On the train set:")
-predictions_train = predict(train_X, train_Y, parameters)
-print ("On the test set:")
-predictions_test = predict(test_X, test_Y, parameters)
-
-
-# Dropout works great! The test accuracy has increased again (to 95%)! Your model is not overfitting the training set and does a great job on the test set. The French football team will be forever grateful to you!
-#
-# Run the code below to plot the decision boundary.
-
-# In[26]:
-
-plt.title("Model with dropout")
-axes = plt.gca()
-axes.set_xlim([-0.75,0.40])
-axes.set_ylim([-0.75,0.65])
-plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)
-
-
-# **Note**:
-# - A **common mistake** when using dropout is to use it both in training and testing. You should use dropout (randomly eliminate nodes) only in training.
-# - Deep learning frameworks like [tensorflow](https://www.tensorflow.org/api_docs/python/tf/nn/dropout), [PaddlePaddle](http://doc.paddlepaddle.org/release_doc/0.9.0/doc/ui/api/trainer_config_helpers/attrs.html), [keras](https://keras.io/layers/core/#dropout) or [caffe](http://caffe.berkeleyvision.org/tutorial/layers/dropout.html) come with a dropout layer implementation. Don't stress - you will soon learn some of these frameworks.
-#
-#
-# **What you should remember about dropout:**
-# - Dropout is a regularization technique.
-# - You only use dropout during training. Don't use dropout (randomly eliminate nodes) during test time.
-# - Apply dropout both during forward and backward propagation.
-# - During training time, divide each dropout layer by keep_prob to keep the same expected value for the activations. For example, if keep_prob is 0.5, then we will on average shut down half the nodes, so the output will be scaled by 0.5 since only the remaining half are contributing to the solution. Dividing by 0.5 is equivalent to multiplying by 2. Hence, the output now has the same expected value. You can check that this works even when keep_prob is other values than 0.5.
-
-# ## 4 - Conclusions
-
-# **Here are the results of our three models**:
-#
-#
-#
-
-# Note that regularization hurts training set performance! This is because it limits the ability of the network to overfit to the training set. But since it ultimately gives better test accuracy, it is helping your system.
-
-# Congratulations for finishing this assignment! And also for revolutionizing French football. :-)
-
-#
-# **What we want you to remember from this notebook**:
-# - Regularization will help you reduce overfitting.
-# - Regularization will drive your weights to lower values.
-# - L2 regularization and Dropout are two very effective regularization techniques.
diff --git a/Residual+Networks+-+v2.ipynb b/Residual+Networks+-+v2.ipynb
deleted file mode 100644
index c47e4b5..0000000
--- a/Residual+Networks+-+v2.ipynb
+++ /dev/null
@@ -1,3192 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Residual Networks\n",
- "\n",
- "Welcome to the second assignment of this week! You will learn how to build very deep convolutional networks, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, they are hard to train. Residual Networks, introduced by [He et al.](https://arxiv.org/pdf/1512.03385.pdf), allow you to train much deeper networks than were previously practically feasible.\n",
- "\n",
- "**In this assignment, you will:**\n",
- "- Implement the basic building blocks of ResNets. \n",
- "- Put together these building blocks to implement and train a state-of-the-art neural network for image classification. \n",
- "\n",
- "This assignment will be done in Keras. \n",
- "\n",
- "Before jumping into the problem, let's run the cell below to load the required packages."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from keras import layers\n",
- "from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D\n",
- "from keras.models import Model, load_model\n",
- "from keras.preprocessing import image\n",
- "from keras.utils import layer_utils\n",
- "from keras.utils.data_utils import get_file\n",
- "from keras.applications.imagenet_utils import preprocess_input\n",
- "import pydot\n",
- "from IPython.display import SVG\n",
- "from keras.utils.vis_utils import model_to_dot\n",
- "from keras.utils import plot_model\n",
- "from resnets_utils import *\n",
- "from keras.initializers import glorot_uniform\n",
- "import scipy.misc\n",
- "from matplotlib.pyplot import imshow\n",
- "%matplotlib inline\n",
- "\n",
- "import keras.backend as K\n",
- "K.set_image_data_format('channels_last')\n",
- "K.set_learning_phase(1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1 - The problem of very deep neural networks\n",
- "\n",
- "Last week, you built your first convolutional neural network. In recent years, neural networks have become deeper, with state-of-the-art networks going from just a few layers (e.g., AlexNet) to over a hundred layers.\n",
- "\n",
- "The main benefit of a very deep network is that it can represent very complex functions. It can also learn features at many different levels of abstraction, from edges (at the lower layers) to very complex features (at the deeper layers). However, using a deeper network doesn't always help. A huge barrier to training them is vanishing gradients: very deep networks often have a gradient signal that goes to zero quickly, thus making gradient descent unbearably slow. More specifically, during gradient descent, as you backprop from the final layer back to the first layer, you are multiplying by the weight matrix on each step, and thus the gradient can decrease exponentially quickly to zero (or, in rare cases, grow exponentially quickly and \"explode\" to take very large values). \n",
- "\n",
- "During training, you might therefore see the magnitude (or norm) of the gradient for the earlier layers descrease to zero very rapidly as training proceeds: "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "![](\"images/vanishing_grad_kiank.png\")
\n",
- " **Figure 1** : **Vanishing gradient**
The speed of learning decreases very rapidly for the early layers as the network trains ![](\"images/skip_connection_kiank.png\")
\n",
- " **Figure 2** : A ResNet block showing a **skip-connection**
![](\"images/idblock2_kiank.png\")
\n",
- " **Figure 3** : **Identity block.** Skip connection \"skips over\" 2 layers. ![](\"images/idblock3_kiank.png\")
\n",
- " **Figure 4** : **Identity block.** Skip connection \"skips over\" 3 layers. \n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2.2 - The convolutional block\n",
- "\n",
- "You've implemented the ResNet identity block. Next, the ResNet \"convolutional block\" is the other type of block. You can use this type of block when the input and output dimensions don't match up. The difference with the identity block is that there is a CONV2D layer in the shortcut path: \n",
- "\n",
- "![](\"images/convblock_kiank.png\")
\n",
- " **Figure 4** : **Convolutional block** \n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 3 - Building your first ResNet model (50 layers)\n",
- "\n",
- "You now have the necessary blocks to build a very deep ResNet. The following figure describes in detail the architecture of this neural network. \"ID BLOCK\" in the diagram stands for \"Identity block,\" and \"ID BLOCK x3\" means you should stack 3 identity blocks together.\n",
- "\n",
- "![](\"images/resnet_kiank.png\")
\n",
- " **Figure 5** : **ResNet-50 model** ![](\"images/signs_data_kiank.png\")
\n",
- " **Figure 6** : **SIGNS dataset** "
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model.fit(X_train, Y_train, epochs = 2, batch_size = 32)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Expected Output**:\n",
- "\n",
- "\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's see how this model (trained on only two epochs) performs on the test set."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "120/120 [==============================] - 10s \n",
- "Loss = 2.93200918833\n",
- "Test Accuracy = 0.166666666667\n"
- ]
- }
- ],
- "source": [
- "preds = model.evaluate(X_test, Y_test)\n",
- "print (\"Loss = \" + str(preds[0]))\n",
- "print (\"Test Accuracy = \" + str(preds[1]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Expected Output**:\n",
- "\n",
- "\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "For the purpose of this assignment, we've asked you to train the model only for two epochs. You can see that it achieves poor performances. Please go ahead and submit your assignment; to check correctness, the online grader will run your code only for a small number of epochs as well."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "After you have finished this official (graded) part of this assignment, you can also optionally train the ResNet for more iterations, if you want. We get a lot better performance when we train for ~20 epochs, but this will take more than an hour when training on a CPU. \n",
- "\n",
- "Using a GPU, we've trained our own ResNet50 model's weights on the SIGNS dataset. You can load and run our trained model on the test set in the cells below. It may take ≈1min to load the model."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "model = load_model('ResNet50.h5') "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "120/120 [==============================] - 10s \n",
- "Loss = 0.530178320408\n",
- "Test Accuracy = 0.866666662693\n"
- ]
- }
- ],
- "source": [
- "preds = model.evaluate(X_test, Y_test)\n",
- "print (\"Loss = \" + str(preds[0]))\n",
- "print (\"Test Accuracy = \" + str(preds[1]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "ResNet50 is a powerful model for image classification when it is trained for an adequate number of iterations. We hope you can use what you've learnt and apply it to your own classification problem to perform state-of-the-art accuracy.\n",
- "\n",
- "Congratulations on finishing this assignment! You've now implemented a state-of-the-art image classification system! "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 4 - Test on your own image (Optional/Ungraded)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If you wish, you can also take a picture of your own hand and see the output of the model. To do this:\n",
- " 1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n",
- " 2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n",
- " 3. Write your image's name in the following code\n",
- " 4. Run the code and check if the algorithm is right! "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Input image shape: (1, 64, 64, 3)\n",
- "class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = \n",
- "[[ 1. 0. 0. 0. 0. 0.]]\n"
- ]
- },
- {
- "data": {
- "image/png": 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E7k1rTQDqQ7kRbm+rWjfQXoFiedlwip0acwPVmhoFyhZetZzaHseBZ8kqKhMS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yCrJuTN+o96NXYR71yZlYvLHYhiESdTwNVVnu5ExTKdEKIKPAR6cuZKOF79eOMACBVBRJsHZA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\n/PMAfuaWPvN7wO72RwF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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "img_path = 'images/my_image.jpg'\n",
- "img = image.load_img(img_path, target_size=(64, 64))\n",
- "x = image.img_to_array(img)\n",
- "x = np.expand_dims(x, axis=0)\n",
- "x = preprocess_input(x)\n",
- "print('Input image shape:', x.shape)\n",
- "my_image = scipy.misc.imread(img_path)\n",
- "imshow(my_image)\n",
- "print(\"class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = \")\n",
- "print(model.predict(x))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can also print a summary of your model by running the following code."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "____________________________________________________________________________________________________\n",
- "Layer (type) Output Shape Param # Connected to \n",
- "====================================================================================================\n",
- "input_1 (InputLayer) (None, 64, 64, 3) 0 \n",
- "____________________________________________________________________________________________________\n",
- "zero_padding2d_1 (ZeroPadding2D) (None, 70, 70, 3) 0 input_1[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "conv1 (Conv2D) (None, 32, 32, 64) 9472 zero_padding2d_1[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn_conv1 (BatchNormalization) (None, 32, 32, 64) 256 conv1[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_4 (Activation) (None, 32, 32, 64) 0 bn_conv1[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "max_pooling2d_1 (MaxPooling2D) (None, 15, 15, 64) 0 activation_4[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res2a_branch2a (Conv2D) (None, 15, 15, 64) 4160 max_pooling2d_1[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn2a_branch2a (BatchNormalizatio (None, 15, 15, 64) 256 res2a_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_5 (Activation) (None, 15, 15, 64) 0 bn2a_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res2a_branch2b (Conv2D) (None, 15, 15, 64) 36928 activation_5[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn2a_branch2b (BatchNormalizatio (None, 15, 15, 64) 256 res2a_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_6 (Activation) (None, 15, 15, 64) 0 bn2a_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res2a_branch2c (Conv2D) (None, 15, 15, 256) 16640 activation_6[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res2a_branch1 (Conv2D) (None, 15, 15, 256) 16640 max_pooling2d_1[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn2a_branch2c (BatchNormalizatio (None, 15, 15, 256) 1024 res2a_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn2a_branch1 (BatchNormalization (None, 15, 15, 256) 1024 res2a_branch1[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_2 (Add) (None, 15, 15, 256) 0 bn2a_branch2c[0][0] \n",
- " bn2a_branch1[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_7 (Activation) (None, 15, 15, 256) 0 add_2[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res2b_branch2a (Conv2D) (None, 15, 15, 64) 16448 activation_7[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn2b_branch2a (BatchNormalizatio (None, 15, 15, 64) 256 res2b_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_8 (Activation) (None, 15, 15, 64) 0 bn2b_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res2b_branch2b (Conv2D) (None, 15, 15, 64) 36928 activation_8[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn2b_branch2b (BatchNormalizatio (None, 15, 15, 64) 256 res2b_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_9 (Activation) (None, 15, 15, 64) 0 bn2b_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res2b_branch2c (Conv2D) (None, 15, 15, 256) 16640 activation_9[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn2b_branch2c (BatchNormalizatio (None, 15, 15, 256) 1024 res2b_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_3 (Add) (None, 15, 15, 256) 0 bn2b_branch2c[0][0] \n",
- " activation_7[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_10 (Activation) (None, 15, 15, 256) 0 add_3[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res2c_branch2a (Conv2D) (None, 15, 15, 64) 16448 activation_10[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn2c_branch2a (BatchNormalizatio (None, 15, 15, 64) 256 res2c_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_11 (Activation) (None, 15, 15, 64) 0 bn2c_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res2c_branch2b (Conv2D) (None, 15, 15, 64) 36928 activation_11[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn2c_branch2b (BatchNormalizatio (None, 15, 15, 64) 256 res2c_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_12 (Activation) (None, 15, 15, 64) 0 bn2c_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res2c_branch2c (Conv2D) (None, 15, 15, 256) 16640 activation_12[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn2c_branch2c (BatchNormalizatio (None, 15, 15, 256) 1024 res2c_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_4 (Add) (None, 15, 15, 256) 0 bn2c_branch2c[0][0] \n",
- " activation_10[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_13 (Activation) (None, 15, 15, 256) 0 add_4[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res3a_branch2a (Conv2D) (None, 8, 8, 128) 32896 activation_13[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn3a_branch2a (BatchNormalizatio (None, 8, 8, 128) 512 res3a_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_14 (Activation) (None, 8, 8, 128) 0 bn3a_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res3a_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_14[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn3a_branch2b (BatchNormalizatio (None, 8, 8, 128) 512 res3a_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_15 (Activation) (None, 8, 8, 128) 0 bn3a_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res3a_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_15[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res3a_branch1 (Conv2D) (None, 8, 8, 512) 131584 activation_13[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn3a_branch2c (BatchNormalizatio (None, 8, 8, 512) 2048 res3a_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn3a_branch1 (BatchNormalization (None, 8, 8, 512) 2048 res3a_branch1[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_5 (Add) (None, 8, 8, 512) 0 bn3a_branch2c[0][0] \n",
- " bn3a_branch1[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_16 (Activation) (None, 8, 8, 512) 0 add_5[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res3b_branch2a (Conv2D) (None, 8, 8, 128) 65664 activation_16[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn3b_branch2a (BatchNormalizatio (None, 8, 8, 128) 512 res3b_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_17 (Activation) (None, 8, 8, 128) 0 bn3b_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res3b_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_17[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn3b_branch2b (BatchNormalizatio (None, 8, 8, 128) 512 res3b_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_18 (Activation) (None, 8, 8, 128) 0 bn3b_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res3b_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_18[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn3b_branch2c (BatchNormalizatio (None, 8, 8, 512) 2048 res3b_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_6 (Add) (None, 8, 8, 512) 0 bn3b_branch2c[0][0] \n",
- " activation_16[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_19 (Activation) (None, 8, 8, 512) 0 add_6[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res3c_branch2a (Conv2D) (None, 8, 8, 128) 65664 activation_19[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn3c_branch2a (BatchNormalizatio (None, 8, 8, 128) 512 res3c_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_20 (Activation) (None, 8, 8, 128) 0 bn3c_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res3c_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_20[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn3c_branch2b (BatchNormalizatio (None, 8, 8, 128) 512 res3c_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_21 (Activation) (None, 8, 8, 128) 0 bn3c_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res3c_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_21[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn3c_branch2c (BatchNormalizatio (None, 8, 8, 512) 2048 res3c_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_7 (Add) (None, 8, 8, 512) 0 bn3c_branch2c[0][0] \n",
- " activation_19[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_22 (Activation) (None, 8, 8, 512) 0 add_7[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res3d_branch2a (Conv2D) (None, 8, 8, 128) 65664 activation_22[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn3d_branch2a (BatchNormalizatio (None, 8, 8, 128) 512 res3d_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_23 (Activation) (None, 8, 8, 128) 0 bn3d_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res3d_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_23[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn3d_branch2b (BatchNormalizatio (None, 8, 8, 128) 512 res3d_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_24 (Activation) (None, 8, 8, 128) 0 bn3d_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res3d_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_24[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn3d_branch2c (BatchNormalizatio (None, 8, 8, 512) 2048 res3d_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_8 (Add) (None, 8, 8, 512) 0 bn3d_branch2c[0][0] \n",
- " activation_22[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_25 (Activation) (None, 8, 8, 512) 0 add_8[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4a_branch2a (Conv2D) (None, 4, 4, 256) 131328 activation_25[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4a_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4a_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_26 (Activation) (None, 4, 4, 256) 0 bn4a_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4a_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_26[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4a_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4a_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_27 (Activation) (None, 4, 4, 256) 0 bn4a_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4a_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_27[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4a_branch1 (Conv2D) (None, 4, 4, 1024) 525312 activation_25[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4a_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4a_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4a_branch1 (BatchNormalization (None, 4, 4, 1024) 4096 res4a_branch1[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_9 (Add) (None, 4, 4, 1024) 0 bn4a_branch2c[0][0] \n",
- " bn4a_branch1[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_28 (Activation) (None, 4, 4, 1024) 0 add_9[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4b_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_28[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4b_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4b_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_29 (Activation) (None, 4, 4, 256) 0 bn4b_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4b_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_29[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4b_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4b_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_30 (Activation) (None, 4, 4, 256) 0 bn4b_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4b_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_30[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4b_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4b_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_10 (Add) (None, 4, 4, 1024) 0 bn4b_branch2c[0][0] \n",
- " activation_28[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_31 (Activation) (None, 4, 4, 1024) 0 add_10[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4c_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_31[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4c_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4c_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_32 (Activation) (None, 4, 4, 256) 0 bn4c_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4c_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_32[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4c_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4c_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_33 (Activation) (None, 4, 4, 256) 0 bn4c_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4c_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_33[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4c_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4c_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_11 (Add) (None, 4, 4, 1024) 0 bn4c_branch2c[0][0] \n",
- " activation_31[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_34 (Activation) (None, 4, 4, 1024) 0 add_11[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4d_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_34[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4d_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4d_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_35 (Activation) (None, 4, 4, 256) 0 bn4d_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4d_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_35[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4d_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4d_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_36 (Activation) (None, 4, 4, 256) 0 bn4d_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4d_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_36[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4d_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4d_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_12 (Add) (None, 4, 4, 1024) 0 bn4d_branch2c[0][0] \n",
- " activation_34[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_37 (Activation) (None, 4, 4, 1024) 0 add_12[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4e_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_37[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4e_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4e_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_38 (Activation) (None, 4, 4, 256) 0 bn4e_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4e_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_38[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4e_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4e_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_39 (Activation) (None, 4, 4, 256) 0 bn4e_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4e_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_39[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4e_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4e_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_13 (Add) (None, 4, 4, 1024) 0 bn4e_branch2c[0][0] \n",
- " activation_37[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_40 (Activation) (None, 4, 4, 1024) 0 add_13[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4f_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_40[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4f_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4f_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_41 (Activation) (None, 4, 4, 256) 0 bn4f_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4f_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_41[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4f_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4f_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_42 (Activation) (None, 4, 4, 256) 0 bn4f_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res4f_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_42[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn4f_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4f_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_14 (Add) (None, 4, 4, 1024) 0 bn4f_branch2c[0][0] \n",
- " activation_40[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_43 (Activation) (None, 4, 4, 1024) 0 add_14[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res5a_branch2a (Conv2D) (None, 2, 2, 512) 524800 activation_43[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn5a_branch2a (BatchNormalizatio (None, 2, 2, 512) 2048 res5a_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_44 (Activation) (None, 2, 2, 512) 0 bn5a_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res5a_branch2b (Conv2D) (None, 2, 2, 512) 2359808 activation_44[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn5a_branch2b (BatchNormalizatio (None, 2, 2, 512) 2048 res5a_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_45 (Activation) (None, 2, 2, 512) 0 bn5a_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res5a_branch2c (Conv2D) (None, 2, 2, 2048) 1050624 activation_45[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res5a_branch1 (Conv2D) (None, 2, 2, 2048) 2099200 activation_43[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn5a_branch2c (BatchNormalizatio (None, 2, 2, 2048) 8192 res5a_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn5a_branch1 (BatchNormalization (None, 2, 2, 2048) 8192 res5a_branch1[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_15 (Add) (None, 2, 2, 2048) 0 bn5a_branch2c[0][0] \n",
- " bn5a_branch1[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_46 (Activation) (None, 2, 2, 2048) 0 add_15[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res5b_branch2a (Conv2D) (None, 2, 2, 512) 1049088 activation_46[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn5b_branch2a (BatchNormalizatio (None, 2, 2, 512) 2048 res5b_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_47 (Activation) (None, 2, 2, 512) 0 bn5b_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res5b_branch2b (Conv2D) (None, 2, 2, 512) 2359808 activation_47[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn5b_branch2b (BatchNormalizatio (None, 2, 2, 512) 2048 res5b_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_48 (Activation) (None, 2, 2, 512) 0 bn5b_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res5b_branch2c (Conv2D) (None, 2, 2, 2048) 1050624 activation_48[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn5b_branch2c (BatchNormalizatio (None, 2, 2, 2048) 8192 res5b_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_16 (Add) (None, 2, 2, 2048) 0 bn5b_branch2c[0][0] \n",
- " activation_46[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_49 (Activation) (None, 2, 2, 2048) 0 add_16[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res5c_branch2a (Conv2D) (None, 2, 2, 512) 1049088 activation_49[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn5c_branch2a (BatchNormalizatio (None, 2, 2, 512) 2048 res5c_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_50 (Activation) (None, 2, 2, 512) 0 bn5c_branch2a[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res5c_branch2b (Conv2D) (None, 2, 2, 512) 2359808 activation_50[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn5c_branch2b (BatchNormalizatio (None, 2, 2, 512) 2048 res5c_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_51 (Activation) (None, 2, 2, 512) 0 bn5c_branch2b[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "res5c_branch2c (Conv2D) (None, 2, 2, 2048) 1050624 activation_51[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "bn5c_branch2c (BatchNormalizatio (None, 2, 2, 2048) 8192 res5c_branch2c[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "add_17 (Add) (None, 2, 2, 2048) 0 bn5c_branch2c[0][0] \n",
- " activation_49[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "activation_52 (Activation) (None, 2, 2, 2048) 0 add_17[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "avg_pool (AveragePooling2D) (None, 1, 1, 2048) 0 activation_52[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "flatten_1 (Flatten) (None, 2048) 0 avg_pool[0][0] \n",
- "____________________________________________________________________________________________________\n",
- "fc6 (Dense) (None, 6) 12294 flatten_1[0][0] \n",
- "====================================================================================================\n",
- "Total params: 23,600,006\n",
- "Trainable params: 23,546,886\n",
- "Non-trainable params: 53,120\n",
- "____________________________________________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model.summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, run the code below to visualize your ResNet50. You can also download a .png picture of your model by going to \"File -> Open...-> model.png\"."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plot_model(model, to_file='model.png')\n",
- "SVG(model_to_dot(model).create(prog='dot', format='svg'))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "**What you should remember:**\n",
- "- Very deep \"plain\" networks don't work in practice because they are hard to train due to vanishing gradients. \n",
- "- The skip-connections help to address the Vanishing Gradient problem. They also make it easy for a ResNet block to learn an identity function. \n",
- "- There are two main type of blocks: The identity block and the convolutional block. \n",
- "- Very deep Residual Networks are built by stacking these blocks together."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### References \n",
- "\n",
- "This notebook presents the ResNet algorithm due to He et al. (2015). The implementation here also took significant inspiration and follows the structure given in the github repository of Francois Chollet: \n",
- "\n",
- "- Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun - [Deep Residual Learning for Image Recognition (2015)](https://arxiv.org/abs/1512.03385)\n",
- "- Francois Chollet's github repository: https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "coursera": {
- "course_slug": "convolutional-neural-networks",
- "graded_item_id": "OEpi5",
- "launcher_item_id": "jK9EQ"
- },
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.0"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Residual+Networks+-+v2.py b/Residual+Networks+-+v2.py
deleted file mode 100644
index 8b36c82..0000000
--- a/Residual+Networks+-+v2.py
+++ /dev/null
@@ -1,610 +0,0 @@
-
-# coding: utf-8
-
-# # Residual Networks
-#
-# Welcome to the second assignment of this week! You will learn how to build very deep convolutional networks, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, they are hard to train. Residual Networks, introduced by [He et al.](https://arxiv.org/pdf/1512.03385.pdf), allow you to train much deeper networks than were previously practically feasible.
-#
-# **In this assignment, you will:**
-# - Implement the basic building blocks of ResNets.
-# - Put together these building blocks to implement and train a state-of-the-art neural network for image classification.
-#
-# This assignment will be done in Keras.
-#
-# Before jumping into the problem, let's run the cell below to load the required packages.
-
-# In[16]:
-
-import numpy as np
-from keras import layers
-from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D
-from keras.models import Model, load_model
-from keras.preprocessing import image
-from keras.utils import layer_utils
-from keras.utils.data_utils import get_file
-from keras.applications.imagenet_utils import preprocess_input
-import pydot
-from IPython.display import SVG
-from keras.utils.vis_utils import model_to_dot
-from keras.utils import plot_model
-from resnets_utils import *
-from keras.initializers import glorot_uniform
-import scipy.misc
-from matplotlib.pyplot import imshow
-get_ipython().magic('matplotlib inline')
-
-import keras.backend as K
-K.set_image_data_format('channels_last')
-K.set_learning_phase(1)
-
-
-# ## 1 - The problem of very deep neural networks
-#
-# Last week, you built your first convolutional neural network. In recent years, neural networks have become deeper, with state-of-the-art networks going from just a few layers (e.g., AlexNet) to over a hundred layers.
-#
-# The main benefit of a very deep network is that it can represent very complex functions. It can also learn features at many different levels of abstraction, from edges (at the lower layers) to very complex features (at the deeper layers). However, using a deeper network doesn't always help. A huge barrier to training them is vanishing gradients: very deep networks often have a gradient signal that goes to zero quickly, thus making gradient descent unbearably slow. More specifically, during gradient descent, as you backprop from the final layer back to the first layer, you are multiplying by the weight matrix on each step, and thus the gradient can decrease exponentially quickly to zero (or, in rare cases, grow exponentially quickly and "explode" to take very large values).
-#
-# During training, you might therefore see the magnitude (or norm) of the gradient for the earlier layers descrease to zero very rapidly as training proceeds:
-
-# 
-# **Figure 1** : **Vanishing gradient**
The speed of learning decreases very rapidly for the early layers as the network trains 
-# **Figure 2** : A ResNet block showing a **skip-connection**
-# **Figure 3** : **Identity block.** Skip connection "skips over" 2 layers. 
-# **Figure 4** : **Identity block.** Skip connection "skips over" 3 layers.
-#
-
-# ## 2.2 - The convolutional block
-#
-# You've implemented the ResNet identity block. Next, the ResNet "convolutional block" is the other type of block. You can use this type of block when the input and output dimensions don't match up. The difference with the identity block is that there is a CONV2D layer in the shortcut path:
-#
-# 
-# **Figure 4** : **Convolutional block**
-#
-
-# ## 3 - Building your first ResNet model (50 layers)
-#
-# You now have the necessary blocks to build a very deep ResNet. The following figure describes in detail the architecture of this neural network. "ID BLOCK" in the diagram stands for "Identity block," and "ID BLOCK x3" means you should stack 3 identity blocks together.
-#
-# 
-# **Figure 5** : **ResNet-50 model** 
-# **Figure 6** : **SIGNS dataset**
-#
-
-# Let's see how this model (trained on only two epochs) performs on the test set.
-
-# In[31]:
-
-preds = model.evaluate(X_test, Y_test)
-print ("Loss = " + str(preds[0]))
-print ("Test Accuracy = " + str(preds[1]))
-
-
-# **Expected Output**:
-#
-#
-#
-
-# For the purpose of this assignment, we've asked you to train the model only for two epochs. You can see that it achieves poor performances. Please go ahead and submit your assignment; to check correctness, the online grader will run your code only for a small number of epochs as well.
-
-# After you have finished this official (graded) part of this assignment, you can also optionally train the ResNet for more iterations, if you want. We get a lot better performance when we train for ~20 epochs, but this will take more than an hour when training on a CPU.
-#
-# Using a GPU, we've trained our own ResNet50 model's weights on the SIGNS dataset. You can load and run our trained model on the test set in the cells below. It may take ≈1min to load the model.
-
-# In[32]:
-
-model = load_model('ResNet50.h5')
-
-
-# In[33]:
-
-preds = model.evaluate(X_test, Y_test)
-print ("Loss = " + str(preds[0]))
-print ("Test Accuracy = " + str(preds[1]))
-
-
-# ResNet50 is a powerful model for image classification when it is trained for an adequate number of iterations. We hope you can use what you've learnt and apply it to your own classification problem to perform state-of-the-art accuracy.
-#
-# Congratulations on finishing this assignment! You've now implemented a state-of-the-art image classification system!
-
-# ## 4 - Test on your own image (Optional/Ungraded)
-
-# If you wish, you can also take a picture of your own hand and see the output of the model. To do this:
-# 1. Click on "File" in the upper bar of this notebook, then click "Open" to go on your Coursera Hub.
-# 2. Add your image to this Jupyter Notebook's directory, in the "images" folder
-# 3. Write your image's name in the following code
-# 4. Run the code and check if the algorithm is right!
-
-# In[34]:
-
-img_path = 'images/my_image.jpg'
-img = image.load_img(img_path, target_size=(64, 64))
-x = image.img_to_array(img)
-x = np.expand_dims(x, axis=0)
-x = preprocess_input(x)
-print('Input image shape:', x.shape)
-my_image = scipy.misc.imread(img_path)
-imshow(my_image)
-print("class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = ")
-print(model.predict(x))
-
-
-# You can also print a summary of your model by running the following code.
-
-# In[35]:
-
-model.summary()
-
-
-# Finally, run the code below to visualize your ResNet50. You can also download a .png picture of your model by going to "File -> Open...-> model.png".
-
-# In[36]:
-
-plot_model(model, to_file='model.png')
-SVG(model_to_dot(model).create(prog='dot', format='svg'))
-
-
-#
-# **What you should remember:**
-# - Very deep "plain" networks don't work in practice because they are hard to train due to vanishing gradients.
-# - The skip-connections help to address the Vanishing Gradient problem. They also make it easy for a ResNet block to learn an identity function.
-# - There are two main type of blocks: The identity block and the convolutional block.
-# - Very deep Residual Networks are built by stacking these blocks together.
-
-# ### References
-#
-# This notebook presents the ResNet algorithm due to He et al. (2015). The implementation here also took significant inspiration and follows the structure given in the github repository of Francois Chollet:
-#
-# - Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun - [Deep Residual Learning for Image Recognition (2015)](https://arxiv.org/abs/1512.03385)
-# - Francois Chollet's github repository: https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py
-#
-
-# In[ ]:
-
-
-
-
-# In[ ]:
-
-
-
diff --git a/Roboflow_Train_YOLOv5.ipynb b/Roboflow_Train_YOLOv5.ipynb
deleted file mode 100644
index dfe5bb2..0000000
--- a/Roboflow_Train_YOLOv5.ipynb
+++ /dev/null
@@ -1,1197 +0,0 @@
-{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
- "colab": {
- "provenance": [],
- "collapsed_sections": [],
- "include_colab_link": true
- },
- "kernelspec": {
- "name": "python3",
- "display_name": "Python 3"
- },
- "accelerator": "GPU"
- },
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "view-in-github",
- "colab_type": "text"
- },
- "source": [
- "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "GD9gUQpaBxNa"
- },
- "source": [
- "# How to Train YOLOv5 on Custom Objects\n",
- "\n",
- "This tutorial is based on the [YOLOv5 repository](https://github.com/ultralytics/yolov5) by [Ultralytics](https://www.ultralytics.com/). This notebook shows training on **your own custom objects**. Many thanks to Ultralytics for putting this repository together - we hope that in combination with clean data management tools at Roboflow, this technologoy will become easily accessible to any developer wishing to use computer vision in their projects.\n",
- "\n",
- "### Accompanying Blog Post\n",
- "\n",
- "We recommend that you follow along in this notebook while reading the blog post on [how to train YOLOv5](https://blog.roboflow.ai/how-to-train-yolov5-on-a-custom-dataset/), concurrently.\n",
- "\n",
- "### Steps Covered in this Tutorial\n",
- "\n",
- "In this tutorial, we will walk through the steps required to train YOLOv5 on your custom objects. We use a [public blood cell detection dataset](https://public.roboflow.ai/object-detection/bccd), which is open source and free to use. You can also use this notebook on your own data.\n",
- "\n",
- "To train our detector we take the following steps:\n",
- "\n",
- "* Install YOLOv5 dependencies\n",
- "* Download custom YOLOv5 object detection data\n",
- "* Write our YOLOv5 Training configuration\n",
- "* Run YOLOv5 training\n",
- "* Evaluate YOLOv5 performance\n",
- "* Visualize YOLOv5 training data\n",
- "* Run YOLOv5 inference on test images\n",
- "* Export saved YOLOv5 weights for future inference\n",
- "\n",
- "\n",
- "\n",
- "### **About**\n",
- "\n",
- "[Roboflow](https://roboflow.com) enables teams to deploy custom computer vision models quickly and accurately. Convert data from to annotation format, assess dataset health, preprocess, augment, and more. It's free for your first 1000 source images.\n",
- "\n",
- "**Looking for a vision model available via API without hassle? Try Roboflow Train.**\n",
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7mGmQbAO5pQb"
- },
- "source": [
- "#Install Dependencies\n",
- "\n",
- "_(Remember to choose GPU in Runtime if not already selected. Runtime --> Change Runtime Type --> Hardware accelerator --> GPU)_"
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Ie5uLDH4uzAp",
- "outputId": "34ec3e68-40f6-4ce4-8dc5-cca7f824f451"
- },
- "source": [
- "# clone YOLOv5 repository\n",
- "!git clone https://github.com/ultralytics/yolov5 # clone repo\n",
- "%cd yolov5\n",
- "!git reset --hard fbe67e465375231474a2ad80a4389efc77ecff99"
- ],
- "execution_count": 1,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Cloning into 'yolov5'...\n",
- "remote: Enumerating objects: 14411, done.\u001b[K\n",
- "remote: Counting objects: 100% (32/32), done.\u001b[K\n",
- "remote: Compressing objects: 100% (22/22), done.\u001b[K\n",
- "remote: Total 14411 (delta 12), reused 22 (delta 10), pack-reused 14379\u001b[K\n",
- "Receiving objects: 100% (14411/14411), 13.37 MiB | 31.18 MiB/s, done.\n",
- "Resolving deltas: 100% (9962/9962), done.\n",
- "/content/yolov5\n",
- "HEAD is now at fbe67e4 Fix `OMP_NUM_THREADS=1` for macOS (#8624)\n"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "wbvMlHd_QwMG",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "484dd5f5-a33e-4e56-ac20-10dc71b36009"
- },
- "source": [
- "# install dependencies as necessary\n",
- "!pip install -qr requirements.txt # install dependencies (ignore errors)\n",
- "import torch\n",
- "\n",
- "from IPython.display import Image, clear_output # to display images\n",
- "from utils.downloads import attempt_download # to download models/datasets\n",
- "\n",
- "# clear_output()\n",
- "print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
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- "\u001b[?25hSetup complete. Using torch 1.12.0+cu113 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', major=6, minor=0, total_memory=16280MB, multi_processor_count=56)\n"
- ]
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "SDIhrBF0sPaM"
- },
- "source": [
- "# Download Correctly Formatted Custom Dataset \n",
- "\n",
- "We'll download our dataset from Roboflow. Use the \"**YOLOv5 PyTorch**\" export format. Note that the Ultralytics implementation calls for a YAML file defining where your training and test data is. The Roboflow export also writes this format for us.\n",
- "\n",
- "To get your data into Roboflow, follow the [Getting Started Guide](https://blog.roboflow.ai/getting-started-with-roboflow/)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "vDeebwqS9JbZ"
- },
- "source": [
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "Knxi2ncxWffW",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "d4d0155d-63c9-4c2c-a63e-a553ecbef611"
- },
- "source": [
- "#follow the link below to get your download code from from Roboflow\n",
- "!pip install -q roboflow\n",
- "from roboflow import Roboflow\n",
- "rf = Roboflow(model_format=\"yolov5\", notebook=\"roboflow-yolov5\")"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "\u001b[K |████████████████████████████████| 178 kB 7.6 MB/s \n",
- "\u001b[K |████████████████████████████████| 21.6 MB 1.2 MB/s \n",
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- "\u001b[K |████████████████████████████████| 1.1 MB 68.0 MB/s \n",
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- "\u001b[K |████████████████████████████████| 62 kB 1.7 MB/s \n",
- "\u001b[?25h Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
- "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
- "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.28.1 which is incompatible.\n",
- "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
- "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
- "upload and label your dataset, and get an API KEY here: https://app.roboflow.com/?model=yolov5&ref=roboflow-yolov5\n"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "Ug_PhK1oqwQA",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "b417162b-c116-416c-88e8-96b2563d1ee1"
- },
- "source": [
- "%cd /content/yolov5\n",
- "#after following the link above, recieve python code with these fields filled in\n",
- "#from roboflow import Roboflow\n",
- "#rf = Roboflow(api_key=\"YOUR API KEY HERE\")\n",
- "#project = rf.workspace().project(\"YOUR PROJECT\")\n",
- "#dataset = project.version(\"YOUR VERSION\").download(\"yolov5\")"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "/content/yolov5\n",
- "loading Roboflow workspace...\n",
- "loading Roboflow project...\n",
- "Downloading Dataset Version Zip in American-Mushrooms-1 to yolov5pytorch: 100% [3866359 / 3866359] bytes\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "Extracting Dataset Version Zip to American-Mushrooms-1 in yolov5pytorch:: 100%|██████████| 278/278 [00:00<00:00, 1111.85it/s]\n"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "ZZ3DmmGQztJj",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "895f8313-8441-4c2d-9c87-01cc89f22acb"
- },
- "source": [
- "# this is the YAML file Roboflow wrote for us that we're loading into this notebook with our data\n",
- "%cat {dataset.location}/data.yaml"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "names:\n",
- "- CoW\n",
- "- chanterelle\n",
- "nc: 2\n",
- "train: American-Mushrooms-1/train/images\n",
- "val: American-Mushrooms-1/valid/images\n"
- ]
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "UwJx-2NHsYxT"
- },
- "source": [
- "# Define Model Configuration and Architecture\n",
- "\n",
- "We will write a yaml script that defines the parameters for our model like the number of classes, anchors, and each layer.\n",
- "\n",
- "You do not need to edit these cells, but you may."
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "dOPn9wjOAwwK"
- },
- "source": [
- "# define number of classes based on YAML\n",
- "import yaml\n",
- "with open(dataset.location + \"/data.yaml\", 'r') as stream:\n",
- " num_classes = str(yaml.safe_load(stream)['nc'])"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "1Rvt5wilnDyX",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "8b8386bc-0e05-49a8-c1e0-48a836a0b399"
- },
- "source": [
- "#this is the model configuration we will use for our tutorial \n",
- "%cat /content/yolov5/models/yolov5s.yaml"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "# parameters\n",
- "nc: 80 # number of classes\n",
- "depth_multiple: 0.33 # model depth multiple\n",
- "width_multiple: 0.50 # layer channel multiple\n",
- "\n",
- "# anchors\n",
- "anchors:\n",
- " - [10,13, 16,30, 33,23] # P3/8\n",
- " - [30,61, 62,45, 59,119] # P4/16\n",
- " - [116,90, 156,198, 373,326] # P5/32\n",
- "\n",
- "# YOLOv5 backbone\n",
- "backbone:\n",
- " # [from, number, module, args]\n",
- " [[-1, 1, Focus, [64, 3]], # 0-P1/2\n",
- " [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n",
- " [-1, 3, C3, [128]],\n",
- " [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n",
- " [-1, 9, C3, [256]],\n",
- " [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n",
- " [-1, 9, C3, [512]],\n",
- " [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n",
- " [-1, 1, SPP, [1024, [5, 9, 13]]],\n",
- " [-1, 3, C3, [1024, False]], # 9\n",
- " ]\n",
- "\n",
- "# YOLOv5 head\n",
- "head:\n",
- " [[-1, 1, Conv, [512, 1, 1]],\n",
- " [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
- " [[-1, 6], 1, Concat, [1]], # cat backbone P4\n",
- " [-1, 3, C3, [512, False]], # 13\n",
- "\n",
- " [-1, 1, Conv, [256, 1, 1]],\n",
- " [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
- " [[-1, 4], 1, Concat, [1]], # cat backbone P3\n",
- " [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n",
- "\n",
- " [-1, 1, Conv, [256, 3, 2]],\n",
- " [[-1, 14], 1, Concat, [1]], # cat head P4\n",
- " [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n",
- "\n",
- " [-1, 1, Conv, [512, 3, 2]],\n",
- " [[-1, 10], 1, Concat, [1]], # cat head P5\n",
- " [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n",
- "\n",
- " [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n",
- " ]\n"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "t14hhyqdmw6O"
- },
- "source": [
- "#customize iPython writefile so we can write variables\n",
- "from IPython.core.magic import register_line_cell_magic\n",
- "\n",
- "@register_line_cell_magic\n",
- "def writetemplate(line, cell):\n",
- " with open(line, 'w') as f:\n",
- " f.write(cell.format(**globals()))"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "uDxebz13RdRA"
- },
- "source": [
- "%%writetemplate /content/yolov5/models/custom_yolov5s.yaml\n",
- "\n",
- "# parameters\n",
- "nc: {num_classes} # number of classes\n",
- "depth_multiple: 0.33 # model depth multiple\n",
- "width_multiple: 0.50 # layer channel multiple\n",
- "\n",
- "# anchors\n",
- "anchors:\n",
- " - [10,13, 16,30, 33,23] # P3/8\n",
- " - [30,61, 62,45, 59,119] # P4/16\n",
- " - [116,90, 156,198, 373,326] # P5/32\n",
- "\n",
- "# YOLOv5 backbone\n",
- "backbone:\n",
- " # [from, number, module, args]\n",
- " [[-1, 1, Focus, [64, 3]], # 0-P1/2\n",
- " [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n",
- " [-1, 3, BottleneckCSP, [128]],\n",
- " [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n",
- " [-1, 9, BottleneckCSP, [256]],\n",
- " [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n",
- " [-1, 9, BottleneckCSP, [512]],\n",
- " [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n",
- " [-1, 1, SPP, [1024, [5, 9, 13]]],\n",
- " [-1, 3, BottleneckCSP, [1024, False]], # 9\n",
- " ]\n",
- "\n",
- "# YOLOv5 head\n",
- "head:\n",
- " [[-1, 1, Conv, [512, 1, 1]],\n",
- " [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
- " [[-1, 6], 1, Concat, [1]], # cat backbone P4\n",
- " [-1, 3, BottleneckCSP, [512, False]], # 13\n",
- "\n",
- " [-1, 1, Conv, [256, 1, 1]],\n",
- " [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
- " [[-1, 4], 1, Concat, [1]], # cat backbone P3\n",
- " [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small)\n",
- "\n",
- " [-1, 1, Conv, [256, 3, 2]],\n",
- " [[-1, 14], 1, Concat, [1]], # cat head P4\n",
- " [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium)\n",
- "\n",
- " [-1, 1, Conv, [512, 3, 2]],\n",
- " [[-1, 10], 1, Concat, [1]], # cat head P5\n",
- " [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large)\n",
- "\n",
- " [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n",
- " ]"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "VUOiNLtMP5aG"
- },
- "source": [
- "# Train Custom YOLOv5 Detector\n",
- "\n",
- "### Next, we'll fire off training!\n",
- "\n",
- "\n",
- "Here, we are able to pass a number of arguments:\n",
- "- **img:** define input image size\n",
- "- **batch:** determine batch size\n",
- "- **epochs:** define the number of training epochs. (Note: often, 3000+ are common here!)\n",
- "- **data:** set the path to our yaml file\n",
- "- **cfg:** specify our model configuration\n",
- "- **weights:** specify a custom path to weights. (Note: you can download weights from the Ultralytics Google Drive [folder](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J))\n",
- "- **name:** result names\n",
- "- **nosave:** only save the final checkpoint\n",
- "- **cache:** cache images for faster training"
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "1NcFxRcFdJ_O",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "84614320-0d9c-40c2-dbef-a2c121ab667b"
- },
- "source": [
- "# train yolov5s on custom data for 100 epochs\n",
- "# time its performance\n",
- "%%time\n",
- "%cd /content/yolov5/\n",
- "!python train.py --img 416 --batch 16 --epochs 100 --data {dataset.location}/data.yaml --cfg ./models/custom_yolov5s.yaml --weights '' --name yolov5s_results --cache"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "/content/yolov5\n",
- "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ WARNING: code is out of date by 577 commits. Use 'git pull' to update or 'git clone https://github.com/ultralytics/yolov5' to download latest.\n",
- "YOLOv5 v4.0-126-g886f1c0 torch 1.9.0+cu111 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)\n",
- "\n",
- "Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='./models/custom_yolov5s.yaml', data='/content/yolov5/American-Mushrooms-1/data.yaml', device='', entity=None, epochs=100, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[416, 416], linear_lr=False, local_rank=-1, log_artifacts=False, log_imgs=16, multi_scale=False, name='yolov5s_results', noautoanchor=False, nosave=False, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/yolov5s_results', single_cls=False, sync_bn=False, total_batch_size=16, weights='', workers=8, world_size=1)\n",
- "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)\n",
- "Start Tensorboard with \"tensorboard --logdir runs/train\", view at http://localhost:6006/\n",
- "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0\n",
- "\n",
- " from n params module arguments \n",
- " 0 -1 1 3520 models.common.Focus [3, 32, 3] \n",
- " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n",
- " 2 -1 1 19904 models.common.BottleneckCSP [64, 64, 1] \n",
- " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n",
- " 4 -1 1 161152 models.common.BottleneckCSP [128, 128, 3] \n",
- " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
- " 6 -1 1 641792 models.common.BottleneckCSP [256, 256, 3] \n",
- " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
- " 8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]] \n",
- " 9 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n",
- " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
- " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
- " 12 [-1, 6] 1 0 models.common.Concat [1] \n",
- " 13 -1 1 378624 models.common.BottleneckCSP [512, 256, 1, False] \n",
- " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n",
- " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
- " 16 [-1, 4] 1 0 models.common.Concat [1] \n",
- " 17 -1 1 95104 models.common.BottleneckCSP [256, 128, 1, False] \n",
- " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n",
- " 19 [-1, 14] 1 0 models.common.Concat [1] \n",
- " 20 -1 1 313088 models.common.BottleneckCSP [256, 256, 1, False] \n",
- " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n",
- " 22 [-1, 10] 1 0 models.common.Concat [1] \n",
- " 23 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n",
- " 24 [17, 20, 23] 1 18879 models.yolo.Detect [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
- "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n",
- " return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n",
- "Model Summary: 283 layers, 7257791 parameters, 7257791 gradients, 16.8 GFLOPS\n",
- "\n",
- "Scaled weight_decay = 0.0005\n",
- "Optimizer groups: 62 .bias, 70 conv.weight, 59 other\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mScanning 'American-Mushrooms-1/train/labels' for images and labels... 123 found, 0 missing, 0 empty, 0 corrupted: 100% 123/123 [00:00<00:00, 2407.13it/s]\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: American-Mushrooms-1/train/labels.cache\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB): 100% 123/123 [00:00<00:00, 511.60it/s]\n",
- "\u001b[34m\u001b[1mval: \u001b[0mScanning 'American-Mushrooms-1/valid/labels' for images and labels... 5 found, 0 missing, 0 empty, 0 corrupted: 100% 5/5 [00:00<00:00, 1242.98it/s]\n",
- "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: American-Mushrooms-1/valid/labels.cache\n",
- "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.0GB): 100% 5/5 [00:00<00:00, 321.12it/s]\n",
- "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
- "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
- "Plotting labels... \n",
- "\n",
- "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 3.48, Best Possible Recall (BPR) = 1.0000\n",
- "Image sizes 416 train, 416 test\n",
- "Using 2 dataloader workers\n",
- "Logging results to runs/train/yolov5s_results\n",
- "Starting training for 100 epochs...\n",
- "\n",
- " Epoch gpu_mem box obj cls total targets img_size\n",
- " 0/99 1.41G 0.1144 0.02597 0.02773 0.1681 49 416: 100% 8/8 [00:03<00:00, 2.55it/s]\n",
- " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 4.55it/s]\n",
- " all 5 14 0.00492 0.478 0.00691 0.00154\n",
- "\n",
- " Epoch gpu_mem box obj cls total targets img_size\n",
- " 0% 0/8 [00:00
-# The goal keeper kicks the ball in the air, the players of each team are fighting to hit the ball with their head
| -# **cost** -# | -#-# 1.78648594516 -# | -# -#
| -# **dW1** -# | -#-# [[-0.25604646 0.12298827 -0.28297129] -# [-0.17706303 0.34536094 -0.4410571 ]] -# | -#
| -# **dW2** -# | -#-# [[ 0.79276486 0.85133918] -# [-0.0957219 -0.01720463] -# [-0.13100772 -0.03750433]] -# | -#
| -# **dW3** -# | -#-# [[-1.77691347 -0.11832879 -0.09397446]] -# | -#
-#
At each iteration, you shut down (= set to zero) each neuron of a layer with probability $1 - keep\_prob$ or keep it with probability $keep\_prob$ (50% here). The dropped neurons don't contribute to the training in both the forward and backward propagations of the iteration.
$1^{st}$ layer: we shut down on average 40% of the neurons. $3^{rd}$ layer: we shut down on average 20% of the neurons.
| -# **A3** -# | -#-# [[ 0.36974721 0.00305176 0.04565099 0.49683389 0.36974721]] -# | -# -#
| -# **dA1** -# | -#-# [[ 0.36544439 0. -0.00188233 0. -0.17408748] -# [ 0.65515713 0. -0.00337459 0. -0. ]] -# | -# -#
| -# **dA2** -# | -#-# [[ 0.58180856 0. -0.00299679 0. -0.27715731] -# [ 0. 0.53159854 -0. 0.53159854 -0.34089673] -# [ 0. 0. -0.00292733 0. -0. ]] -# | -# -#
| -# **model** -# | -#-# **train accuracy** -# | -#-# **test accuracy** -# | -# -#-# 3-layer NN without regularization -# | -#-# 95% -# | -#-# 91.5% -# | -#
| -# 3-layer NN with L2-regularization -# | -#-# 94% -# | -#-# 93% -# | -#
| -# 3-layer NN with dropout -# | -#-# 93% -# | -#-# 95% -# | -#
\n",
- "The speed of learning decreases very rapidly for the early layers as the network trains
\n",
- "\n",
- "\n",
- "| \n", - " **out**\n", - " | \n", - "\n", - " [ 0.94822985 0. 1.16101444 2.747859 0. 1.36677003]\n", - " | \n", - "
\n",
- "| \n", - " **out**\n", - " | \n", - "\n", - " [ 0.09018463 1.23489773 0.46822017 0.0367176 0. 0.65516603]\n", - " | \n", - "
\n",
- "\n",
- "| \n", - " ** Epoch 1/2**\n", - " | \n", - "\n", - " loss: between 1 and 5, acc: between 0.2 and 0.5, although your results can be different from ours.\n", - " | \n", - "
| \n", - " ** Epoch 2/2**\n", - " | \n", - "\n", - " loss: between 1 and 5, acc: between 0.2 and 0.5, you should see your loss decreasing and the accuracy increasing.\n", - " | \n", - "
| \n", - " **Test Accuracy**\n", - " | \n", - "\n", - " between 0.16 and 0.25\n", - " | \n", - "
-# The speed of learning decreases very rapidly for the early layers as the network trains
-#
-#
-# | -# **out** -# | -#-# [ 0.94822985 0. 1.16101444 2.747859 0. 1.36677003] -# | -#
-# | -# **out** -# | -#-# [ 0.09018463 1.23489773 0.46822017 0.0367176 0. 0.65516603] -# | -#
-#
-# | -# ** Epoch 1/2** -# | -#-# loss: between 1 and 5, acc: between 0.2 and 0.5, although your results can be different from ours. -# | -#
| -# ** Epoch 2/2** -# | -#-# loss: between 1 and 5, acc: between 0.2 and 0.5, you should see your loss decreasing and the accuracy increasing. -# | -#
| -# **Test Accuracy** -# | -#-# between 0.16 and 0.25 -# | -#
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "GD9gUQpaBxNa"
- },
- "source": [
- "# How to Train YOLOv5 on Custom Objects\n",
- "\n",
- "This tutorial is based on the [YOLOv5 repository](https://github.com/ultralytics/yolov5) by [Ultralytics](https://www.ultralytics.com/). This notebook shows training on **your own custom objects**. Many thanks to Ultralytics for putting this repository together - we hope that in combination with clean data management tools at Roboflow, this technologoy will become easily accessible to any developer wishing to use computer vision in their projects.\n",
- "\n",
- "### Accompanying Blog Post\n",
- "\n",
- "We recommend that you follow along in this notebook while reading the blog post on [how to train YOLOv5](https://blog.roboflow.ai/how-to-train-yolov5-on-a-custom-dataset/), concurrently.\n",
- "\n",
- "### Steps Covered in this Tutorial\n",
- "\n",
- "In this tutorial, we will walk through the steps required to train YOLOv5 on your custom objects. We use a [public blood cell detection dataset](https://public.roboflow.ai/object-detection/bccd), which is open source and free to use. You can also use this notebook on your own data.\n",
- "\n",
- "To train our detector we take the following steps:\n",
- "\n",
- "* Install YOLOv5 dependencies\n",
- "* Download custom YOLOv5 object detection data\n",
- "* Write our YOLOv5 Training configuration\n",
- "* Run YOLOv5 training\n",
- "* Evaluate YOLOv5 performance\n",
- "* Visualize YOLOv5 training data\n",
- "* Run YOLOv5 inference on test images\n",
- "* Export saved YOLOv5 weights for future inference\n",
- "\n",
- "\n",
- "\n",
- "### **About**\n",
- "\n",
- "[Roboflow](https://roboflow.com) enables teams to deploy custom computer vision models quickly and accurately. Convert data from to annotation format, assess dataset health, preprocess, augment, and more. It's free for your first 1000 source images.\n",
- "\n",
- "**Looking for a vision model available via API without hassle? Try Roboflow Train.**\n",
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7mGmQbAO5pQb"
- },
- "source": [
- "#Install Dependencies\n",
- "\n",
- "_(Remember to choose GPU in Runtime if not already selected. Runtime --> Change Runtime Type --> Hardware accelerator --> GPU)_"
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Ie5uLDH4uzAp",
- "outputId": "34ec3e68-40f6-4ce4-8dc5-cca7f824f451"
- },
- "source": [
- "# clone YOLOv5 repository\n",
- "!git clone https://github.com/ultralytics/yolov5 # clone repo\n",
- "%cd yolov5\n",
- "!git reset --hard fbe67e465375231474a2ad80a4389efc77ecff99"
- ],
- "execution_count": 1,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Cloning into 'yolov5'...\n",
- "remote: Enumerating objects: 14411, done.\u001b[K\n",
- "remote: Counting objects: 100% (32/32), done.\u001b[K\n",
- "remote: Compressing objects: 100% (22/22), done.\u001b[K\n",
- "remote: Total 14411 (delta 12), reused 22 (delta 10), pack-reused 14379\u001b[K\n",
- "Receiving objects: 100% (14411/14411), 13.37 MiB | 31.18 MiB/s, done.\n",
- "Resolving deltas: 100% (9962/9962), done.\n",
- "/content/yolov5\n",
- "HEAD is now at fbe67e4 Fix `OMP_NUM_THREADS=1` for macOS (#8624)\n"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "wbvMlHd_QwMG",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "484dd5f5-a33e-4e56-ac20-10dc71b36009"
- },
- "source": [
- "# install dependencies as necessary\n",
- "!pip install -qr requirements.txt # install dependencies (ignore errors)\n",
- "import torch\n",
- "\n",
- "from IPython.display import Image, clear_output # to display images\n",
- "from utils.downloads import attempt_download # to download models/datasets\n",
- "\n",
- "# clear_output()\n",
- "print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
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- "\u001b[?25hSetup complete. Using torch 1.12.0+cu113 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', major=6, minor=0, total_memory=16280MB, multi_processor_count=56)\n"
- ]
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "SDIhrBF0sPaM"
- },
- "source": [
- "# Download Correctly Formatted Custom Dataset \n",
- "\n",
- "We'll download our dataset from Roboflow. Use the \"**YOLOv5 PyTorch**\" export format. Note that the Ultralytics implementation calls for a YAML file defining where your training and test data is. The Roboflow export also writes this format for us.\n",
- "\n",
- "To get your data into Roboflow, follow the [Getting Started Guide](https://blog.roboflow.ai/getting-started-with-roboflow/)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "vDeebwqS9JbZ"
- },
- "source": [
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "Knxi2ncxWffW",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "d4d0155d-63c9-4c2c-a63e-a553ecbef611"
- },
- "source": [
- "#follow the link below to get your download code from from Roboflow\n",
- "!pip install -q roboflow\n",
- "from roboflow import Roboflow\n",
- "rf = Roboflow(model_format=\"yolov5\", notebook=\"roboflow-yolov5\")"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "\u001b[K |████████████████████████████████| 178 kB 7.6 MB/s \n",
- "\u001b[K |████████████████████████████████| 21.6 MB 1.2 MB/s \n",
- "\u001b[K |████████████████████████████████| 54 kB 2.8 MB/s \n",
- "\u001b[K |████████████████████████████████| 67 kB 6.4 MB/s \n",
- "\u001b[K |████████████████████████████████| 1.1 MB 68.0 MB/s \n",
- "\u001b[K |████████████████████████████████| 145 kB 72.2 MB/s \n",
- "\u001b[K |████████████████████████████████| 4.3 MB 55.6 MB/s \n",
- "\u001b[K |████████████████████████████████| 138 kB 68.1 MB/s \n",
- "\u001b[K |████████████████████████████████| 62 kB 1.7 MB/s \n",
- "\u001b[?25h Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
- "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
- "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.28.1 which is incompatible.\n",
- "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
- "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
- "upload and label your dataset, and get an API KEY here: https://app.roboflow.com/?model=yolov5&ref=roboflow-yolov5\n"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "Ug_PhK1oqwQA",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "b417162b-c116-416c-88e8-96b2563d1ee1"
- },
- "source": [
- "%cd /content/yolov5\n",
- "#after following the link above, recieve python code with these fields filled in\n",
- "#from roboflow import Roboflow\n",
- "#rf = Roboflow(api_key=\"YOUR API KEY HERE\")\n",
- "#project = rf.workspace().project(\"YOUR PROJECT\")\n",
- "#dataset = project.version(\"YOUR VERSION\").download(\"yolov5\")"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "/content/yolov5\n",
- "loading Roboflow workspace...\n",
- "loading Roboflow project...\n",
- "Downloading Dataset Version Zip in American-Mushrooms-1 to yolov5pytorch: 100% [3866359 / 3866359] bytes\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "Extracting Dataset Version Zip to American-Mushrooms-1 in yolov5pytorch:: 100%|██████████| 278/278 [00:00<00:00, 1111.85it/s]\n"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "ZZ3DmmGQztJj",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "895f8313-8441-4c2d-9c87-01cc89f22acb"
- },
- "source": [
- "# this is the YAML file Roboflow wrote for us that we're loading into this notebook with our data\n",
- "%cat {dataset.location}/data.yaml"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "names:\n",
- "- CoW\n",
- "- chanterelle\n",
- "nc: 2\n",
- "train: American-Mushrooms-1/train/images\n",
- "val: American-Mushrooms-1/valid/images\n"
- ]
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "UwJx-2NHsYxT"
- },
- "source": [
- "# Define Model Configuration and Architecture\n",
- "\n",
- "We will write a yaml script that defines the parameters for our model like the number of classes, anchors, and each layer.\n",
- "\n",
- "You do not need to edit these cells, but you may."
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "dOPn9wjOAwwK"
- },
- "source": [
- "# define number of classes based on YAML\n",
- "import yaml\n",
- "with open(dataset.location + \"/data.yaml\", 'r') as stream:\n",
- " num_classes = str(yaml.safe_load(stream)['nc'])"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "1Rvt5wilnDyX",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "8b8386bc-0e05-49a8-c1e0-48a836a0b399"
- },
- "source": [
- "#this is the model configuration we will use for our tutorial \n",
- "%cat /content/yolov5/models/yolov5s.yaml"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "# parameters\n",
- "nc: 80 # number of classes\n",
- "depth_multiple: 0.33 # model depth multiple\n",
- "width_multiple: 0.50 # layer channel multiple\n",
- "\n",
- "# anchors\n",
- "anchors:\n",
- " - [10,13, 16,30, 33,23] # P3/8\n",
- " - [30,61, 62,45, 59,119] # P4/16\n",
- " - [116,90, 156,198, 373,326] # P5/32\n",
- "\n",
- "# YOLOv5 backbone\n",
- "backbone:\n",
- " # [from, number, module, args]\n",
- " [[-1, 1, Focus, [64, 3]], # 0-P1/2\n",
- " [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n",
- " [-1, 3, C3, [128]],\n",
- " [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n",
- " [-1, 9, C3, [256]],\n",
- " [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n",
- " [-1, 9, C3, [512]],\n",
- " [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n",
- " [-1, 1, SPP, [1024, [5, 9, 13]]],\n",
- " [-1, 3, C3, [1024, False]], # 9\n",
- " ]\n",
- "\n",
- "# YOLOv5 head\n",
- "head:\n",
- " [[-1, 1, Conv, [512, 1, 1]],\n",
- " [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
- " [[-1, 6], 1, Concat, [1]], # cat backbone P4\n",
- " [-1, 3, C3, [512, False]], # 13\n",
- "\n",
- " [-1, 1, Conv, [256, 1, 1]],\n",
- " [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
- " [[-1, 4], 1, Concat, [1]], # cat backbone P3\n",
- " [-1, 3, C3, [256, False]], # 17 (P3/8-small)\n",
- "\n",
- " [-1, 1, Conv, [256, 3, 2]],\n",
- " [[-1, 14], 1, Concat, [1]], # cat head P4\n",
- " [-1, 3, C3, [512, False]], # 20 (P4/16-medium)\n",
- "\n",
- " [-1, 1, Conv, [512, 3, 2]],\n",
- " [[-1, 10], 1, Concat, [1]], # cat head P5\n",
- " [-1, 3, C3, [1024, False]], # 23 (P5/32-large)\n",
- "\n",
- " [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n",
- " ]\n"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "t14hhyqdmw6O"
- },
- "source": [
- "#customize iPython writefile so we can write variables\n",
- "from IPython.core.magic import register_line_cell_magic\n",
- "\n",
- "@register_line_cell_magic\n",
- "def writetemplate(line, cell):\n",
- " with open(line, 'w') as f:\n",
- " f.write(cell.format(**globals()))"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "uDxebz13RdRA"
- },
- "source": [
- "%%writetemplate /content/yolov5/models/custom_yolov5s.yaml\n",
- "\n",
- "# parameters\n",
- "nc: {num_classes} # number of classes\n",
- "depth_multiple: 0.33 # model depth multiple\n",
- "width_multiple: 0.50 # layer channel multiple\n",
- "\n",
- "# anchors\n",
- "anchors:\n",
- " - [10,13, 16,30, 33,23] # P3/8\n",
- " - [30,61, 62,45, 59,119] # P4/16\n",
- " - [116,90, 156,198, 373,326] # P5/32\n",
- "\n",
- "# YOLOv5 backbone\n",
- "backbone:\n",
- " # [from, number, module, args]\n",
- " [[-1, 1, Focus, [64, 3]], # 0-P1/2\n",
- " [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n",
- " [-1, 3, BottleneckCSP, [128]],\n",
- " [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n",
- " [-1, 9, BottleneckCSP, [256]],\n",
- " [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n",
- " [-1, 9, BottleneckCSP, [512]],\n",
- " [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n",
- " [-1, 1, SPP, [1024, [5, 9, 13]]],\n",
- " [-1, 3, BottleneckCSP, [1024, False]], # 9\n",
- " ]\n",
- "\n",
- "# YOLOv5 head\n",
- "head:\n",
- " [[-1, 1, Conv, [512, 1, 1]],\n",
- " [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
- " [[-1, 6], 1, Concat, [1]], # cat backbone P4\n",
- " [-1, 3, BottleneckCSP, [512, False]], # 13\n",
- "\n",
- " [-1, 1, Conv, [256, 1, 1]],\n",
- " [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
- " [[-1, 4], 1, Concat, [1]], # cat backbone P3\n",
- " [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small)\n",
- "\n",
- " [-1, 1, Conv, [256, 3, 2]],\n",
- " [[-1, 14], 1, Concat, [1]], # cat head P4\n",
- " [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium)\n",
- "\n",
- " [-1, 1, Conv, [512, 3, 2]],\n",
- " [[-1, 10], 1, Concat, [1]], # cat head P5\n",
- " [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large)\n",
- "\n",
- " [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)\n",
- " ]"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "VUOiNLtMP5aG"
- },
- "source": [
- "# Train Custom YOLOv5 Detector\n",
- "\n",
- "### Next, we'll fire off training!\n",
- "\n",
- "\n",
- "Here, we are able to pass a number of arguments:\n",
- "- **img:** define input image size\n",
- "- **batch:** determine batch size\n",
- "- **epochs:** define the number of training epochs. (Note: often, 3000+ are common here!)\n",
- "- **data:** set the path to our yaml file\n",
- "- **cfg:** specify our model configuration\n",
- "- **weights:** specify a custom path to weights. (Note: you can download weights from the Ultralytics Google Drive [folder](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J))\n",
- "- **name:** result names\n",
- "- **nosave:** only save the final checkpoint\n",
- "- **cache:** cache images for faster training"
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "1NcFxRcFdJ_O",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "84614320-0d9c-40c2-dbef-a2c121ab667b"
- },
- "source": [
- "# train yolov5s on custom data for 100 epochs\n",
- "# time its performance\n",
- "%%time\n",
- "%cd /content/yolov5/\n",
- "!python train.py --img 416 --batch 16 --epochs 100 --data {dataset.location}/data.yaml --cfg ./models/custom_yolov5s.yaml --weights '' --name yolov5s_results --cache"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "/content/yolov5\n",
- "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ WARNING: code is out of date by 577 commits. Use 'git pull' to update or 'git clone https://github.com/ultralytics/yolov5' to download latest.\n",
- "YOLOv5 v4.0-126-g886f1c0 torch 1.9.0+cu111 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)\n",
- "\n",
- "Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='./models/custom_yolov5s.yaml', data='/content/yolov5/American-Mushrooms-1/data.yaml', device='', entity=None, epochs=100, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[416, 416], linear_lr=False, local_rank=-1, log_artifacts=False, log_imgs=16, multi_scale=False, name='yolov5s_results', noautoanchor=False, nosave=False, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/yolov5s_results', single_cls=False, sync_bn=False, total_batch_size=16, weights='', workers=8, world_size=1)\n",
- "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)\n",
- "Start Tensorboard with \"tensorboard --logdir runs/train\", view at http://localhost:6006/\n",
- "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0\n",
- "\n",
- " from n params module arguments \n",
- " 0 -1 1 3520 models.common.Focus [3, 32, 3] \n",
- " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n",
- " 2 -1 1 19904 models.common.BottleneckCSP [64, 64, 1] \n",
- " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n",
- " 4 -1 1 161152 models.common.BottleneckCSP [128, 128, 3] \n",
- " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
- " 6 -1 1 641792 models.common.BottleneckCSP [256, 256, 3] \n",
- " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
- " 8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]] \n",
- " 9 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n",
- " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
- " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
- " 12 [-1, 6] 1 0 models.common.Concat [1] \n",
- " 13 -1 1 378624 models.common.BottleneckCSP [512, 256, 1, False] \n",
- " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n",
- " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
- " 16 [-1, 4] 1 0 models.common.Concat [1] \n",
- " 17 -1 1 95104 models.common.BottleneckCSP [256, 128, 1, False] \n",
- " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n",
- " 19 [-1, 14] 1 0 models.common.Concat [1] \n",
- " 20 -1 1 313088 models.common.BottleneckCSP [256, 256, 1, False] \n",
- " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n",
- " 22 [-1, 10] 1 0 models.common.Concat [1] \n",
- " 23 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n",
- " 24 [17, 20, 23] 1 18879 models.yolo.Detect [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
- "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n",
- " return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n",
- "Model Summary: 283 layers, 7257791 parameters, 7257791 gradients, 16.8 GFLOPS\n",
- "\n",
- "Scaled weight_decay = 0.0005\n",
- "Optimizer groups: 62 .bias, 70 conv.weight, 59 other\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mScanning 'American-Mushrooms-1/train/labels' for images and labels... 123 found, 0 missing, 0 empty, 0 corrupted: 100% 123/123 [00:00<00:00, 2407.13it/s]\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: American-Mushrooms-1/train/labels.cache\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB): 100% 123/123 [00:00<00:00, 511.60it/s]\n",
- "\u001b[34m\u001b[1mval: \u001b[0mScanning 'American-Mushrooms-1/valid/labels' for images and labels... 5 found, 0 missing, 0 empty, 0 corrupted: 100% 5/5 [00:00<00:00, 1242.98it/s]\n",
- "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: American-Mushrooms-1/valid/labels.cache\n",
- "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.0GB): 100% 5/5 [00:00<00:00, 321.12it/s]\n",
- "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
- "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
- "Plotting labels... \n",
- "\n",
- "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 3.48, Best Possible Recall (BPR) = 1.0000\n",
- "Image sizes 416 train, 416 test\n",
- "Using 2 dataloader workers\n",
- "Logging results to runs/train/yolov5s_results\n",
- "Starting training for 100 epochs...\n",
- "\n",
- " Epoch gpu_mem box obj cls total targets img_size\n",
- " 0/99 1.41G 0.1144 0.02597 0.02773 0.1681 49 416: 100% 8/8 [00:03<00:00, 2.55it/s]\n",
- " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 4.55it/s]\n",
- " all 5 14 0.00492 0.478 0.00691 0.00154\n",
- "\n",
- " Epoch gpu_mem box obj cls total targets img_size\n",
- " 0% 0/8 [00:00