Deep Learning with Keras and Tensorflow

Author: Valerio Maggio

PostDoc Data Scientist @ FBK/MPBA

Contacts:

@leriomaggio	+ValerioMaggio
valeriomaggio	vmaggio_at_fbk_dot_eu

git clone -b pydatait 
https://github.com/leriomaggio/deep-learning-keras-tensorflow.git

---

Outline at a glance

• Warmup

• Part I: Introduction

  • Intro to ANN

    • naive pure-Python implementation
    • fast forward, sgd, backprop

  • Intro to Tensorflow

    • Model + SGD with Tensorflow

  • Introduction to Keras

    • Overview and main features
      • Tensorflow backend
      • Theano backend
      • Keras Backend
      • Overview of the main layers
    • Multi-Layer Perceptron and Fully Connected
      • Examples with keras.models.Sequential and Dense
      • HandsOn: FC with keras

  • Extra Material:

    • Intro to Theano
    • Alternative ANN implementation for MNIST

• Break

• Part II: Supervised Learning and Convolutional Neural Nets

  • Intro: Focus on Image Classification

  • Intro to ConvNets

    • meaning of convolutional filters
      • examples from ImageNet
    • Meaning of dimensions of Conv filters (through an exmple of ConvNet)
    • Visualising ConvNets
    • HandsOn: ConvNet with keras

  • Advanced CNN

    • Dropout
    • MaxPooling
    • Batch Normalisation

  • Famous Models in Keras (ref: keras.applications) - VGG16 - VGG19 - ResNet50 - Inception v3

    • Transfer Learning
    • HandsOn: Fine tuning a network on new dataset

• Part III: Unsupervised Learning

  • AutoEncoders (5 mins)
  • word2vec & doc2vec (gensim) & keras.datasets
    • Embedding
    • word2vec and CNN
  • Exercises

• Part IV: Advanced Materials

  • RNN and LSTM (10 mins)
    • RNN, LSTM, GRU
  • Example of RNN and LSTM with Text
  • HandsOn: IMDB
  • Multi-Input/Multi-Output Network Topologies

• Wrap up and Conclusions

---

Requirements

This tutorial requires the following packages:

• Python version 3.5

  • Python 3.4 should be fine as well
  • likely Python 2.7 would be also fine, but who knows? :P

• numpy version 1.10 or later: http://www.numpy.org/

• scipy version 0.16 or later: http://www.scipy.org/

• matplotlib version 1.4 or later: http://matplotlib.org/

• pandas version 0.16 or later: http://pandas.pydata.org

• scikit-learn version 0.15 or later: http://scikit-learn.org

• keras version 2.0 or later: http://keras.io

• tensorflow version 1.0 or later: https://www.tensorflow.org

• ipython/jupyter version 4.0 or later, with notebook support

(Optional but recommended):

• pyyaml
• hdf5 and h5py (required if you use model saving/loading functions in keras)
• NVIDIA cuDNN if you have NVIDIA GPUs on your machines. https://developer.nvidia.com/rdp/cudnn-download

The easiest way to get (most) these is to use an all-in-one installer such as Anaconda from Continuum. These are available for multiple architectures.

---

Python Version

I'm currently running this tutorial with Python 3 on Anaconda

!python --version

Python 3.5.2

---

Setting the Environment

In this repository, files to re-create virtual env with conda are provided for Linux and OSX systems, namely deep-learning.yml and deep-learning-osx.yml, respectively.

To re-create the virtual environments (on Linux, for example):

conda env create -f deep-learning.yml

For OSX, just change the filename, accordingly.

Installing Tensorflow

To date tensorflow comes in two different packages, namely tensorflow and tensorflow-gpu, whether you want to install the framework with CPU-only or GPU support, respectively.

For this reason, tensorflow has not been included in the conda envs and has to be installed separately.

Tensorflow for CPU only:

pip install tensorflow

Tensorflow with GPU support:

pip install tensorflow-gpu

Note: NVIDIA Drivers and CuDNN must be installed and configured before hand. Please refer to the official Tensorflow documentation for further details.

Important Note:

All the code provided+ in this tutorial can run even if tensorflow is not installed, and so using theano as the (default) backend!

This is exactly the power of Keras!

Therefore, installing tensorflow is not stricly required!

+: Apart from the 1.2 Introduction to Tensorflow tutorial, of course.

Configure Keras with tensorflow

By default, Keras is configured with theano as backend.

If you want to use tensorflow instead, these are the simple steps to follow:

1. Create the keras.json (if it does not exist):

touch $HOME/.keras/keras.json

2. Copy the following content into the file:

{
    "epsilon": 1e-07,
    "backend": "tensorflow",
    "floatx": "float32",
    "image_data_format": "channels_last"
}

3. Verify it is properly configured:

!cat ~/.keras/keras.json

{
	"epsilon": 1e-07,
	"backend": "tensorflow",
	"floatx": "float32",
	"image_data_format": "channels_last"
}

---

Test if everything is up&running

1. Check import

import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import sklearn

import keras

Using TensorFlow backend.

2. Check installed Versions

import numpy
print('numpy:', numpy.__version__)

import scipy
print('scipy:', scipy.__version__)

import matplotlib
print('matplotlib:', matplotlib.__version__)

import IPython
print('iPython:', IPython.__version__)

import sklearn
print('scikit-learn:', sklearn.__version__)

numpy: 1.11.1
scipy: 0.18.0
matplotlib: 1.5.2
iPython: 5.1.0
scikit-learn: 0.18

import keras
print('keras: ', keras.__version__)

# optional
import theano
print('Theano: ', theano.__version__)

import tensorflow as tf
print('Tensorflow: ', tf.__version__)

keras:  2.0.2
Theano:  0.9.0
Tensorflow:  1.0.1

If everything worked till down here, you're ready to start!