add image classification tutorial

x4nth055 · x4nth055 · commit 810e604a9957 · 2019-09-14T12:16:43.000+02:00
diff --git a/README.md b/README.md
@@ -19,9 +19,10 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
 - ### [Machine Learning](https://www.thepythoncode.com/topic/machine-learning)
     - ### [Natural Language Processing](https://www.thepythoncode.com/topic/nlp)
         - [How to Build a Spam Classifier using Keras in Python](https://www.thepythoncode.com/article/build-spam-classifier-keras-python). ([code](machine-learning/nlp/spam-classifier))
-        - [How to Detect Human Faces in Python using OpenCV](https://www.thepythoncode.com/article/detect-faces-opencv-python). ([code](machine-learning/face_detection))
 
+    - [How to Detect Human Faces in Python using OpenCV](https://www.thepythoncode.com/article/detect-faces-opencv-python). ([code](machine-learning/face_detection))
     - [Building a Speech Emotion Recognizer using Scikit-learn](https://www.thepythoncode.com/article/building-a-speech-emotion-recognizer-using-sklearn). ([code](machine-learning/speech-emotion-recognition))
+    - [How to Make an Image Classifier in Python using Keras](https://www.thepythoncode.com/article/image-classification-keras-python). ([code](machine-learning/image-classifier))
     - [Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists).
 
 - ### [General Python Topics](https://www.thepythoncode.com/topic/general-python-topics)
diff --git a/machine-learning/image-classifier/README.md b/machine-learning/image-classifier/README.md
@@ -0,0 +1,14 @@
+# [How to Make an Image Classifier in Python using Keras](https://www.thepythoncode.com/article/image-classification-keras-python)
+To run this:
+- `pip3 install -r requirements.txt`
+- First, you need to train the model using `python train.py`
+- Edit the code in `test.py` for you optimal model weights in `results` folder ( currently empty because you need to train first ) and run:
+    ```
+    python test.py
+    ```
+    **Output:**
+    ```
+    10000/10000 [==============================] - 3s 331us/step
+    Test accuracy: 81.17999999999999 %
+    frog
+    ```
diff --git a/machine-learning/image-classifier/requirements.txt b/machine-learning/image-classifier/requirements.txt
@@ -0,0 +1 @@
+keras
diff --git a/machine-learning/image-classifier/test.py b/machine-learning/image-classifier/test.py
@@ -0,0 +1,37 @@
+from train import load_data
+from keras.models import load_model
+import matplotlib.pyplot as plt
+import numpy as np
+
+# CIFAR-10 classes
+categories = {
+    0: "airplane",
+    1: "automobile",
+    2: "bird",
+    3: "cat",
+    4: "deer",
+    5: "dog",
+    6: "frog",
+    7: "horse",
+    8: "ship",
+    9: "truck"
+}
+
+# load the testing set
+(_, _), (X_test, y_test) = load_data()
+# load the model with optimal weights
+model = load_model("results/cifar10-loss-0.58-acc-0.81.h5")
+# evaluation
+loss, accuracy = model.evaluate(X_test, y_test)
+print("Test accuracy:", accuracy*100, "%")
+
+# get prediction for this image
+sample_image = X_test[7500]
+prediction = np.argmax(model.predict(sample_image.reshape(-1, *sample_image.shape))[0])
+print(categories[prediction])
+
+# show the first image
+plt.axis('off')
+plt.imshow(sample_image)
+plt.savefig("frog.png")
+plt.show()
diff --git a/machine-learning/image-classifier/train.py b/machine-learning/image-classifier/train.py
@@ -0,0 +1,134 @@
+from keras.datasets import cifar10 # importing the dataset from keras
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+from keras.callbacks import ModelCheckpoint, TensorBoard
+from keras.utils import to_categorical
+import os
+
+# hyper-parameters
+batch_size = 64
+# 10 categories of images (CIFAR-10)
+num_classes = 10
+# number of training epochs
+epochs = 30
+
+def create_model(input_shape):
+    """
+    Constructs the model:
+        - 32 Convolutional (3x3)
+        - Relu
+        - 32 Convolutional (3x3)
+        - Relu
+        - Max pooling (2x2)
+        - Dropout
+
+        - 64 Convolutional (3x3)
+        - Relu
+        - 64 Convolutional (3x3)
+        - Relu
+        - Max pooling (2x2)
+        - Dropout
+
+        - 128 Convolutional (3x3)
+        - Relu
+        - 128 Convolutional (3x3)
+        - Relu
+        - Max pooling (2x2)
+        - Dropout
+        
+        - Flatten (To make a 1D vector out of convolutional layers)
+        - 1024 Fully connected units
+        - Relu
+        - Dropout
+        - 10 Fully connected units (each corresponds to a label category (cat, dog, etc.))
+    """
+
+    # building the model
+    model = Sequential()
+
+    model.add(Conv2D(filters=32, kernel_size=(3, 3), padding="same", input_shape=input_shape))
+    model.add(Activation("relu"))
+    model.add(Conv2D(filters=32, kernel_size=(3, 3), padding="same"))
+    model.add(Activation("relu"))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Dropout(0.25))
+
+    model.add(Conv2D(filters=64, kernel_size=(3, 3), padding="same"))
+    model.add(Activation("relu"))
+    model.add(Conv2D(filters=64, kernel_size=(3, 3), padding="same"))
+    model.add(Activation("relu"))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Dropout(0.25))
+
+    model.add(Conv2D(filters=128, kernel_size=(3, 3), padding="same"))
+    model.add(Activation("relu"))
+    model.add(Conv2D(filters=128, kernel_size=(3, 3), padding="same"))
+    model.add(Activation("relu"))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Dropout(0.25))
+
+    # flattening the convolutions
+    model.add(Flatten())
+    # fully-connected layers
+    model.add(Dense(1024))
+    model.add(Activation("relu"))
+    model.add(Dropout(0.5))
+    model.add(Dense(num_classes, activation="softmax"))
+
+    # print the summary of the model architecture
+    model.summary()
+
+    # training the model using rmsprop optimizer
+    model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+    return model
+
+
+def load_data():
+    """
+    This function loads CIFAR-10 dataset, normalized, and labels one-hot encoded
+    """
+    # loading the CIFAR-10 dataset, splitted between train and test sets
+    (X_train, y_train), (X_test, y_test) = cifar10.load_data()
+    print("Training samples:", X_train.shape[0])
+    print("Testing samples:", X_test.shape[0])
+    print(f"Images shape: {X_train.shape[1:]}")
+
+    # converting image labels to binary class matrices
+    y_train = to_categorical(y_train, num_classes)
+    y_test = to_categorical(y_test, num_classes)
+
+    # convert to floats instead of int, so we can divide by 255
+    X_train = X_train.astype("float32")
+    X_test = X_test.astype("float32")
+    X_train /= 255
+    X_test /= 255
+
+    return (X_train, y_train), (X_test, y_test)
+
+
+if __name__ == "__main__":
+
+    # load the data
+    (X_train, y_train), (X_test, y_test) = load_data()
+
+    # constructs the model
+    model = create_model(input_shape=X_train.shape[1:])
+
+    # some nice callbacks
+    tensorboard = TensorBoard(log_dir="logs/cifar10-model-v1")
+    checkpoint = ModelCheckpoint("results/cifar10-loss-{val_loss:.2f}-acc-{val_acc:.2f}.h5",
+                                save_best_only=True,
+                                verbose=1)
+
+    # make sure results folder exist
+    if not os.path.isdir("results"):
+        os.mkdir("results")
+
+    # train
+    model.fit(X_train, y_train,
+            batch_size=batch_size,
+            epochs=epochs,
+            validation_data=(X_test, y_test),
+            callbacks=[tensorboard, checkpoint],
+            shuffle=True)
