Update Rock_Paper_Scissors_VGG16.py

Rock_Paper_Scissors_VGG16.py

@@ -1,186 +1,180 @@
-import os
-import numpy as np
-from keras import applications, Sequential
-from keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
-from keras.layers import Dense, Dropout, Flatten, BatchNormalization
-from keras.optimizers import Adam, SGD
-from keras.preprocessing.image import ImageDataGenerator
-from keras.regularizers import l2
-from matplotlib import pyplot as plt
-from sklearn.metrics import classification_report, confusion_matrix
-
-# Part 1
-# a) visualized samples from the dataset, i.e.: rock, paper, scissors hand signs
-# with the appropriate labels
-# b) summary of the model architecture in a form of a plot or text
-# c) model accuracy evaluation plot after the training concludes
-# d) model loss evaluation plot after the training concludes
-
-
-# Image directory's and defining the dimensions & Batch size as well as epochs
-base_dir = '../rps'
-train_dir = os.path.join(base_dir, 'train')
-valid_dir = os.path.join(base_dir, 'validation')
-BATCH_SIZE = 32
-EPOCHS = 7
-img_width, img_height = 224, 224
-# Define L2 regularization coefficient to prevent overfitting
-l2_reg = 0.00001
-
-# Optimization + Learning rate variables
-opt = Adam(learning_rate=1e-4)
-opt1 = Adam(learning_rate=2e-4)
-opt2 = Adam(learning_rate=0.0001)
-opt3 = SGD(learning_rate=1e-4, momentum=0.99)
-
-# Preparing the Train/Validation and Augmentation Data
-train_datagen = ImageDataGenerator(
-    rescale=1.0 / 255,
-    rotation_range=90,
-    zoom_range=0.1,
-    width_shift_range=0.2,
-    height_shift_range=0.2,
-    shear_range=0.2,
-    # horizontal_flip=True,
-    vertical_flip=True,
-    brightness_range=(0.2, 1),
-    fill_mode='nearest',
-    validation_split=0.2)
-
-train_generator = train_datagen.flow_from_directory(
-    train_dir,
-    shuffle=True,
-    target_size=(img_width, img_height),
-    batch_size=BATCH_SIZE,
-    class_mode='categorical',
-    subset='training')
-
-# a) Visualize samples from the dataset
-class_names = ['paper', 'rock', 'scissors']
-images, labels = train_generator.next()
-plt.figure(figsize=(10, 10))
-for i in range(9):
-    plt.subplot(3, 3, i + 1)
-    label_index = np.argmax(labels[i])
-    plt.title('Label: ' + class_names[label_index])
-    plt.imshow(images[i])
-    plt.tight_layout()
-    plt.axis('off')
-plt.show()
-
-validation_datagen = ImageDataGenerator(rescale=1.0 / 255)
-validation_generator = validation_datagen.flow_from_directory(
-    valid_dir,
-    target_size=(img_width, img_height),
-    batch_size=BATCH_SIZE,
-    class_mode='categorical')
-
-# -------Callbacks-------------#
-# It'll save the best trained weight
-checkpoint = ModelCheckpoint(
-    filepath='best_weights.hdf5',
-    monitor='val_loss',
-    verbose=1,
-    save_best_only=True,
-    mode='min',
-    save_weights_only=False
-)
-# Early stop = in case of high Validation Loss
-early_stop = EarlyStopping(
-    monitor='val_loss',
-    min_delta=0.001,
-    patience=5,
-    verbose=1,
-    mode='auto'
-)
-# Defining a learning rate reduction callback when its necessary it'll reduce
-#  the learning rate when its necessary
-lr_reduction = ReduceLROnPlateau(
-    monitor='val_loss',
-    factor=0.2,
-    patience=2,
-    verbose=1,
-    mode='auto',
-    cooldown=1,
-    min_lr=0.000001
-)
-callbacks = [checkpoint, early_stop, lr_reduction]
-
-# Load the pre-trained VGG16 model without the top layer
-base_model = applications.VGG16(weights='imagenet', include_top=False, pooling='max',
-                                input_shape=(img_width, img_height, 3))
-
-# Freeze the pre-trained layers from 0-14,
-# so they are not updated during training
-for layer in base_model.layers[:10]:
-    layer.trainable = False
-# b) summary of base model
-base_model.summary()
-
-# Adding custom layers on top of VGG16
-model = Sequential()
-model.add(base_model)
-model.add(Flatten())
-model.add(Dense(512, activation='relu', kernel_regularizer=l2(l2_reg)))
-model.add(BatchNormalization())
-model.add(Dropout(0.3))
-model.add(Dense(3, activation='softmax', kernel_regularizer=l2(l2_reg)))
-# b) summary of model
-model.summary()
-
-# Compile the model
-model.compile(optimizer=opt,
-              loss='categorical_crossentropy',
-              metrics=['accuracy'])
-
-# Finally we train the model with our desired adjustments
-history = model.fit(
-    train_generator,
-    epochs=EPOCHS,
-    callbacks=callbacks,
-    validation_data=validation_generator)
-
-
-# Plotting the Models 'accuracy' & 'loss'
-def eval_plot(history):
-    plt.figure(figsize=(14, 5))
-
-    # Accuracy plot
-    plt.subplot(1, 2, 1)
-    acc = history.history['accuracy']
-    val_acc = history.history['val_accuracy']
-    epochs = range(len(acc))
-    acc_plot, = plt.plot(epochs, acc, 'r')
-    val_acc_plot, = plt.plot(epochs, val_acc, 'b')
-    plt.title('Training and Validation Accuracy')
-    plt.legend([acc_plot, val_acc_plot], ['Training Accuracy', 'Validation Accuracy'])
-
-    # Loss plot
-    plt.subplot(1, 2, 2)
-    loss = history.history['loss']
-    val_loss = history.history['val_loss']
-    epochs = range(len(loss))
-    loss_plot, = plt.plot(epochs, loss, 'r')
-    val_loss_plot, = plt.plot(epochs, val_loss, 'b')
-    plt.title('Training and Validation Loss')
-    plt.legend([loss_plot, val_loss_plot], ['Training Loss', 'Validation Loss'])
-    plt.tight_layout()
-    plt.show()
-
-
-# Evaluate the Process to find out how well the model has been trained
-def evaluate(model):
-    num_of_test_samples = len(validation_generator.filenames)
-
-    y_pred = model.predict(validation_generator, num_of_test_samples // BATCH_SIZE + 1)
-    y_pred = np.argmax(y_pred, axis=1)
-    print('\nConfusion Matrix\n')
-    print(confusion_matrix(validation_generator.classes, y_pred))
-    print('\n\nClassification Report\n')
-    target_names = ['Paper', 'Rock', 'Scissors']
-    print(classification_report(validation_generator.classes, y_pred, target_names=target_names))
-
-
-eval_plot(history)
-evaluate(model)
-model.save('../Rock_Paper_Scissors_VGG16/RPS_Model.hdf5')
+import os
+import numpy as np
+from keras import applications, Sequential
+from keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
+from keras.layers import Dense, Dropout, Flatten, BatchNormalization
+from keras.optimizers import Adam, SGD
+from keras.preprocessing.image import ImageDataGenerator
+from keras.regularizers import l2
+from matplotlib import pyplot as plt
+from sklearn.metrics import classification_report, confusion_matrix
+
+
+
+# Image directory's and defining the dimensions & Batch size as well as epochs
+base_dir = '../rps'
+train_dir = os.path.join(base_dir, 'train')
+valid_dir = os.path.join(base_dir, 'validation')
+BATCH_SIZE = 32
+EPOCHS = 7
+img_width, img_height = 224, 224
+# Define L2 regularization coefficient to prevent overfitting
+l2_reg = 0.00001
+
+# Optimization + Learning rate variables
+opt = Adam(learning_rate=1e-4)
+opt1 = Adam(learning_rate=2e-4)
+opt2 = Adam(learning_rate=0.0001)
+opt3 = SGD(learning_rate=1e-4, momentum=0.99)
+
+# Preparing the Train/Validation and Augmentation Data
+train_datagen = ImageDataGenerator(
+    rescale=1.0 / 255,
+    rotation_range=90,
+    zoom_range=0.1,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    shear_range=0.2,
+    # horizontal_flip=True,
+    vertical_flip=True,
+    brightness_range=(0.2, 1),
+    fill_mode='nearest',
+    validation_split=0.2)
+
+train_generator = train_datagen.flow_from_directory(
+    train_dir,
+    shuffle=True,
+    target_size=(img_width, img_height),
+    batch_size=BATCH_SIZE,
+    class_mode='categorical',
+    subset='training')
+
+# a) Visualize samples from the dataset
+class_names = ['paper', 'rock', 'scissors']
+images, labels = train_generator.next()
+plt.figure(figsize=(10, 10))
+for i in range(9):
+    plt.subplot(3, 3, i + 1)
+    label_index = np.argmax(labels[i])
+    plt.title('Label: ' + class_names[label_index])
+    plt.imshow(images[i])
+    plt.tight_layout()
+    plt.axis('off')
+plt.show()
+
+validation_datagen = ImageDataGenerator(rescale=1.0 / 255)
+validation_generator = validation_datagen.flow_from_directory(
+    valid_dir,
+    target_size=(img_width, img_height),
+    batch_size=BATCH_SIZE,
+    class_mode='categorical')
+
+# -------Callbacks-------------#
+# It'll save the best trained weight
+checkpoint = ModelCheckpoint(
+    filepath='best_weights.hdf5',
+    monitor='val_loss',
+    verbose=1,
+    save_best_only=True,
+    mode='min',
+    save_weights_only=False
+)
+# Early stop = in case of high Validation Loss
+early_stop = EarlyStopping(
+    monitor='val_loss',
+    min_delta=0.001,
+    patience=5,
+    verbose=1,
+    mode='auto'
+)
+# Defining a learning rate reduction callback when its necessary it'll reduce
+#  the learning rate when its necessary
+lr_reduction = ReduceLROnPlateau(
+    monitor='val_loss',
+    factor=0.2,
+    patience=2,
+    verbose=1,
+    mode='auto',
+    cooldown=1,
+    min_lr=0.000001
+)
+callbacks = [checkpoint, early_stop, lr_reduction]
+
+# Load the pre-trained VGG16 model without the top layer
+base_model = applications.VGG16(weights='imagenet', include_top=False, pooling='max',
+                                input_shape=(img_width, img_height, 3))
+
+# Freeze the pre-trained layers from 0-10,
+# so they are not updated during training
+for layer in base_model.layers[:10]:
+    layer.trainable = False
+# b) summary of base model
+base_model.summary()
+
+# Adding custom layers on top of VGG16
+model = Sequential()
+model.add(base_model)
+model.add(Flatten())
+model.add(Dense(512, activation='relu', kernel_regularizer=l2(l2_reg)))
+model.add(BatchNormalization())
+model.add(Dropout(0.3))
+model.add(Dense(3, activation='softmax', kernel_regularizer=l2(l2_reg)))
+# b) summary of model
+model.summary()
+
+# Compile the model
+model.compile(optimizer=opt,
+              loss='categorical_crossentropy',
+              metrics=['accuracy'])
+
+# Finally we train the model with our desired adjustments
+history = model.fit(
+    train_generator,
+    epochs=EPOCHS,
+    callbacks=callbacks,
+    validation_data=validation_generator)
+
+
+# Plotting the Models 'accuracy' & 'loss'
+def eval_plot(history):
+    plt.figure(figsize=(14, 5))
+
+    # Accuracy plot
+    plt.subplot(1, 2, 1)
+    acc = history.history['accuracy']
+    val_acc = history.history['val_accuracy']
+    epochs = range(len(acc))
+    acc_plot, = plt.plot(epochs, acc, 'r')
+    val_acc_plot, = plt.plot(epochs, val_acc, 'b')
+    plt.title('Training and Validation Accuracy')
+    plt.legend([acc_plot, val_acc_plot], ['Training Accuracy', 'Validation Accuracy'])
+
+    # Loss plot
+    plt.subplot(1, 2, 2)
+    loss = history.history['loss']
+    val_loss = history.history['val_loss']
+    epochs = range(len(loss))
+    loss_plot, = plt.plot(epochs, loss, 'r')
+    val_loss_plot, = plt.plot(epochs, val_loss, 'b')
+    plt.title('Training and Validation Loss')
+    plt.legend([loss_plot, val_loss_plot], ['Training Loss', 'Validation Loss'])
+    plt.tight_layout()
+    plt.show()
+
+
+# Evaluate the Process to find out how well the model has been trained
+def evaluate(model):
+    num_of_test_samples = len(validation_generator.filenames)
+
+    y_pred = model.predict(validation_generator, num_of_test_samples // BATCH_SIZE + 1)
+    y_pred = np.argmax(y_pred, axis=1)
+    print('\nConfusion Matrix\n')
+    print(confusion_matrix(validation_generator.classes, y_pred))
+    print('\n\nClassification Report\n')
+    target_names = ['Paper', 'Rock', 'Scissors']
+    print(classification_report(validation_generator.classes, y_pred, target_names=target_names))
+
+
+eval_plot(history)
+evaluate(model)
+model.save('../Rock_Paper_Scissors_VGG16/RPS_Model.hdf5')