keras_cnn.py

import os
import common

import matplotlib.pyplot as plt

import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.callbacks import EarlyStopping

batch_size = 10
num_classes = 2
epochs = 50
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'keras_apk_trained_model.h5'

# The data, split between train and test sets
(x_train, y_train), (x_test, y_test) = common.load_dataset_small()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

# Building model
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', activation='relu',
                 input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

# # Training
# opt = keras.optimizers.SGD(lr=0.005, decay=1e-4)
# model.compile(loss='categorical_crossentropy',
#               optimizer=opt,
#               metrics=['accuracy'])

# early_stopping = EarlyStopping(monitor='value_loss', patience=3, verbose=1)

# history = model.fit(x_train, y_train,
#                     batch_size=batch_size,
#                     epochs=epochs,
#                     verbose=1,
#                     validation_data=(x_test, y_test),
#                     shuffle=True,
#                     callbacks=[early_stopping]
#                     )

# # # Plot accuracy/loss to epochs
# # # history - dict of ['acc', 'loss', 'val_acc', 'val_loss']
# plt.plot(range(0, epochs), history.history['val_acc'])
# plt.xlabel('Epochs')
# plt.ylabel('Accuracy')
# plt.show()

# # plt.plot(range(0, epochs), history.history['val_loss'])
# # plt.xlabel('Epochs')
# # plt.ylabel('Loss')
# # plt.show()

# # Save model and weights
# if not os.path.isdir(save_dir):
#     os.makedirs(save_dir)
# model_path = os.path.join(save_dir, model_name)
# model.save(model_path)
# print('Saved trained model at %s ' % model_path)

# scores = model.evaluate(x_test, y_test, verbose=1)
# print('Test loss:', scores[0])
# print('Test accuracy:', scores[1])