def example(x_train, y_train, x_test, y_test):
from keras.callbacks import TensorBoard
tensor_board = TensorBoard(log_dir='./Graph',
histogram_freq=1,
write_graph=True,
write_images=True)
input1 = Input(shape=(28 * 28, ))
hidden1 = Dense(512, activation='relu')(input1)
hidden2 = Dense(256, activation='relu')(hidden1)
output = Dense(10, activation='softmax')(hidden2)
model = Model(inputs=input1, outputs=output)
model.compile(optimizer='sgd',
loss='binary_crossentropy',
metrics=['accuracy', 'mean_squared_error'])
model._get_distribution_strategy = lambda: None
model.run_eagerly = lambda: None
model.fit(x_train,
y_train,
epochs=5,
validation_data=(x_test, y_test),
callbacks=[tensor_board])
test_loss, test_acc, test_mse = model.evaluate(x_test, y_test)
print(
f'Test loss: {test_loss}, test mse: {test_mse}, test accuracy: {test_acc}'
)
print(model.summary())
def End_to_end_model(time_step, feature_dim, output_dim):
frames_input = layers.Input(shape=(None, 299, 299, 3),
dtype='float32',
name='frame_input')
InceptionModel = Inception_model()
InceptionModel.load_weights('./out/Inception_weight.h5')
feature_model = Model(
inputs=InceptionModel.input,
outputs=InceptionModel.get_layer('feature_out').output)
# apply feature model to each time step of input
feature_output = layers.TimeDistributed(feature_model,
input_shape=(299, 299,
3))(frames_input)
# apply lstm model to extracted feature
lstmModel = LSTM_model(time_step, feature_dim, output_dim)
# mask padding value
# feature_output = layers.Masking(mask_value=0., input_shape=(time_step, feature_dim))(feature_output)
model_output = lstmModel(inputs=feature_output)
end_to_end_model = Model(inputs=frames_input, outputs=model_output)
end_to_end_model._get_distribution_strategy = lambda: None
return end_to_end_model
def batchnorm_dropout(x_train, y_train, x_test, y_test):
input1 = Input(shape=(28 * 28, ))
hidden1 = Dense(512, activation='relu')(input1)
hidden2 = BatchNormalization()(hidden1)
hidden3 = Activation('relu')(hidden2)
hidden4 = Dropout(0.5)(hidden3)
hidden5 = Dense(256)(hidden4)
hidden6 = BatchNormalization()(hidden5)
hidden7 = Activation('relu')(hidden6)
output = Dense(10, activation='softmax')(hidden7)
model = Model(inputs=input1, outputs=output)
model.compile(optimizer='Adam',
loss='binary_crossentropy',
metrics=['accuracy', 'mean_squared_error'])
model._get_distribution_strategy = lambda: None
model.run_eagerly = lambda: None
model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))
test_loss, test_acc, test_mse = model.evaluate(x_test, y_test)
print(
f'Test loss: {test_loss}, test mse: {test_mse}, test accuracy: {test_acc}'
)
print(model.summary())
return model
