model.compile(loss='categorical_crossentropy',
optimizer='Adam',
metrics=['acc'])
# Train the model
nama_filenya = "weights_" + vartuning + "_.hdf5"
checkpointer = ModelCheckpoint(filepath=nama_filenya,
monitor='val_acc',
verbose=1,
save_best_only=True,
save_weights_only=True)
hist = model.fit(train_set_R1,
Y_train,
validation_data=(test_set_R1, Y_test),
batch_size=16,
nb_epoch=jumEpoch,
shuffle=True,
verbose=1,
callbacks=[checkpointer])
# Evaluate the model
# load best model
model.load_weights(nama_filenya)
Y_pred = model.predict(test_set_R1, batch_size=8)
#print(Y_pred)
k_val = 1
Y_pred_label = []
for idt in range(len(Y_pred)):
Y_pred_label.append(np.argmax(Y_pred[idt]))
def distortion_model_functional(X_train, Y_train, X_val, Y_val, params):
print("In", len(X_train))
# nb_conv = 4
# nb_pool = 2
batch_size = 64
nb_epoch = 1000
opt = keras.optimizers.Adam()
nb_filters = params['nb_filters']
nb_conv = params['nb_conv']
nb_pool = params['nb_pool']
nb_layer = params['nb_layer']
dropout = params['dropout']
hidden = params['nb_hidden']
nb_classes = Y_train.shape[1]
input_shape = (8, 8, 1)
input_pattern = Input(shape=input_shape, name='input1')
# build the rest of the network
model = Conv2D(nb_filters, (nb_conv, nb_conv),
padding='valid',
input_shape=input_shape,
name='conv2d_0')(input_pattern)
model = BatchNormalization()(model)
model = Activation('relu')(model)
model = Dropout(dropout)(model)
model = MaxPooling2D(pool_size=(nb_pool, nb_pool))(model)
model = BatchNormalization()(model)
# model = Activation('relu')(model)
for i in range(1, nb_layer):
model = Conv2D(nb_filters, nb_conv, nb_conv,
name='conv2d_' + str(i))(model)
model = BatchNormalization()(model)
model = Activation('relu')(model)
model = Dropout(dropout)(model)
model = MaxPooling2D(pool_size=(nb_pool, nb_pool))(model)
model = BatchNormalization()(model)
# model = Activation('relu')(model)
model = Dense(hidden, activation='relu')(model)
model = Activation('relu')(model)
# model.add(Activation('sigmoid'))
model = Dropout(dropout)(model)
out = Dense(nb_classes, name='dense_output', activation='linear')(model)
model = Model(inputs=[input_pattern], outputs=out)
model.summary()
model.compile(loss='mean_squared_error', optimizer=opt)
history = model.fit(X_train,
Y_train,
batch_size=batch_size,
epochs=nb_epoch,
verbose=0,
validation_data=[X_val, Y_val],
shuffle=True,
metric="mse")
return history, model
