model = Dropout(0.2)(model)
model = Dense(k, activation='relu')(model)
model = Dropout(0.5)(model)
model = Dense(int(k/4), activation='relu')(model)
model = Dropout(0.5)(model)
model = Dense(int(k/16), activation='relu')(model)
model = Dropout(0.5)(model)
output = Dense(1, activation='linear')(model)
model = Model([input1, input2], output)
model.compile(loss='mse', optimizer='adam', metrics=['mae', 'acc'])
SVG(model_to_dot(model).create(prog='dot', format='svg'))
train, test = cross_validation.train_test_split(data, test_size=0.1, random_state=1)
x_train = [train.uid, train.sid]
y_train = train.rating
x_test = [test.uid, test.sid]
y_test = test.rating
history = model.fit(x_train, y_train, batch_size=500, epochs=20, validation_data=(x_test, y_test))
save_path = './save/ml-deep/'
model.save(save_path + 'model.h5')
with open(save_path + 'history.pkl', 'wb') as file_history:
pickle.dump(history.history, file_history)
pd.DataFrame(history.history, columns=['loss', 'val_loss']).head(20).transpose()
plot(history.history['loss'], label='loss')
plot(history.history['val_loss'], label='val_loss')
plot(history.history['val_mean_absolute_error'], label='val_mae')
plot(history.history['val_acc'], label='val_acc')
plt.legend()
plt.ylim(0, 3)
test_size=0.1,
random_state=1)
x_train = [train.uid, train.sid]
y_train = train.rating
x_test = [test.uid, test.sid]
y_test = test.rating
save_path = './save/bgm-deep/'
checkpoint = ModelCheckpoint(save_path + 'best_model.h5',
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min')
callbacks_list = [checkpoint]
history = model.fit(x_train,
y_train,
batch_size=1000,
epochs=20,
validation_data=(x_test, y_test),
callbacks=callbacks_list)
with open(save_path + 'history.pkl', 'wb') as file_history:
pickle.dump(history.history, file_history)
pd.DataFrame(
history.history,
columns=['loss', 'val_loss', 'val_mean_absolute_error',
'val_acc']).head(20).transpose()
plot(history.history['loss'], label='loss')
plot(history.history['val_loss'], label='val_loss')
plot(history.history['val_mean_absolute_error'], label='val_mae')
plot(history.history['val_acc'], label='val_acc')
plt.legend()
plt.ylim(0, 3)
