def _train(self):
data, label = self._load_data()
train_data, train_label, validate_data, validate_label, test_data, test_label = split_data(data, label,
to_categorical=True)
network_input = Input(shape=(100, 3))
network = LSTM(32, return_sequences=True)(network_input)
network = LSTM(32)(network)
network = Dense(5, activation=softmax)(network)
network = Model(inputs=[network_input], outputs=[network])
network.compile(optimizer=RMSprop(lr=0.01), loss=categorical_crossentropy, metrics=[categorical_accuracy])
network.summary()
callback = [
callbacks.ReduceLROnPlateau(monitor="categorical_accuracy", factor=0.1, patience=3)
]
self.train_history = network.fit(train_data, train_label,
validation_data=(validate_data, validate_label), batch_size=self.BATCH_SIZE,
epochs=self.EPOCHS, callbacks=callback)
self.evaluate_history = network.evaluate(test_data, test_label, batch_size=self.BATCH_SIZE)
return network
def run_LSTM(X_train, X_test, y_train, y_test, symbol, price):
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
LSTM = create_LSTM(X_train)
history = LSTM.fit(X_train, y_train, epochs=100, batch_size=32)
print(LSTM.summary())
results = LSTM.evaluate(X_test, y_test, batch_size=32)
print("test loss, test acc:", results)
# summarize history for accuracy
plt.plot(history.history['accuracy'], label='Train')
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
prediction = LSTM.predict(X_test)
y_prediction = prediction[:, -1, :]
y_prediction = np.argmax(prediction, axis=1)
print(y_prediction)
NAV_history = Generate_nav(10000, symbol, price, y_prediction)
merge = Dense(8)(merge) output = Dense(1)(merge) output_1 = Dense(4)(output) output_1 = Dense(1)(output_1) output_2 = Dense(4)(output) output_2 = Dense(1)(output_2) model = Model(inputs=[input, input2], outputs=[output_1, output_2]) model.summary() # quit() model.compile(loss='mse', optimizer='adam', metrics=['mse']) model.fit([x, x2], [y, y2], epochs=100, batch_size=1) res = model.evaluate([x, x2], [y, y2], batch_size=1) from keras.callbacks import EarlyStopping # callback = EarlyStopping(monitor='loss', patience=20, mode='auto') # # callback = EarlyStopping(monitor='acc', patience=20, mode='max') # model2.compile(loss='mse', optimizer='adam', metrics=['mse']) # model2.fit(x_, y, epochs=1000, batch_size=1, callbacks=[callback]) # loss2, mse2 = model2.evaluate(x_, y, batch_size=1, verbose=1) # x_input = np.array([[6.5, 7.5, 8.5], [50, 60, 70], [70, 80, 90], [100, 110, 120]]) # x = x_input.reshape(-1, 3, 1) # y_pred = model.predict(x) print(res) # print(loss2, mse2)
