regressor.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], x_train.shape[2])))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=50, return_sequences=True ))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=33, return_sequences=True))
regressor.add(Flatten())
regressor.add(Dense(units=1))
#compiling the model with mean_absolute_percentage_error and adam optimizer
regressor.compile(optimizer='adam', loss='mean_absolute_percentage_error')
#fitting model with training sets and validation set
history = regressor.fit(x_train, y_train, epochs = EPOCH, batch_size=BATCH_SIZE, validation_data=(x_test, y_test))
bm.save_val_loss_plot(history, PLACE+"_epoch_history.csv")
results = regressor.predict(x_test)
#constructing estimation dataframe
real_values = pd.DataFrame(index = test.index,
data = bm.inverse_scale(sc, y_test.reshape(-1, 1)),
columns = ['Real'])
predictions = pd.DataFrame(index = test.index,
data = bm.inverse_scale(sc, results),
columns = ['Predictions'])
predictions = pd.concat([real_values, predictions], axis = 1)
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=33, return_sequences=True))
regressor.add(Flatten())
regressor.add(Dense(units=1))
#compiling the model with mean_absolute_percentage_error and adam optimizer
regressor.compile(optimizer='adam', loss='mean_absolute_percentage_error')
#fitting model with training sets and validation set
history = regressor.fit(x_train,
y_train,
epochs=EPOCH,
batch_size=BATCH_SIZE,
validation_data=(x_test, y_test))
bm.save_val_loss_plot(history, "loss_graph.png")
results = regressor.predict(x_test)
#constructing estimation dataframe
real_values = pd.DataFrame(index=test.index,
data=bm.inverse_scale(sc, y_test.reshape(-1, 1)),
columns=['Real'])
predictions = pd.DataFrame(index=test.index,
data=bm.inverse_scale(sc, results),
columns=['Predictions'])
predictions = pd.concat([real_values, predictions], axis=1)
#constructing daily error dataframe
