def lstm_prediction(indicator_file_name, data_input):
modelh5_path = Configuration.get_file_model_file(indicator_file_name)
# initialize the Recurrent Neural Network
# Solving pattern with sequence of data
regressor = keras.models.load_model(modelh5_path)
predicted_price = regressor.predict(data_input)
result = {
'summary': str(regressor.summary()),
'predicted_price': predicted_price
}
return result
def initialise_model_lstm(indicator_file_name, timesteps, test_set_input,
test_set_output, train_set_input, train_set_output,
epochs):
"""[summary]
Args:
indicator_file_name ([type]): [description]
timesteps ([type]): [description]
test_set_input ([type]): [description]
test_set_output ([type]): [description]
train_set_input ([type]): [description]
train_set_output ([type]): [description]
epochs ([type]): [description]
Returns:
[type]: [description]
"""
keras_model_directory = Configuration.get_directory(indicator_file_name)
modelh5_path = Configuration.get_file_model_file(indicator_file_name)
# define the RNN input shape
lstm_input_shape = (timesteps, 1)
# initialize the Recurrent Neural Network
# Solving pattern with sequence of data
regressor = keras.models.Sequential()
training.update_state(state="PROGRESS", meta={'progress': 70})
neurons = 50
batch_size = 32
# Long Short Term Memory model -
# supervised Deep Neural Network that is very good at doing time-series prediction.
# adding the first LSTM Layer and some Dropout regularisation
#
# Dropout: Makes sure that network can never rely on any given activation
# to be present because at any moment they could become squashed i.e. value = 0
# forced to learn a redunant representation for everything
#
# return sequences: We want output after every layer in which will be
# passed to the next layer
regressor.add(
keras.layers.LSTM(units=neurons,
return_sequences=True,
input_shape=lstm_input_shape))
regressor.add(keras.layers.Dropout(0.2))
# adding a second LSTM Layer and some Dropout regularisation
regressor.add(keras.layers.LSTM(units=neurons, return_sequences=True))
regressor.add(keras.layers.Dropout(0.2))
# adding a third LSTM Layer and some Dropout regularisation
regressor.add(keras.layers.LSTM(units=neurons, return_sequences=True))
regressor.add(keras.layers.Dropout(0.2))
# adding a fourth LSTM Layer and some Dropout regularisation
regressor.add(keras.layers.LSTM(units=neurons))
regressor.add(keras.layers.Dropout(0.2))
# adding the output layer
# Dense format output layer
regressor.add(keras.layers.Dense(units=1)) # prediction
# compile network
# find the global minimal point
regressor.compile(optimizer='adam', loss='mean_absolute_error')
regressor.summary()
accuracy = regressor.evaluate(test_set_input, test_set_output)
print('Restored model, accuracy: {:5.2f}%'.format(100 * accuracy))
# fitting the RNN to the training set
# giving input in batch sizes of 5
# loss should decrease on each an every epochs
history = regressor.fit(train_set_input,
train_set_output,
batch_size=batch_size,
epochs=epochs,
steps_per_epoch=5,
validation_data=(test_set_input, test_set_output),
verbose=0,
callbacks=[KerasFitCallback(training, epochs)])
# save the model for javascript format readable
tfjs.converters.save_keras_model(regressor, keras_model_directory)
# save the model for python format readable
regressor.save(modelh5_path)
predicted_price = regressor.predict(test_set_input)
result = {
'summary': str(regressor.summary()),
'history': str(history),
'predicted_price': predicted_price
}
return result
