def build(self):
dim_data = self.size_of_input_data_dim
nb_time_step = self.size_of_input_timesteps
financial_time_series_input = Input(shape=(nb_time_step, dim_data), name='x1')
lstm_layer_1 = LSTM(output_dim=nb_hidden_units, dropout_U=dropout, dropout_W=dropout,
W_regularizer=l2(l2_norm_alpha), b_regularizer=l2(l2_norm_alpha), activation='tanh',
return_sequences=True, name='lstm_layer1')
lstm_layer_21 = LSTM(output_dim=nb_hidden_units, dropout_U=dropout, dropout_W=dropout,
W_regularizer=l2(l2_norm_alpha), b_regularizer=l2(l2_norm_alpha), activation='tanh',
return_sequences=True, name='lstm_layer2_loss1')
lstm_layer_22 = LSTM(output_dim=nb_hidden_units, dropout_U=dropout, dropout_W=dropout,
W_regularizer=l2(l2_norm_alpha), b_regularizer=l2(l2_norm_alpha), activation='tanh',
return_sequences=True, name='lstm_layer2_loss2')
lstm_layer_23 = LSTM(output_dim=nb_hidden_units, dropout_U=dropout, dropout_W=dropout,
W_regularizer=l2(l2_norm_alpha), b_regularizer=l2(l2_norm_alpha), activation='tanh',
return_sequences=True, name='lstm_layer2_loss3')
lstm_layer_24 = LSTM(output_dim=nb_hidden_units, dropout_U=dropout, dropout_W=dropout,
W_regularizer=l2(l2_norm_alpha), b_regularizer=l2(l2_norm_alpha), activation='tanh',
return_sequences=True, name='lstm_layer2_loss4')
lstm_layer_25 = LSTM(output_dim=nb_hidden_units, dropout_U=dropout, dropout_W=dropout,
W_regularizer=l2(l2_norm_alpha), b_regularizer=l2(l2_norm_alpha), activation='tanh',
return_sequences=True, name='lstm_layer2_loss5')
h1 = lstm_layer_1(financial_time_series_input)
h21 = lstm_layer_21(h1)
h22 = lstm_layer_22(h1)
h23 = lstm_layer_23(h1)
h24 = lstm_layer_24(h1)
h25 = lstm_layer_25(h1)
time_series_predictions1 = TimeDistributed(Dense(1), name="p1")(h21) # custom 1
time_series_predictions2 = TimeDistributed(Dense(1), name="p2")(h22) # custom 2
time_series_predictions3 = TimeDistributed(Dense(1), name="p3")(h23) # mse
time_series_predictions4 = TimeDistributed(Dense(1, activation='sigmoid'), name="p4")(h24) # logloss
time_series_predictions5 = TimeDistributed(Dense(nb_labels, activation='softmax'), name="p5")(h25) # cross
self.model = Model(input=financial_time_series_input,
output=[time_series_predictions1, time_series_predictions2,
time_series_predictions3, time_series_predictions4,
time_series_predictions5],
name="multi-task deep rnn for financial time series forecasting")
plot(self.model, to_file='model.png')
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