Add neural network models configs in separate file

config_neural_network_models.py

@@ -0,0 +1,57 @@
+# This configuration file allows to define the layers that constitute each
+#of the neural network models used by our MLP, RNN and LSTM models.
+# The first layer is the input layer, meaning that to it we add the input shape expected.
+#Note that we can still add the number of neurons on the first layer.
+# The last layer is the output layer, meaning that from it we have our prediction share price days
+#therefore, we don't define units for this layer as this is defined from the terminal directly.
+# There are several types of parameters available to configure your Neural Network model, and more on
+#these can be found in https://www.tensorflow.org/api_docs/python/tf/keras/layers
+# So far the following type of layers are allowed: Dense, SimpleRNN, LSTM, and Dropout
+
+# This is an on-going work, hence, feel free to request support for more types of layers
+
+# From the terminal, we are able to define:
+#   - The number of inputs used by the module
+#   - The number of training epochs
+#   - The frequency of data training (i.e. do we use all data, or every other day - may speed up training)
+#   - The preprocessing type: normalization, standardization, or none
+#   - The optimizer
+#   - The loss function
+
+MultiLayer_Perceptron \
+    = [ {'Dense': 
+                {'units':50, 'activation':'relu'} },
+        {'Dense': 
+                {'units':100, 'activation':'relu'} },
+        {'Dense': 
+                {'units':80, 'activation':'relu'} },
+        {'Dense': 
+                {'units':30, 'activation':'relu'} },
+        {'Dense': 
+                {'activation':'linear'} }]
+
+Recurrent_Neural_Network \
+    = [ {'SimpleRNN': 
+                {'units':100, 'activation':'linear', 'return_sequences':True} },
+        {'SimpleRNN': 
+                {'units':50, 'activation':'linear', 'return_sequences':True} },
+        {'Dropout': 
+                {'rate':0.2} },
+        {'SimpleRNN': 
+                {'units':21, 'activation':'linear', 'return_sequences':False} },
+        {'Dense': 
+                {'activation':'linear'} }]
+
+Long_Short_Term_Memory \
+    = [ {'LSTM': 
+                {'units':25, 'activation':'tanh', 'return_sequences':True} },
+        {'LSTM': 
+                {'units':50, 'activation':'tanh', 'return_sequences':True} },
+        {'LSTM': 
+                {'units':30, 'activation':'tanh', 'return_sequences':True} },
+        {'LSTM': 
+                {'units':20, 'activation':'tanh', 'return_sequences':True} },
+        {'LSTM': 
+                {'units':15, 'activation':'tanh', 'return_sequences':False} },
+        {'Dense': 
+                {'activation':'linear'} }]    

prediction_techniques/neural_networks.py

@@ -9,10 +9,10 @@ register_matplotlib_converters()
 from TimeSeriesCrossValidation import splitTrain
 
 from sklearn.preprocessing import MinMaxScaler, StandardScaler
-from tensorflow.keras.layers import Dense, Activation, Dropout
-from tensorflow.keras.layers import LSTM, SimpleRNN
 from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import RepeatVector, TimeDistributed
+from tensorflow.keras.layers import LSTM, SimpleRNN, Dense, Dropout, Activation, RepeatVector, TimeDistributed
+
+import config_neural_network_models as cfg_nn_models
 
 import os
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
@@ -109,19 +109,7 @@ def mlp(l_args, s_ticker, s_interval, df_stock):
         stock_y = np.reshape(stock_y, (stock_y.shape[0], stock_y.shape[1]))
 
         # Build Neural Network model
-        MultiLayer_Perceptron \
-            = [ {'Dense': 
-                        {'units':50, 'activation':'relu'} },
-                {'Dense': 
-                        {'units':100, 'activation':'relu'} },
-                {'Dense': 
-                        {'units':80, 'activation':'relu'} },
-                {'Dense': 
-                        {'units':30, 'activation':'relu'} },
-                {'Dense': 
-                        {'activation':'linear'} }]
-
-        model = build_neural_network_model(MultiLayer_Perceptron, ns_parser.n_inputs, ns_parser.n_days)
+        model = build_neural_network_model(cfg_nn_models.MultiLayer_Perceptron, ns_parser.n_inputs, ns_parser.n_days)
         model.compile(optimizer=ns_parser.s_optimizer, loss=ns_parser.s_loss)
 
         # Train our model
@@ -209,19 +197,7 @@ def rnn(l_args, s_ticker, s_interval, df_stock):
         stock_y = np.reshape(stock_y, (stock_y.shape[0], stock_y.shape[1], 1))
 
         # Build Neural Network model
-        Recurrent_Neural_Network \
-            = [{'SimpleRNN': 
-                        {'units':100, 'activation':'linear', 'return_sequences':True} },
-                {'SimpleRNN': 
-                        {'units':50, 'activation':'linear', 'return_sequences':True} },
-                {'Dropout': 
-                        {'rate':0.2} },
-                {'SimpleRNN': 
-                        {'units':21, 'activation':'linear', 'return_sequences':False} },
-                {'Dense': 
-                        {'activation':'linear'} }]
-
-        model = build_neural_network_model(Recurrent_Neural_Network, ns_parser.n_inputs, ns_parser.n_days)
+        model = build_neural_network_model(cfg_nn_models.Recurrent_Neural_Network, ns_parser.n_inputs, ns_parser.n_days)
         model.compile(optimizer=ns_parser.s_optimizer, loss=ns_parser.s_loss)
 
         # Train our model
@@ -309,21 +285,7 @@ def lstm(l_args, s_ticker, s_interval, df_stock):
         stock_y = np.reshape(stock_y, (stock_y.shape[0], stock_y.shape[1], 1))
 
         # Build Neural Network model
-        Long_Short_Term_Memory \
-            = [ {'LSTM': 
-                        {'units':25, 'activation':'tanh', 'return_sequences':True} },
-                {'LSTM': 
-                        {'units':50, 'activation':'tanh', 'return_sequences':True} },
-                {'LSTM': 
-                        {'units':30, 'activation':'tanh', 'return_sequences':True} },
-                {'LSTM': 
-                        {'units':20, 'activation':'tanh', 'return_sequences':True} },
-                {'LSTM': 
-                        {'units':15, 'activation':'tanh', 'return_sequences':False} },
-                {'Dense': 
-                        {'activation':'linear'} }]
-
-        model = build_neural_network_model(Long_Short_Term_Memory, ns_parser.n_inputs, ns_parser.n_days)
+        model = build_neural_network_model(cfg_nn_models.Long_Short_Term_Memory, ns_parser.n_inputs, ns_parser.n_days)
         model.compile(optimizer=ns_parser.s_optimizer, loss=ns_parser.s_loss)
 
         # Train our model