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feat(Data): added various data loading config options, walk forward method draft (#9)

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* feat(Eval): added format_data_for_backtest()

* feat(Data): added many configurable parameters to load_files to reduce boilerplate and prepare for HPO

* feat(Core): added walk forward method of training/testing

* fix(Model): remove the unnecessary softmax activation from the keras models

* feat(Core): added walk_forward_train_test()
This commit is contained in:
Mark Aron Szulyovszky
2021-12-01 09:28:24 +01:00
committed by GitHub
parent d4676e099b
commit 7aedb91069
8 changed files with 678 additions and 75 deletions
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keras_models/classification.py
+3 -3
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@@ -9,7 +9,7 @@ def create_basic_lstm_model(input_shape, num_classes):
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(units = 32, activation = 'sigmoid'))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(units = num_classes, activation = 'softmax'))
model.add(keras.layers.Dense(units = num_classes, activation = 'linear'))
return model
def create_basic_cnn_model(input_shape, num_classes):
@@ -24,7 +24,7 @@ def create_basic_cnn_model(input_shape, num_classes):
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.ReLU())
model.add(keras.layers.GlobalAveragePooling1D())
model.add(keras.layers.Dense(num_classes, activation="softmax"))
model.add(keras.layers.Dense(num_classes, activation="linear"))
return model
@@ -95,7 +95,7 @@ def create_resnet_cnn_model(input_shape, num_classes):
gap_layer = keras.layers.GlobalAveragePooling1D()(output_block_3)
output_layer = keras.layers.Dense(num_classes, activation='softmax')(gap_layer)
output_layer = keras.layers.Dense(num_classes, activation='linear')(gap_layer)
model = keras.models.Model(inputs=input_layer, outputs=output_layer)