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