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b71abcfbf4
* quick econ fix * black * keys and feature flags * terminal name 👀 * some more replacements * some more replacements * edit pyproject * gst -> openbb * add example portfolios back to git * Update api from gst * sorry. skipping some tests * another round of names * another round of test edits * Missed some .gst refs and update timezone * water mark stuff * Fixing Names in terminal.spec and name of GTFF_DEFAULTS to OBBFF_DEFAULTS * fix more GST to OpenBB Terminal * Logging : merge conflicts with main * Revert wrong files Co-authored-by: Andrew <andrew.kenreich@gmail.com> Co-authored-by: DidierRLopes <dro.lopes@campus.fct.unl.pt> Co-authored-by: Chavithra PARANA <chavithra@gmail.com>
71 lines
3.1 KiB
Python
71 lines
3.1 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": "relu"}},
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]
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Recurrent_Neural_Network = [
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{"SimpleRNN": {"units": 20, "activation": "relu", "return_sequences": True}},
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{"SimpleRNN": {"units": 10, "activation": "relu", "return_sequences": True}},
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{"Dropout": {"rate": 0.1}},
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{"SimpleRNN": {"units": 5, "activation": "relu", "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": "relu"}},
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]
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Convolutional = [
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{"Conv1D": {"filters": 20, "kernel_size": 10, "activation": "relu"}},
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{"MaxPool1D": {"pool_size": 2}},
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{"Conv1D": {"filters": 10, "kernel_size": 5, "activation": "relu"}},
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{"MaxPool1D": {"pool_size": 2}},
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{"Flatten": {}},
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{"Dense": {"activation": "relu"}},
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]
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# Optimizers : https://www.tensorflow.org/api_docs/python/tf/keras/optimizers
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# Adam, Adagrad, Adadelta, Adamax,Ftrl, Nadam, Rmsprop, Sgd
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Optimizer = "Adam"
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# Add early stopping to save time training when validation is plateaued
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# Can be set to large number or None to ignore
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Early_Stop_Patience = 10
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# Losses https://www.tensorflow.org/api_docs/python/tf/keras/losses
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# mae, mape, mse, msle, poisson, logcosh, kld, hinge, squared_hinge, huber
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Loss = "mae"
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# Preprocessing technique to use : https://scikit-learn.org/stable/modules/preprocessing.html
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# Options are:
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# minmax - Adjusts data to be between 0 and 1 - Most Used for Stocks
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# standardization - Standardizes data so that it has mean 0 and standard deviation = 1
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# normalization - Normalizes data so that L2 norm (sum x_i^2) = 1
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# none - No preprocessing
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Preprocess = "minmax"
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