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2.2 KiB
Executable File
Vendored
2.2 KiB
Executable File
Vendored
usage: rnn [-d N_DAYS] [-i N_INPUTS] [--epochs N_EPOCHS] [-e S_END_DATE] [--batch_size N_BATCH_SIZE] [--xla_cpu] [--xla_gpu]
[--force_gpu_allow_growth {true,false,default}] [--loops N_LOOPS] [-v VALID_SPLIT] [--lr LR] [--no_shuffle] [-h]
Recurrent Neural Network
Due to the complexity of defining a model through command line, one can define it in: config_neural_network_models.txt
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':'relu'} }]
optional arguments:
-d N_DAYS, --days N_DAYS
prediction days.
-i N_INPUTS, --input N_INPUTS
number of days to use for prediction.
--epochs N_EPOCHS number of training epochs.
-e S_END_DATE, --end S_END_DATE
The end date (format YYYY-MM-DD) to select - Backtesting
--batch_size N_BATCH_SIZE
batch size for model fitting (use a power of 2)
--xla_cpu enable XLA for CPU (see https://www.tensorflow.org/xla)
--xla_gpu enable XLA for GPU (see https://www.tensorflow.org/xla)
--force_gpu_allow_growth {true,false,default}
true: GPU memory will grow as needed.
false: TensorFlow will allocate 100% of GPU memory.
default: usually the same as false, uses env/TensorFlow default
--loops N_LOOPS number of loops to iterate and train models
-v VALID_SPLIT, --valid VALID_SPLIT
Validation data split fraction
--lr LR Specify learning rate for optimizer.
--no_shuffle Specify if shuffling validation inputs.
-h, --help show this help message
