``` 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](/config_neural_network_models.py) ``` 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 ``` ![rnn](https://user-images.githubusercontent.com/25267873/108604940-d0d12700-73a8-11eb-837e-a5aa128942d9.png)