UCR_looping_runner.py

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
from random import Random
from tensorflow.contrib import rnn
from tensorflow.python.framework import ops
import os
from UCR_time_series_data_handler import get_dataset_with_series_size, direc
import lstm_mnist



import single_layer_lstm_looper_UCR

def get_immediate_subdirectories(a_dir):
    return [name for name in os.listdir(a_dir)
            if os.path.isdir(os.path.join(a_dir, name))]

def get_dataset_list():
    return get_immediate_subdirectories(direc)

def run_with_dataset(dataset):
    pass

def modify_config_wtih_current_dataset(config):
    pass


run_with_config = single_layer_lstm_looper_UCR.run_with_config
config = single_layer_lstm_looper_UCR.config

def looper_runner(UCR_dataset_name):
    run_with_config(config)


if __name__ == '__main__':
    print (get_dataset_list())
    exit(0)

    run_with_config = single_layer_lstm_looper_UCR.run_with_config
    config = single_layer_lstm_looper_UCR.config

    for learning_rate in [0.005, 0.0025, 0.003, 0.0005]:  # 1, 0.0025, 0.002]:  # [0.01, 0.007, 0.001, 0.0007, 0.0001]:
        for decay in [0.9]:  # [0.005, 0.01]:
            for bn_enabled in [True, False]:
                for n_stacked in [2, 3, 4, 6]:  # 2 3 6
                    for epoch_count in [200, 300, 350]:
                        config.training_epochs = epoch_count
                        config.tensor_board_logging_enabled = False  # should be always False, log summary folder gets impacted by mulitple runs
                        config.n_stacked_layers = n_stacked
                        config.batch_norm_enabled = bn_enabled
                        config.learning_rate = learning_rate
                        config.decay = decay
                        run_with_config(config)  # , trX, trY, teX, teY)