rnns.py 文件源码

def generate_random_hyperparams(lr_min, lr_max, K_min, K_max, num_layers_min, num_layers_max,load_hparams):
    """This function generates random hyper-parameters for hyperparameter search"""

    #this is for new random parameters
    if not load_hparams[0]:
        lr_exp = np.random.uniform(lr_min, lr_max)
        lr = 10**(lr_exp)
        K = np.random.choice(np.arange(K_min, K_max+1),1)[0]
        num_layers = np.random.choice(np.arange(num_layers_min, num_layers_max + 1),1)[0]
        #momentum_exp = np.random.uniform(-8,0) 
        momentum = np.random.uniform(0,1) #(2**momentum_exp)

    #this loads hyperparameters from an existing file
    else:
        exp_data = np.load('experiment_data/nmf_data_timit_model_bi_mod_lstm_diag_to_full_device_cpu:0_1490813245.npy')[load_hparams[1]]
        lr = exp_data['LR']
        K = exp_data['K']
        num_layers = exp_data['num_layers']
        try:
            momentum = exp_data['num_layers']
        except:
            momentum = None

    return lr, K, num_layers, momentum