noise.py

#TODO
# shuffle data when training
# make test data

# This code is for checking 3-3 layer autoencoder performace for all noise levels. 
import sys
import numpy as np
from keras.layers import Input, Dense
from keras.models import Model, Sequential
from keras import regularizers
import random
from latex import *
from keras.callbacks import TensorBoard
x_train = np.load('train_l1000.npy')
x_test = np.load('test_l1000.npy')

def add_encoder(model, n):
    encoding_dim = 2**(5+n-1) # 
    model.add(Dense(encoding_dim, activation='relu', input_shape=(943,)))
    for l in range(n-1):
        encoding_dim /= 2
        model.add(Dense(int(encoding_dim), activation='relu'))
    return model

def add_decoder(model, n):
    decoding_dim = 2**6    
    for l in range(n-1):
        model.add(Dense(int(decoding_dim), activation='relu'))
        decoding_dim *= 2
    model.add(Dense(943, activation='sigmoid'))
    return model

def print_model_layers(model):
    for layer in model.layers:
        print(layer.output_shape)
        
def autoencoder_num_layers(n_encoder_layers, n_decoder_layers):
    # 2921*943= 2754503
    model = Sequential()
    model = add_encoder(model, n_encoder_layers)
    print(type(model))
    model = add_decoder(model, n_decoder_layers)
    print(type(model))
    print_model_layers(model)
    return model
    
for i in range(3,4):
    n_encoder_layers = i
    n_decoder_layers = i
    autoencoder = autoencoder_num_layers(n_encoder_layers, n_decoder_layers)

    autoencoder.compile(optimizer='adadelta', loss='mean_squared_error')

    MSE = [] # mean square error

    for r in range(0,11): # noise factor
        noise_factor = r * 0.1
        x_train_noisy = x_train + noise_factor *\
        np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)
        x_test_noisy = x_test + noise_factor *\
            np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)
        print('bw noisy and train',((x_train - x_train_noisy)**2).mean(axis=None) )
        autoencoder.fit(x_train_noisy, x_train, epochs=10, batch_size=256,\
                        shuffle=True, validation_data=(x_test_noisy, x_test),
                        callbacks=[TensorBoard(log_dir='/localdisk/pshar10/tb', histogram_freq=0, write_graph=True)])

        decoded_profile = autoencoder.predict(x_train)

        mse = ((x_train - decoded_profile)**2).mean(axis=None)
        MSE.append(mse)        
            
    np.save('./data/debugged_noise/'+str(n_encoder_layers)+'_'+str(n_decoder_layers)+'.npy', MSE)
    write_g_tex_file_1D(MSE, './data/debugged_noise/'+str(n_encoder_layers)+'_'+str(n_decoder_layers)+'_g.tex', 'a')
    write_t_tex_file_1D(MSE, './data/debugged_noise/'+str(n_encoder_layers)+'_'+str(n_decoder_layers)+'_t.tex', 'a')
    print('files written at', './data/debugged_noise/')