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GAN_EEG.py
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GAN_EEG.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Oct 8 17:12:33 2019
@author: sb00747428
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import os
import time
import numpy as np
import matplotlib.pyplot as plt
from read_data import get_data
from signalProcessing import run_sig_processing
from tensorflow import keras
import pandas as pd
import matplotlib.pyplot as plt
"""Collect all the data"""
def arrange_data(data, labels):
output_data = list()
output_labels = list()
for idx in range(len(data)):
for segment in data[idx]:
output_data.append(np.expand_dims(segment, axis=2))
if labels[idx][0] == 1:
output_labels.append(0)
else:
output_labels.append(1)
output_data = np.array(output_data)
output_labels = np.array(output_labels)
return output_data, output_labels
def run_classification(data, labels, session1=(1, 2, 3), session2=(4,5)):
for subject in data:
# if subject == 'subject1': continue
input_data = list()
target_labels = list()
x_test = list()
y_test = list()
# combine trials data of target session
[input_data.extend(data[subject]["session" + str(idx)]['input data']) for idx in session1]
[target_labels.extend(labels[subject]["session" + str(idx)]) for idx in session1]
input_data = np.array(input_data)
target_labels = np.array(target_labels)
[x_test.extend(data[subject]["session" + str(idx)]['input data']) for idx in session2]
[y_test.extend(labels[subject]["session" + str(idx)]) for idx in session2]
test_data = np.array(x_test)
test_labels = np.array(y_test)
train_data, train_labels = arrange_data(input_data, target_labels)
test_data, test_labels = arrange_data(x_test, y_test)
train_data = train_data.reshape(train_data.shape[0],train_data.shape[1],train_data.shape[2],train_data.shape[4])
test_data = test_data.reshape(test_data.shape[0],test_data.shape[1],test_data.shape[2],test_data.shape[4])
size_y, size_x = train_data[0].shape[0:2]
return train_data, test_data, size_y, size_x, test_labels, train_labels
"""-----------------"""
'''downsample'''
def downsample(filters, size, apply_batchnorm=True):
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2D(filters, size, strides=2, padding='same',
kernel_initializer=initializer, use_bias=False))
if apply_batchnorm:
result.add(tf.keras.layers.BatchNormalization())
result.add(tf.keras.layers.LeakyReLU())
return result
'''------------------------------------------------------'''
'''Upsample'''
def upsample(filters, size, apply_dropout=False):
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2DTranspose(filters, size, strides=2,
padding='same',
kernel_initializer=initializer,
use_bias=False))
result.add(tf.keras.layers.BatchNormalization())
if apply_dropout:
result.add(tf.keras.layers.Dropout(0.5))
result.add(tf.keras.layers.ReLU())
return result
"""-----------------"""
'''Building Generator'''
def Generator():
down_stack = [
downsample(64, 4, apply_batchnorm=False), # (bs, 128, 128, 64)
downsample(128, 4), # (bs, 64, 64, 128)
downsample(256, 4), # (bs, 32, 32, 256)
# downsample(512, 4), # (bs, 16, 16, 512)
# downsample(512, 4), # (bs, 8, 8, 512)
# downsample(512, 4), # (bs, 4, 4, 512)
]
up_stack = [
# upsample(512, 4, apply_dropout=True), # (bs, 4, 4, 1024)
# upsample(512, 4, apply_dropout=True), # (bs, 8, 8, 1024)
# upsample(512, 4), # (bs, 16, 16, 1024)
upsample(256, 4), # (bs, 32, 32, 512)
upsample(128, 4), # (bs, 64, 64, 256)
upsample(64, 4), # (bs, 128, 128, 128)
]
initializer = tf.random_normal_initializer(0., 0.02)
last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 4,
strides=2,
padding='same',
kernel_initializer=initializer,
activation='tanh') # (bs, 256, 256, 3)
concat = tf.keras.layers.Concatenate()
inputs = tf.keras.layers.Input(shape=[40,32,3])
x = inputs
# Downsampling through the model
skips = []
for down in down_stack:
x = down(x)
skips.append(x)
skips = reversed(skips[:-1])
# Upsampling and establishing the skip connections
for up, skip in zip(up_stack, skips):
x = up(x)
x = concat([x, skip])
x = last(x)
return tf.keras.Model(inputs=inputs, outputs=x)
'''--------------------------------------'''
'''Building discriminator'''
def Discriminator():
initializer = tf.random_normal_initializer(0., 0.02)
inp = tf.keras.layers.Input(shape=[None, None, 3], name='input_image')
tar = tf.keras.layers.Input(shape=[None, None, 3], name='target_image')
x = tf.keras.layers.concatenate([inp, tar]) # (bs, 256, 256, channels*2)
down1 = downsample(64, 4, False)(x) # (bs, 128, 128, 64)
down2 = downsample(128, 4)(down1) # (bs, 64, 64, 128)
down3 = downsample(256, 4)(down2) # (bs, 32, 32, 256)
zero_pad1 = tf.keras.layers.ZeroPadding2D()(down3) # (bs, 34, 34, 256)
conv = tf.keras.layers.Conv2D(512, 4, strides=1,
kernel_initializer=initializer,
use_bias=False)(zero_pad1) # (bs, 31, 31, 512)
batchnorm1 = tf.keras.layers.BatchNormalization()(conv)
leaky_relu = tf.keras.layers.LeakyReLU()(batchnorm1)
zero_pad2 = tf.keras.layers.ZeroPadding2D()(leaky_relu) # (bs, 33, 33, 512)
last = tf.keras.layers.Conv2D(1, 4, strides=1,
kernel_initializer=initializer)(zero_pad2) # (bs, 30, 30, 1)
return tf.keras.Model(inputs=[inp, tar], outputs=last)
'''---------------'''
'''Define loss'''
def discriminator_loss(disc_real_output, disc_generated_output):
real_loss = loss_object(tf.ones_like(disc_real_output), disc_real_output)
generated_loss = loss_object(tf.zeros_like(disc_generated_output), disc_generated_output)
total_disc_loss = real_loss + generated_loss
return total_disc_loss
def generator_loss(disc_generated_output, gen_output, target):
gan_loss = loss_object(tf.ones_like(disc_generated_output), disc_generated_output)
# mean absolute error
l1_loss = tf.reduce_mean(tf.abs(target - gen_output))
total_gen_loss = gan_loss + (LAMBDA * l1_loss)
return total_gen_loss
'''-----------'''
@tf.function
def train_step(input_image, target):
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
gen_output = generator(input_image, training=True)
disc_real_output = discriminator([input_image, target], training=True)
disc_generated_output = discriminator([input_image, gen_output], training=True)
gen_loss = generator_loss(disc_generated_output, gen_output, target)
disc_loss = discriminator_loss(disc_real_output, disc_generated_output)
generator_gradients = gen_tape.gradient(gen_loss,
generator.trainable_variables)
discriminator_gradients = disc_tape.gradient(disc_loss,
discriminator.trainable_variables)
generator_optimizer.apply_gradients(zip(generator_gradients,
generator.trainable_variables))
discriminator_optimizer.apply_gradients(zip(discriminator_gradients,
discriminator.trainable_variables))
def generate_images(model, test_input, tar):
# the training=True is intentional here since
# we want the batch statistics while running the model
# on the test dataset. If we use training=False, we will get
# the accumulated statistics learned from the training dataset
# (which we don't want)
prediction = model(test_input, training=True)
plt.figure(figsize=(15,15))
display_list = [test_input[0], tar[0], prediction[0]]
title = ['Input Image', 'Ground Truth', 'Predicted Image']
for i in range(3):
plt.subplot(1, 3, i+1)
plt.title(title[i])
# getting the pixel values between [0, 1] to plot it.
plt.imshow(display_list[i] * 0.5 + 0.5)
plt.axis('off')
plt.show()
def fit(train_ds, epochs, test_ds):
for epoch in range(epochs):
start = time.time()
# Train
for input_image, target in train_ds:
train_step(input_image, target)
clear_output(wait=True)
# Test on the same image so that the progress of the model can be
# easily seen.
for example_input, example_target in test_ds.take(1):
generate_images(generator, example_input, example_target)
print ('Time taken for epoch {} is {} sec\n'.format(epoch + 1,
time.time()-start))
if __name__ == '__main__':
# '''
OUTPUT_CHANNELS = 3
BUFFER_SIZE = 400
BATCH_SIZE = 1
IMG_WIDTH = 32
IMG_HEIGHT = 40
EPOCHS = 150
data_src = r"Y:\Sujit Roy\data1"
labels_src = r"Y:\Sujit Roy\labels1"
generator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
loss_object = tf.keras.losses.BinaryCrossentropy(from_logits=True)
LAMBDA = 100
#
# band_type: 0: band pass feature, 1: AR PSD feature, 2: extend band
'''load and prepare data'''
data, labels = run_sig_processing(data_src, labels_src, band_type=2)
train_data, test_data, size_y, size_x, test_labels, train_labels = run_classification(data, labels)
'''-----------------------------'''
'''playing with generator'''
generator = Generator()
xxx= train_data[0].astype(np.float32)
gen_output = generator(xxx[tf.newaxis,...], training=False)
plt.imshow(gen_output[0,...])
'''-----------------------------'''
'''playing with discriminator'''
discriminator = Discriminator()
disc_out = discriminator([inp[tf.newaxis,...], gen_output], training=False)
plt.imshow(disc_out[0,...,-1], vmin=-20, vmax=20, cmap='RdBu_r')
plt.colorbar()
'''----------------------------'''
fit(train_data, EPOCHS, test_data)