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laptop.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Jan 21 08:02:50 2017
@author: abhin
"""
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 20 16:31:36 2017
@author: abhin
"""
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Reshape
from keras.layers.core import Activation
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import UpSampling2D
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.core import Flatten
from keras.optimizers import SGD
from keras.datasets import mnist
import resize_laptop
import numpy as np
from PIL import Image
import argparse
import math
import theano
#theano.config.blas.ldflags = '-LC:\\openblas -lopenblas'
def generator_model():
model = Sequential()
model.add(Dense(input_dim=100, output_dim=1024))
model.add(Activation('tanh'))
model.add(Dense(128*7*7))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Reshape((128, 7, 7), input_shape=(128*7*7,)))
model.add(UpSampling2D(size=(2, 2)))
model.add(Convolution2D(64, 5, 5, border_mode='same'))
model.add(Activation('tanh'))
model.add(UpSampling2D(size=(2, 2)))
model.add(Convolution2D(1, 5, 5, border_mode='same'))
model.add(Activation('tanh'))
return model
def discriminator_model():
model = Sequential()
model.add(Convolution2D(
64, 5, 5,
border_mode='same',
input_shape=(1, 28, 28)))
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(128, 5, 5))
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('tanh'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
return model
def generator_containing_discriminator(generator, discriminator):
model = Sequential()
model.add(generator)
discriminator.trainable = False
model.add(discriminator)
return model
def combine_images(generated_images):
num = generated_images.shape[0]
width = int(math.sqrt(num))
height = int(math.ceil(float(num)/width))
shape = generated_images.shape[2:]
image = np.zeros((height*shape[0], width*shape[1]),
dtype=generated_images.dtype)
for index, img in enumerate(generated_images):
i = int(index/width)
j = index % width
image[i*shape[0]:(i+1)*shape[0], j*shape[1]:(j+1)*shape[1]] = \
img[0, :, :]
return image
def train(BATCH_SIZE):
X_train = resize_laptop.load()
# (X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = (X_train.astype(np.float32) - 127.5)/127.5
print X_train.shape
X_train = X_train.reshape((X_train.shape[0], 1) + X_train.shape[1:])
print X_train.shape
discriminator = discriminator_model()
generator = generator_model()
discriminator_on_generator = \
generator_containing_discriminator(generator, discriminator)
d_optim = SGD(lr=0.0005, momentum=0.9, nesterov=True)
g_optim = SGD(lr=0.0005, momentum=0.9, nesterov=True)
generator.compile(loss='binary_crossentropy', optimizer="SGD")
discriminator_on_generator.compile(
loss='binary_crossentropy', optimizer=g_optim)
discriminator.trainable = True
discriminator.compile(loss='binary_crossentropy', optimizer=d_optim)
noise = np.zeros((BATCH_SIZE, 100))
for epoch in range(200):
print("Epoch is", epoch)
print("Number of batches", int(X_train.shape[0]/BATCH_SIZE))
for index in range(int(X_train.shape[0]/BATCH_SIZE)):
for i in range(BATCH_SIZE):
noise[i, :] = np.random.uniform(-1, 1, 100)
image_batch = X_train[index*BATCH_SIZE:(index+1)*BATCH_SIZE]
# print image_batch.shape
generated_images = generator.predict(noise, verbose=0)
# print generated_images.shape
if index % 20 == 0:
image = combine_images(generated_images)
image = image*127.5+127.5
Image.fromarray(image.astype(np.uint8)).save(
str(epoch)+"_"+str(index)+".png")
X = np.concatenate((image_batch, generated_images))
y = [1] * BATCH_SIZE + [0] * BATCH_SIZE
d_loss = discriminator.train_on_batch(X, y)
print("batch %d d_loss : %f" % (index, d_loss))
for i in range(BATCH_SIZE):
noise[i, :] = np.random.uniform(-1, 1, 100)
discriminator.trainable = False
g_loss = discriminator_on_generator.train_on_batch(
noise, [1] * BATCH_SIZE)
discriminator.trainable = True
print("Epoch:",epoch)
print("batch %d g_loss : %f" % (index, g_loss))
if index % 10 == 9:
generator.save_weights('generator_laptop', True)
discriminator.save_weights('discriminator_laptop', True)
def generate(BATCH_SIZE, nice=False):
generator = generator_model()
generator.compile(loss='binary_crossentropy', optimizer="SGD")
generator.load_weights('generator')
if nice:
discriminator = discriminator_model()
discriminator.compile(loss='binary_crossentropy', optimizer="SGD")
discriminator.load_weights('discriminator')
noise = np.zeros((BATCH_SIZE*20, 100))
for i in range(BATCH_SIZE*20):
noise[i, :] = np.random.uniform(-1, 1, 100)
generated_images = generator.predict(noise, verbose=1)
d_pret = discriminator.predict(generated_images, verbose=1)
index = np.arange(0, BATCH_SIZE*20)
index.resize((BATCH_SIZE*20, 1))
pre_with_index = list(np.append(d_pret, index, axis=1))
pre_with_index.sort(key=lambda x: x[0], reverse=True)
nice_images = np.zeros((BATCH_SIZE, 1) +
(generated_images.shape[2:]), dtype=np.float32)
for i in range(int(BATCH_SIZE)):
idx = int(pre_with_index[i][1])
nice_images[i, 0, :, :] = generated_images[idx, 0, :, :]
image = combine_images(nice_images)
else:
noise = np.zeros((BATCH_SIZE, 100))
for i in range(BATCH_SIZE):
noise[i, :] = np.random.uniform(-1, 1, 100)
generated_images = generator.predict(noise, verbose=1)
image = combine_images(generated_images)
image = image*127.5+127.5
Image.fromarray(image.astype(np.uint8)).save(
"generated_image.png")
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--mode", type=str, default = "train")
parser.add_argument("--batch_size", type=int, default=20)
#parser.add_argument("--batch_size", type=int, default=1)
parser.add_argument("--nice", dest="nice", action="store_true")
parser.set_defaults(nice=False)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = get_args()
train(BATCH_SIZE=args.batch_size)
#generate(BATCH_SIZE=args.batch_size, nice=args.nice)
# if args.mode == "train":
# train(BATCH_SIZE=args.batch_size)
# elif args.mode == "generate":
# generate(BATCH_SIZE=args.batch_size, nice=args.nice)