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dcgan.py
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dcgan.py
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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 keras.backend as K
import numpy as np
from PIL import Image
import argparse
import math
import matplotlib.pyplot as plt
import os
image_dim_order = K.image_dim_ordering()
def generator_model():
# bulid the generator model, it is a model made up of UpSample and Convolution
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'))
if image_dim_order == 'th':
model.add(Reshape((128, 7, 7), input_shape=(128*7*7,)))
else:
model.add(Reshape((7, 7, 128), 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():
# build the discriminator model, it is one common convolutional neural network
model = Sequential()
# different backend has different image dim order, so we need to judge first.
if image_dim_order == 'th':
input_shape = (1,28,28)
else:
input_shape = (28,28,1)
model.add(Convolution2D(
64, 5, 5,
border_mode='same',
input_shape=input_shape))
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)
# when train the generator, the discriminator model cannot be trained
discriminator.trainable = False
model.add(discriminator)
return model
def combine_images(generated_images):
# combine one batch of images to one big image
num = generated_images.shape[0]
width = int(math.sqrt(num))
height = int(math.ceil(float(num)/width))
if image_dim_order == 'th':
shape = generated_images.shape[2:]
else:
shape = generated_images.shape[1:3]
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
if image_dim_order == 'th':
image[i*shape[0]:(i+1)*shape[0], j*shape[1]:(j+1)*shape[1]] = \
img[0, :, :]
else:
image[i*shape[0]:(i+1)*shape[0], j*shape[1]:(j+1)*shape[1]] = \
img[:, :, 0]
return image
def train(BATCH_SIZE, epoch_num):
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = (X_train.astype(np.float32) - 127.5)/127.5
if image_dim_order == 'th':
X_train = X_train.reshape((X_train.shape[0], 1) + X_train.shape[1:])
else:
X_train = X_train.reshape((X_train.shape[0], ) + X_train.shape[1:] + (1,))
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))
false_negative_list = []
false_positive_list = []
total_accuracy_list = []
for epoch in range(epoch_num):
print('~'*70)
print("Epoch is", epoch)
print("Number of batches", int(X_train.shape[0]/BATCH_SIZE))
batches_num = int(X_train.shape[0]/BATCH_SIZE)
# we train the generator first
for index in range(batches_num):
for i in range(BATCH_SIZE):
noise[i, :] = np.random.uniform(-1, 1, 100)
# note that when train the generator, the discriminator is not trainalbe
discriminator.trainable = False
g_loss = discriminator_on_generator.train_on_batch(
noise, [1] * BATCH_SIZE)
discriminator.trainable = True
print("epoch %d/%d batch %d/%d g_loss : %f" % (epoch+1, epoch_num,index, batches_num, g_loss))
# leverage the generative images and natural images to train the discriminator
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]
generated_images = generator.predict(noise, verbose=0)
# save the generative images on the process of training regularly
if index % 80 == 0:
image = combine_images(generated_images)
image = image*127.5+127.5
Image.fromarray(image.astype(np.uint8)).save('images/'+
str(epoch)+"_"+str(index)+".png")
# create the trainset of the discriminator
X = np.concatenate((image_batch, generated_images))
y = [1] * BATCH_SIZE + [0] * BATCH_SIZE
d_loss = discriminator.train_on_batch(X, y)
print("epoch %d/%d batch %d/%d d_loss : %f" % (epoch+1, epoch_num, index, batches_num, d_loss))
# calculate the accuracy of the generator and discriminator
if index%20 == 0:
# to test the generator and discriminator's ability
y_pred = discriminator.predict(X)
false_positive = float(np.sum(y_pred[BATCH_SIZE:]>0.5))/BATCH_SIZE
true_negative = float(np.sum(y_pred[BATCH_SIZE:]<0.5))/BATCH_SIZE
true_positive = float(np.sum(y_pred[:BATCH_SIZE]>0.5))/BATCH_SIZE
false_negative = float(np.sum(y_pred[:BATCH_SIZE]<0.5))/BATCH_SIZE
total_accuracy = (true_positive + true_negative) /2
print('*'*10)
print("the ratio that generator deceive discrimator successly is :%f"%false_positive)
print("the ratio that discrimator fail to discrimate true/natural mnist :%f"%false_negative)
print("the totoal accuracy of the discriminator is :%f"%total_accuracy)
false_negative_list.append(false_negative)
false_positive_list.append(false_positive)
total_accuracy_list.append(total_accuracy)
if index % 20 == 0:
generator.save_weights('generator', True)
discriminator.save_weights('discriminator', True)
# when training process is finished, plt the accuracy of generator and discriminator
plt.figure()
plt.title("the log of the training process")
plt.plot(range(len(false_negative_list)), false_negative_list,'g-',label='D failed to discrimate natural image')
plt.plot(range(len(false_positive_list)), false_positive_list,'r-', label='G deceive D successly')
plt.plot(range(len(total_accuracy_list)), total_accuracy_list,'b-',label='total accuracy of D')
plt.legend()
plt.show()
plt.savefig('log.png')
def generate(BATCH_SIZE, nice=False):
generator = generator_model()
generator.compile(loss='binary_crossentropy', optimizer="SGD")
generator.load_weights('generator')
# if nice is true, choose the 5% best generative images for storage, else the original generative images
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)
if image_dim_order == 'th':
nice_images = np.zeros((BATCH_SIZE, 1) +
(generated_images.shape[2:]), dtype=np.float32)
else:
nice_images = np.zeros((BATCH_SIZE, ) +
(generated_images.shape[1:3]) + (1,), dtype=np.float32)
for i in range(int(BATCH_SIZE)):
idx = int(pre_with_index[i][1])
if image_dim_order == 'th':
nice_images[i, 0, :, :] = generated_images[idx, 0, :, :]
else:
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)
parser.add_argument("--batch_size", type=int, default=128)
parser.add_argument("--nice", dest="nice", action="store_true")
parser.add_argument("--epoch_num",type=int,default=100)
parser.set_defaults(nice=False)
args = parser.parse_args()
return args
if __name__ == "__main__":
# path to save generative images
if not os.path.exists('images'):
os.mkdir('images')
args = get_args()
if args.mode == "train":
print ('totol epochs of the train:'+str(args.epoch_num))
train(BATCH_SIZE=args.batch_size , epoch_num=args.epoch_num)
elif args.mode == "generate":
generate(BATCH_SIZE=args.batch_size, nice=args.nice)