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GAN Generative Adversarial 1
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GAN Generative Adversarial 1
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import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from keras.layers import Input, Dense, Lambda
from keras.models import Model
from keras import backend as K
from keras import objectives
from keras.datasets import mnist
from keras.layers.core import Reshape
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
x_train=x_train[1:2]
x_test=x_test[1:2]
y_train=y_train[1:2]
y_test=y_test[1:2]
batch_size = 1
original_dim = 784
latent_dim = 2
intermediate_dim = 256
nb_epoch = 2
epsilon_std = 1.0
x = Input(batch_shape=(batch_size, original_dim))
h = Dense(intermediate_dim, activation='relu')(x)
z_mean = Dense(latent_dim)(h)
z_log_var = Dense(latent_dim)(h)
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(batch_size, latent_dim), mean=0.,
std=epsilon_std)
return z_mean + K.exp(z_log_var / 2) * epsilon
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])
decoder_h = Dense(intermediate_dim, activation='relu')
decoder_mean = Dense(original_dim, activation='sigmoid')
h_decoded = decoder_h(z)
x_decoded_mean = decoder_mean(h_decoded)
##### KEY HERE
x_decoded_mean2=Reshape([28,28,1])(x_decoded_mean)
def generator_loss(x, x_decoded_mean):
xent_loss = original_dim * objectives.binary_crossentropy(x, x_decoded_mean)
kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
return xent_loss + kl_loss
generator = Model(x, x_decoded_mean2)
generator.compile(optimizer='rmsprop', loss=generator_loss)
generator.summary()
#############
from __future__ import print_function
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
batch_size = 1
nb_classes = 10
nb_epoch = 1
# input image dimensions
img_rows, img_cols = 28, 28
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
pool_size = (2, 2)
# convolution kernel size
kernel_size = (3, 3)
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
if K.image_dim_ordering() == 'th':
X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows,img_cols)
else:
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
X_train = X_train.astype('float32')[1:2]
X_test = X_test.astype('float32')[1:2]
X_train /= 255
X_test /= 255
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)[1:2]
Y_test = np_utils.to_categorical(y_test, nb_classes)[1:2]
discriminator = Sequential()
discriminator.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
border_mode='valid',
input_shape=input_shape))
discriminator.add(Activation('relu'))
discriminator.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
discriminator.add(Activation('relu'))
discriminator.add(MaxPooling2D(pool_size=pool_size))
discriminator.add(Dropout(0.25))
discriminator.add(Flatten())
discriminator.add(Dense(64))
discriminator.add(Activation('relu'))
discriminator.add(Dropout(0.5))
discriminator.add(Dense(nb_classes))
discriminator.add(Activation('sigmoid'))
discriminator.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
discriminator.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=10,
verbose=1, validation_data=(X_test, Y_test))
score = discriminator.evaluate(X_test, Y_test, verbose=2)
print('Test score:', score[0])
print('Test accuracy:', score[1])
def not_train(net, val):
net.trainable = val
for k in net.layers:
k.trainable = val
not_train(discriminator, False)
gan_input = Input(batch_shape=(batch_size, original_dim))
gan_level2 = discriminator(generator(gan_input))
GAN = Model(gan_input, gan_level2)
GAN.compile(loss='categorical_crossentropy', optimizer='adam')
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
x_train=x_train[1:2]
x_test=x_test[1:2]
y_train=y_train[1:2]
y_test=y_test[1:2]
discriminator.summary()
GAN.fit(x_train, Y_train.reshape((1,10)), batch_size=batch_size, nb_epoch=10,
verbose=1)
bb=GAN.predict(x_test)
c=np.where(bb[0]==np.max(bb))[0]
Y_train[c]
plt.imshow(x_train[c].reshape((28,28)),cmap='Greys_r')