GAN Generative Adversarial 3

import keras
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
from __future__ import print_function
import numpy as np
np.random.seed(1337)
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.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D, UpSampling2D
from keras.utils import np_utils
from keras import backend as K
from keras.layers.normalization import BatchNormalization
from keras.callbacks import ModelCheckpoint,LearningRateScheduler
import os
os.environ["KERAS_BACKEND"] = "theano"
#os.environ["THEANO_FLAGS"]  = "device=gpu%d,lib.cnmem=0"%(random.randint(0,3))

(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
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')

sd=[]
class LossHistory(keras.callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.losses = [1,1]

    def on_epoch_end(self, batch, logs={}):
        self.losses.append(logs.get('loss'))
        sd.append(step_decay(len(self.losses)))
        print('learning rate:', step_decay(len(self.losses)))
        print('derivative of loss:', 2*np.sqrt((self.losses[-1])))

def my_init(shape, name=None):
    value = np.random.random(shape)
    return K.variable(value, name=name)

def step_decay(losses):
    if float(2*np.sqrt(np.array(history.losses[-1])))<1.6:
        lrate=0.006
        momentum=0.4
        decay_rate=0.0
        return lrate
    else:
        lrate=0.01
        return lrate

generator = Sequential()
generator.add(Convolution2D(20, 3,3,
                        border_mode='valid',
                        input_shape=input_shape))
generator.add(BatchNormalization(mode=2))
generator.add(Activation('relu'))
generator.add(UpSampling2D(size=(2, 2)))
generator.add(Convolution2D(20, 3, 3,
                            init='glorot_uniform'))
generator.add(BatchNormalization(mode=2))
generator.add(Activation('relu'))
generator.add(Convolution2D(20, 3, 3,init='glorot_uniform'))
generator.add(BatchNormalization(mode=2))
generator.add(Activation('relu'))
generator.add(MaxPooling2D(pool_size=(3,3)))
generator.add(Convolution2D(4, 3, 3,init='glorot_uniform'))
generator.add(BatchNormalization(mode=2))
generator.add(Activation('relu'))
generator.add(Reshape((28,28,1)))
generator.compile(loss='binary_crossentropy', optimizer='adam')
generator.summary()


############# DISCRIMINATOR

# 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]

history=LossHistory()
lrate=LearningRateScheduler(step_decay)

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'])

filepath="CNN_MNIST-{loss:.4f}.hdf5"
checkpoint_discr = ModelCheckpoint(filepath, monitor='binary_crossentropy', verbose=1, save_best_only=True)

discriminator.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=1,callbacks=[history,lrate,checkpoint_discr],
          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=(1, 28,28,1))

generator.summary()
discriminator.summary()

gan_level2 = discriminator(generator(gan_input))

GAN = Model(gan_input, gan_level2)
GAN.compile(loss='categorical_crossentropy', optimizer='adam')

filepath="CNN_STACKED-{loss:.4f}.hdf5"
checkpoint_stacked = ModelCheckpoint(filepath, monitor='binary_crossentropy', verbose=1, save_best_only=False)

GAN.fit(x_train.reshape((1,28,28,1)), Y_train.reshape((1,10)), batch_size=batch_size, nb_epoch=2,
          verbose=1,callbacks=[history,lrate,checkpoint_stacked])

filename = "CNN_STACKED-2.2547.hdf5"

GAN.load_weights(filename)
GAN.compile(loss='binary_crossentropy', optimizer='adam')

bb=GAN.predict(x_test.reshape((1,28,28,1)))
c=np.where(bb[0]==np.max(bb))[0]
Y_train[0][c]

### YESSSS
noise = np.random.uniform(0,1,size=[1,784]).reshape((1,28,28,1))
generated_images = generator.predict(noise)
plt.imshow(generated_images.reshape((28,28)),cmap='Greys')
plt.imshow(X_train.reshape((28,28)),cmap='Greys')

GAN.get_config()

GAN.summary()

GAN.get_weights()

from __future__ import print_function

import h5py

def print_structure(weight_file_path):
    f = h5py.File(weight_file_path)
    try:
        if len(f.attrs.items()):
            print("{} contains: ".format(weight_file_path))
            print("Root attributes:")
        for key, value in f.attrs.items():
            print("  {}: {}".format(key, value))

        if len(f.items())==0:
            return 

        for layer, g in f.items():
            print("  {}".format(layer))
            print("    Attributes:")
            for key, value in g.attrs.items():
                print("      {}: {}".format(key, value))

            print("    Dataset:")
            for p_name in g.keys():
                param = g[p_name]
                print("      {}: {}".format(p_name, np.array(param).shape))
    finally:
        f.close()
        
print_structure('CNN_STACKED-2.2547.hdf5')