Deep-Learning-models/GAN.py at master · SergiosKar/Deep-Learning-models · GitHub

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import numpy as np
from PIL import Image

import math

from tensorflow.contrib.keras.python.keras.datasets import mnist
from tensorflow.contrib.keras.python.keras.layers import Dense, Activation, BatchNormalization, Reshape, UpSampling2D, \
    Conv2D, MaxPooling2D, Flatten
from tensorflow.contrib.keras.python.keras.models import Sequential
from tensorflow.contrib.keras.python.keras.optimizers import SGD


def generator_model():
    model = Sequential()
    model.add(Dense(1024,input_dim=100 ))
    model.add(Activation('tanh'))
    model.add(Dense(128*7*7))
    model.add(BatchNormalization())
    model.add(Activation('tanh'))
    model.add(Reshape((7, 7, 128), input_shape=(128*7*7,)))
    model.add(UpSampling2D(size=(2, 2)))
    model.add(Conv2D(64, (5, 5), padding='same'))
    model.add(Activation('tanh'))
    model.add(UpSampling2D(size=(2, 2)))
    model.add(Conv2D(1, (5, 5), padding='same'))
    model.add(Activation('tanh'))
    return model


def discriminator_model():
    model = Sequential()
    model.add(Conv2D(64, (5, 5),padding='same',input_shape=(28, 28, 1)) )
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(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(g, d):
    model = Sequential()
    model.add(g)
    d.trainable = False
    model.add(d)
    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[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
        image[i*shape[0]:(i+1)*shape[0], j*shape[1]:(j+1)*shape[1]] = \
            img[:, :, 0]
    return image


BATCH_SIZE=128

def train():
    (X_train, y_train), (X_test, y_test) = mnist.load_data()
    X_train = (X_train.astype(np.float32) - 127.5)/127.5
    X_train = X_train[:, :, :, None]
    X_test = X_test[:, :, :, None]
    # X_train = X_train.reshape((X_train.shape, 1) + X_train.shape[1:])
    d = discriminator_model()
    g = generator_model()
    d_on_g = generator_containing_discriminator(g, d)
    d_optim = SGD(lr=0.0005, momentum=0.9, nesterov=True)
    g_optim = SGD(lr=0.0005, momentum=0.9, nesterov=True)
    g.compile(loss='binary_crossentropy', optimizer="SGD")
    d_on_g.compile(loss='binary_crossentropy', optimizer=g_optim)
    d.trainable = True
    d.compile(loss='binary_crossentropy', optimizer=d_optim)
    for epoch in range(100):
        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)):
            noise = np.random.uniform(-1, 1, size=(BATCH_SIZE, 100))
            image_batch = X_train[index*BATCH_SIZE:(index+1)*BATCH_SIZE]
            generated_images = g.predict(noise, verbose=0)
            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 = d.train_on_batch(X, y)
            print("batch %d d_loss : %f" % (index, d_loss))
            noise = np.random.uniform(-1, 1, (BATCH_SIZE, 100))
            d.trainable = False
            g_loss = d_on_g.train_on_batch(noise, [1] * BATCH_SIZE)
            d.trainable = True
            print("batch %d g_loss : %f" % (index, g_loss))
            if index % 10 == 9:
                g.save_weights('generator', True)
                d.save_weights('discriminator', True)


def generate( nice=False):
    g = generator_model()
    g.compile(loss='binary_crossentropy', optimizer="SGD")
    g.load_weights('generator')
    if nice:
        d = discriminator_model()
        d.compile(loss='binary_crossentropy', optimizer="SGD")
        d.load_weights('discriminator')
        noise = np.random.uniform(-1, 1, (BATCH_SIZE*20, 100))
        generated_images = g.predict(noise, verbose=1)
        d_pret = d.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,) + generated_images.shape[1:3], dtype=np.float32)
        nice_images = nice_images[:, :, :, None]
        for i in range(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.random.uniform(-1, 1, (BATCH_SIZE, 100))
        generated_images = g.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")


if __name__ == "__main__":


    train()

    generate()