Easy GAN

GAN.py

from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten
from keras.layers import BatchNormalization
from keras.layers.advanced_activations import LeakyReLU
from keras.models import Sequential, Model
from keras.optimizers import Adam
import matplotlib.pyplot as plt
import numpy as np


class GAN():
    def __init__(self):
        self.img_rows = 28
        self.img_cols = 28
        self.channels = 1
        self.img_shape = (self.img_rows, self.img_cols, self.channels)
        self.latent_dim = 100
        optimizer = Adam(0.0002, 0.5)
        self.discriminator = self.build_discriminator()
        self.discriminator.compile(loss='binary_crossentropy',
                                   optimizer=optimizer,
                                   metrics=['accuracy'])
        self.generator = self.build_generator()
        z = Input(shape=(self.latent_dim,))
        img = self.generator(z)
        self.discriminator.trainable = False
        validity = self.discriminator(img)
        self.combined = Model(z, validity)
        self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)

    def build_generator(self):

        model = Sequential()

        model.add(Dense(256, input_dim=self.latent_dim))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(1024))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(np.prod(self.img_shape), activation='tanh'))
        model.add(Reshape(self.img_shape))
        model.summary()
        noise = Input(shape=(self.latent_dim,))
        img = model(noise)
        return Model(noise, img)

    def build_discriminator(self):
        model = Sequential()

        model.add(Flatten(input_shape=self.img_shape))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(256))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(1, activation='sigmoid'))
        model.summary()
        img = Input(shape=self.img_shape)
        validity = model(img)
        return Model(img, validity)

    def train(self, epochs, batch_size=128, sample_interval=50):
        (X_train, _), (_, _) = mnist.load_data()
        X_train = X_train / 127.5 - 1.
        X_train = np.expand_dims(X_train, axis=3)
        valid = np.ones((batch_size, 1))
        fake = np.zeros((batch_size, 1))

        for epoch in range(epochs):
            idx = np.random.randint(0, X_train.shape[0], batch_size)
            imgs = X_train[idx]
            noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
            g_loss = self.combined.train_on_batch(noise, valid)
            gen_imgs = self.generator.predict(noise)
            d_loss_real = self.discriminator.train_on_batch(imgs, valid)
            d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

        print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100 * d_loss[1], g_loss))
        if epoch % sample_interval == 0:
            self.sample_images(epoch)

    def sample_images(self, epoch):
        r, c = 5, 5
        noise = np.random.normal(0, 1, (r * c, self.latent_dim))
        gen_imgs = self.generator.predict(noise)
        gen_imgs = 0.5 * gen_imgs + 0.5
        fig, axs = plt.subplots(r, c)
        cnt = 0
        for i in range(r):
            for j in range(c):
                axs[i, j].imshow(gen_imgs[cnt, :, :, 0], cmap='gray')
                axs[i, j].axis('off')
                cnt += 1
        fig.savefig("images/%d.png" % epoch)
        plt.close()


if __name__ == '__main__':
    gan = GAN()
    gan.train(epochs=100000, batch_size=132, sample_interval=10000)

Hey,

Thanks for providing a neat implementation of DCNN. However, I tried but failed to run the code. It gives a warning UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set model.trainable without calling model.compile after ? 'Discrepancy between trainable weights and collected trainable'. I tried to find an answer but failed. Could you please help me in this?

Many thanks, and best wishes,
Pawan

Hi @pawan,

That warning was implemented by the Keras team. There an open bug about this, please refer to: https://github.com/keras-team/keras/issues/8585
The replies there have some examples of how to bypass the warning, while they're still discussing about the issue related to GANs implementation.

I hope it helps.

Cheers,
Gus

Hey,

Thanks for providing a neat implementation of DCNN. However, I tried but failed to run the code. It gives a warning UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set model.trainable without calling model.compile after ? 'Discrepancy between trainable weights and collected trainable'. I tried to find an answer but failed. Could you please help me in this?

Many thanks, and best wishes,
Pawan

Hi, This is due to you made "model.trainable=False" after compiling the model.