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 here in the init function, self.discriminator.trainable is made as False.
So how discriminator will be trained?

Hello ! Thanks for the GAN article. But when I tried to implement It did not work. An error pops out saying ''GAN' object has no attribute 'build_discriminator'

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.