dcgan.py 文件源码

def train(self, epochs, batch_size=128, save_interval=50):

        mnist = fetch_mldata('MNIST original')

        X = mnist.data.reshape((-1,) + self.img_shape)
        y = mnist.target

        # Rescale -1 to 1
        X = (X.astype(np.float32) - 127.5) / 127.5

        half_batch = int(batch_size / 2)

        for epoch in range(epochs):

            # ---------------------
            #  Train Discriminator
            # ---------------------

            self.discriminator.set_trainable(True)

            # Select a random half batch of images
            idx = np.random.randint(0, X.shape[0], half_batch)
            imgs = X[idx]

            noise = np.random.normal(0, 1, (half_batch, 100))

            # Generate a half batch of images
            gen_imgs = self.generator.predict(noise)

            valid = np.concatenate((np.ones((half_batch, 1)), np.zeros((half_batch, 1))), axis=1)
            fake = np.concatenate((np.zeros((half_batch, 1)), np.ones((half_batch, 1))), axis=1)

            # Train the discriminator
            d_loss_real, d_acc_real = self.discriminator.train_on_batch(imgs, valid)
            d_loss_fake, d_acc_fake = self.discriminator.train_on_batch(gen_imgs, fake)
            d_loss = 0.5 * (d_loss_real + d_loss_fake)
            d_acc = 0.5 * (d_acc_real + d_acc_fake)


            # ---------------------
            #  Train Generator
            # ---------------------

            # We only want to train the generator for the combined model
            self.discriminator.set_trainable(False)

            # Sample noise and use as generator input
            noise = np.random.normal(0, 1, (batch_size, 100))

            # The generator wants the discriminator to label the generated samples as valid
            valid = np.concatenate((np.ones((batch_size, 1)), np.zeros((batch_size, 1))), axis=1)

            # Train the generator
            g_loss, g_acc = self.combined.train_on_batch(noise, valid)

            # Display the progress
            print ("%d [D loss: %f, acc: %.2f%%] [G loss: %f, acc: %.2f%%]" % (epoch, d_loss, 100*d_acc, g_loss, 100*g_acc))

            # If at save interval => save generated image samples
            if epoch % save_interval == 0:
                self.save_imgs(epoch)