cf65784f7297dca491436112b9a5689ecd7533ec,cogan/cogan.py,COGAN,train,#COGAN#,117

Before Change

        X2 = X_train[int(X_train.shape[0]/2):]
        X2 = scipy.ndimage.interpolation.rotate(X2, 90, axes=(1, 2))

        half_batch = int(batch_size / 2)

        for epoch in range(epochs):

            // ----------------------
            //  Train Discriminators
            // ----------------------

            // Select a random half batch of images
            idx = np.random.randint(0, X1.shape[0], half_batch)
            imgs1 = X1[idx]
            imgs2 = X2[idx]

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

            // Generate a half batch of new images
            gen_imgs1 = self.g1.predict(noise)
            gen_imgs2 = self.g2.predict(noise)

            // Train the discriminators
            d1_loss_real = self.d1.train_on_batch(imgs1, np.ones((half_batch, 1)))
            d2_loss_real = self.d2.train_on_batch(imgs2, np.ones((half_batch, 1)))
            d1_loss_fake = self.d1.train_on_batch(gen_imgs1, np.zeros((half_batch, 1)))
            d2_loss_fake = self.d2.train_on_batch(gen_imgs2, np.zeros((half_batch, 1)))
            d1_loss = 0.5 * np.add(d1_loss_real, d1_loss_fake)
            d2_loss = 0.5 * np.add(d2_loss_real, d2_loss_fake)


            // ------------------
            //  Train Generators
            // ------------------

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

            // The generators wants the discriminators to label the generated samples
            // as valid (ones)
            valid = np.array([1] * batch_size)

            // Train the generators
            g_loss = self.combined.train_on_batch(noise, [valid, valid])

After Change

        // Adversarial ground truths
        valid = np.ones((batch_size, 1))
        fake = np.zeros((batch_size, 1))

        for epoch in range(epochs):

            // ----------------------

Instances