5fa690b64743843d192a2697e3c7b7653cf3950a,infogan/infogan.py,INFOGAN,save_imgs,#INFOGAN#Any#,208

Before Change

        fig, axs = plt.subplots(r, c)
        for i in range(r):
            sampled_noise = np.random.normal(0, 1, (c, 62))
            sampled_labels = np.arange(0, 10).reshape(-1, 1)
            sampled_labels = to_categorical(sampled_labels, num_classes=self.num_classes)
            sampled_cont = np.repeat(np.expand_dims(np.linspace(-1, 1, num=c), axis=1), 2, axis=1)

After Change

        fig, axs = plt.subplots(r, c)
        for i in range(r):
            sampled_noise, sampled_labels, sampled_cont = self.sample_generator_input(c)
            gen_input = np.concatenate((sampled_noise, sampled_labels, sampled_cont), axis=1)
            gen_imgs = self.generator.predict(gen_input)
            gen_imgs = 0.5 * gen_imgs + 0.5

Instances