生成对抗网络的项目代码

# -*- coding: utf-8 -*- import tensorflow as tf import numpy as np import matplotlib.pyplot as plt batch_size = 100 z_dim = 100 def montage(images): if isinstance(images, list): images = np.array(images) img_h = images.shape[1] img_w = images.shape[2] n_plots = int(np.ceil(np.sqrt(images.shape[0]))) m = np.ones((images.shape[1] * n_plots + n_plots + 1, images.shape[2] * n_plots + n_plots + 1)) * 0.5 for i in range(n_plots): for j in range(n_plots): this_filter = i * n_plots + j if this_filter < images.shape[0]: this_img = images[this_filter] m[1 + i + i * img_h:1 + i + (i + 1) * img_h, 1 + j + j * img_w:1 + j + (j + 1) * img_w] = this_img return m sess = tf.Session() sess.run(tf.global_variables_initializer()) saver = tf.train.import_meta_graph('./mnist_dcgan-60000.meta') saver.restore(sess, tf.train.latest_checkpoint('./')) graph = tf.get_default_graph() g = graph.get_tensor_by_name('generator/g/Tanh:0') noise = graph.get_tensor_by_name('noise:0') is_training = graph.get_tensor_by_name('is_training:0') n = np.random.uniform(-1.0, 1.0, [batch_size, z_dim]).astype(np.float32) gen_imgs = sess.run(g, feed_dict={noise: n, is_training: False}) gen_imgs = (gen_imgs + 1) / 2 imgs = [img[:, :, 0] for img in gen_imgs] gen_imgs = montage(imgs) plt.axis('off') plt.imshow(gen_imgs, cmap='gray') plt.show()