我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用utils.save_images()。
def test(self): init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) self.saver_z.restore(sess, self.encode_z_model) self.saver_y.restore(sess, self.encode_y_model) realbatch_array, _ = MnistData.getNextBatch(self.ds_train, self.label_y, 0, 50, self.batch_size) output_image , label_y = sess.run([self.fake_images,self.e_y], feed_dict={self.images: realbatch_array}) #one-hot #label_y = tf.arg_max(label_y, 1) print label_y save_images(output_image , [8 , 8] , './{}/test{:02d}_{:04d}.png'.format(self.sample_path , 0, 0)) save_images(realbatch_array , [8 , 8] , './{}/test{:02d}_{:04d}_r.png'.format(self.sample_path , 0, 0)) gen_img = cv2.imread('./{}/test{:02d}_{:04d}.png'.format(self.sample_path , 0, 0), 0) real_img = cv2.imread('./{}/test{:02d}_{:04d}_r.png'.format(self.sample_path , 0, 0), 0) cv2.imshow("test_EGan", gen_img) cv2.imshow("Real_Image", real_img) cv2.waitKey(-1) print("Test finish!")
def train(dataset, network, stat, sample_dir): initial_step = stat.get_t() logger.info("Training starts on epoch {}".format(initial_step)) train_step_per_epoch = dataset.train.num_examples / conf.batch_size test_step_per_epoch = dataset.test.num_examples / conf.batch_size for epoch in range(initial_step, conf.max_epoch): start_time = time.time() # 1. train total_train_costs = [] for _ in xrange(train_step_per_epoch): images = dataset.train.next_batch(conf.batch_size) cost = network.test(images, with_update=True) total_train_costs.append(cost) # 2. test total_test_costs = [] for _ in xrange(test_step_per_epoch): images = dataset.test.next_batch(conf.batch_size) cost = network.test(images, with_update=False) total_test_costs.append(cost) avg_train_cost, avg_test_cost = np.mean(total_train_costs), np.mean(total_test_costs) stat.on_step(avg_train_cost, avg_test_cost) # 3. generate samples images, _ = dataset.test.next_batch(conf.batch_size) samples, occluded = generate_from_occluded(network, images) util.save_images(np.concatenate((occluded, samples), axis=2), dataset.height, dataset.width * 2, conf.num_generated_images, 1, directory=sample_dir, prefix="epoch_%s" % epoch) logger.info("Epoch {}: {:.2f} seconds, avg train cost: {:.3f}, avg test cost: {:.3f}" .format(epoch,(time.time() - start_time), avg_train_cost, avg_test_cost))
def generate(network, height, width, sample_dir): logger.info("Image generation starts") samples = network.generate() util.save_images(samples, height, width, 10, 10, directory=sample_dir)
def train(env, network, stat, sample_dir): initial_step = stat.get_t() logger.info("Training starts on epoch {}".format(initial_step)) train_step_per_epoch = 100 test_step_per_epoch = 10 action_n = env.action_space.n for epoch in range(initial_step, conf.max_epoch): start_time = time.time() # 1. train total_train_costs = [] for _ in tqdm(xrange(train_step_per_epoch)): images = collect_samples(conf.batch_size, env, action_n) cost = network.test(images, with_update=True) total_train_costs.append(cost) # 2. test total_test_costs = [] for _ in tqdm(xrange(test_step_per_epoch)): images = collect_samples(conf.batch_size, env, action_n) cost = network.test(images, with_update=False) total_test_costs.append(cost) avg_train_cost, avg_test_cost = np.mean(total_train_costs), np.mean(total_test_costs) stat.on_step(avg_train_cost, avg_test_cost) # 3. generate samples images, _ = collect_samples(conf.batch_size, env, action_n) samples, occluded = generate_from_occluded(network, images) util.save_images(np.concatenate((occluded, samples), axis=2), 42, 42 * 2, conf.num_generated_images, 1, directory=sample_dir, prefix="epoch_%s" % epoch) logger.info("Epoch {}: {:.2f} seconds, avg train cost: {:.3f}, avg test cost: {:.3f}" .format(epoch, (time.time() - start_time), avg_train_cost, avg_test_cost))
def train(self): opti_D = tf.train.AdamOptimizer(learning_rate=self.learning_rate_dis, beta1=0.5).minimize(self.loss , var_list=self.d_vars) opti_G = tf.train.AdamOptimizer(learning_rate=self.learning_rate_gen, beta1=0.5).minimize(self.G_fake_loss, var_list=self.g_vars) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) summary_op = tf.summary.merge_all() summary_writer = tf.summary.FileWriter(self.log_dir, sess.graph) #self.saver.restore(sess , self.model_path) batch_num = 0 e = 0 step = 0 while e <= self.max_epoch: rand = np.random.randint(0, 100) rand = 0 while batch_num < len(self.ds_train)/self.batch_size: step = step + 1 realbatch_array, real_y = MnistData.getNextBatch(self.ds_train, self.label_y, rand, batch_num,self.batch_size) batch_z = np.random.normal(0, 1 , size=[self.batch_size, self.sample_size]) #optimization D _,summary_str = sess.run([opti_D, summary_op], feed_dict={self.images:realbatch_array, self.z: batch_z, self.y:real_y}) summary_writer.add_summary(summary_str , step) #optimizaiton G _,summary_str = sess.run([opti_G, summary_op], feed_dict={self.images:realbatch_array, self.z: batch_z, self.y:real_y}) summary_writer.add_summary(summary_str , step) batch_num += 1 if step%1 ==0: D_loss = sess.run(self.loss, feed_dict={self.images:realbatch_array, self.z: batch_z, self.y:real_y}) fake_loss = sess.run(self.G_fake_loss, feed_dict={self.z : batch_z, self.y:real_y}) print("EPOCH %d step %d: D: loss = %.7f G: loss=%.7f " % (e, step , D_loss, fake_loss)) if np.mod(step , 50) == 1: sample_images = sess.run(self.fake_images ,feed_dict={self.z:batch_z, self.y:sample_label()}) save_images(sample_images[0:64] , [8, 8], './{}/train_{:02d}_{:04d}.png'.format(self.sample_path, e, step)) #Save the model self.saver.save(sess , self.model_path) e += 1 batch_num = 0 save_path = self.saver.save(sess , self.model_path) print "Model saved in file: %s" % save_path
def train_ez(self): opti_EZ = tf.train.AdamOptimizer(learning_rate = 0.01, beta1 = 0.5).minimize(self.loss_z, var_list=self.enz_vars) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) #summary_op = tf.summary.merge_all() summary_writer = tf.summary.FileWriter(self.log_dir, sess.graph) self.saver.restore(sess , self.model_path) batch_num = 0 e = 0 step = 0 while e <= self.max_epoch: rand = np.random.randint(0, 100) rand = 0 while batch_num < len(self.ds_train) / self.batch_size: step = step + 1 _,label_y = MnistData.getNextBatch(self.ds_train, self.label_y, rand, batch_num, self.batch_size) batch_z = np.random.normal(0, 1, size=[self.batch_size, self.sample_size]) # optimization E sess.run(opti_EZ, feed_dict={self.y: label_y,self.z: batch_z}) batch_num += 1 if step % 10 == 0: ez_loss = sess.run(self.loss_z, feed_dict={self.y: label_y,self.z: batch_z}) #summary_writer.add_summary(ez_loss, step) print("EPOCH %d step %d EZ loss %.7f" % (e, step, ez_loss)) if np.mod(step, 50) == 0: # sample_images = sess.run(self.fake_images, feed_dict={self.e_y:}) # save_images(sample_images[0:64], [8, 8], # './{}/train_{:02d}_{:04d}.png'.format(self.sample_path, e, step)) self.saver_z.save(sess, self.encode_z_model) e += 1 batch_num = 0 save_path = self.saver_z.save(sess, self.encode_z_model) print "Model saved in file: %s" % save_path
def train_ey(self): opti_EY = tf.train.AdamOptimizer(learning_rate=0.001, beta1=0.5).minimize(self.loss_y, var_list=self.eny_vars) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) # summary_op = tf.summary.merge_all() summary_writer = tf.summary.FileWriter(self.log_dir, sess.graph) batch_num = 0 e = 0 step = 0 while e <= self.max_epoch: rand = np.random.randint(0, 100) rand = 0 while batch_num < len(self.ds_train) / self.batch_size: step = step + 1 realbatch_image, label_y = MnistData.getNextBatch(self.ds_train, self.label_y, rand, batch_num, self.batch_size) #batch_z = np.random.normal(0, 1, size=[self.batch_size, self.sample_size]) # optimization E sess.run(opti_EY, feed_dict={self.y: label_y, self.images: realbatch_image}) batch_num += 1 if step % 10 == 0: ey_loss = sess.run(self.loss_y, feed_dict={self.y: label_y, self.images:realbatch_image}) #summary_writer.add_summary(ez_loss, step) print("EPOCH %d step %d EY loss %.7f" % (e, step, ey_loss)) if np.mod(step, 50) == 0: # sample_images = sess.run(self.fake_images, feed_dict={self.e_y:}) # save_images(sample_images[0:64], [8, 8], # './{}/train_{:02d}_{:04d}.png'.format(self.sample_path, e, step)) self.saver_y.save(sess, self.encode_y_model) e += 1 batch_num = 0 save_path = self.saver_y.save(sess, self.encode_y_model) print "Encode Y Model saved in file: %s" % save_path #do test
