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!")
python类save_images()的实例源码
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
