summary_writer = tf.train.SummaryWriter('experiment', graph=sess.graph)
if os.path.isfile("save/model.ckpt"):
print("Restoring saved parameters")
saver.restore(sess, "save/model.ckpt")
else:
print("Initializing parameters")
sess.run(tf.initialize_all_variables())
print("Initializing parameters")
sess.run(tf.initialize_all_variables())
for step in xrange(1, n_steps):
batch = rd.get_next_batch(batch_size=batch_size)
feed_dict = {x: batch}
_, cur_loss, summary_str = sess.run([train_step, loss, summary_op],
feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
if step % 50 == 0:
print "Step {0} | Loss: {1}".format(step, cur_loss)
# check if the model works
for i in xrange(5):
image = rd.get_one_image()
rd.show_a_image(image)
image = np.reshape(image, newshape=[1, 1024])
feed_dict = {x: image}
x_hatt = sess.run(x_hat, feed_dict=feed_dict)
rd.show_a_image(x_hatt)
