init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
for epoch in range(FLAGS.max_epoch):
discriminator_loss = 0.0
generator_loss = 0.0
widgets = ["epoch #%d|" % epoch, Percentage(), Bar(), ETA()]
pbar = ProgressBar(maxval=FLAGS.updates_per_epoch, widgets=widgets)
pbar.start()
for i in range(FLAGS.updates_per_epoch):
pbar.update(i)
x = rd.get_next_batch(batch_size=FLAGS.batch_size)
_, loss_value = sess.run([train_discrimator, D_loss], {
input_tensor: x,
learning_rate: FLAGS.d_learning_rate
})
discriminator_loss += loss_value
# We still need input for moving averages.
# Need to find how to fix it.
_, loss_value = sess.run([train_generator, G_loss], {
input_tensor: x,
learning_rate: FLAGS.g_learning_rate
})
generator_loss += loss_value
no_batches = int(reader.train_size / args.batch_size)
loss = []
acc = []
updation_step = args.lr_change * 2
with tqdm(total=no_batches,
desc="Epoch {}/{}: loss: {} acc: {}".format(
epoch + 1, args.epochs, loss, acc)) as pbar:
for batch_num in range(no_batches):
start = i
end = i + args.batch_size
i = start + int(args.batch_size * (1 - args.data_overlap))
step = epoch * no_batches + batch_num
epoch_x, epoch_y = reader.get_next_batch(start, end)
if args.model.startswith('cnn'):
epoch_x = np.reshape(
epoch_x, (epoch_x.shape[0], timesteps, embed_size, 1))
_, c, train_summary = sess.run(
[optimizer, cost_func, merged_summary_op],
feed_dict={
lr: args.learning_rate,
x: epoch_x,
y: epoch_y
})
train_summary_writer.add_summary(train_summary, step)
val_loss, val_acc, val_summary = sess.run(
[cost_func, accuracy, merged_summary_op],
steps_per_epoch=int(reader.train_size / args.batch_size),
validation_data=(val_x, val_y),
epochs=args.epochs,
callbacks=[logging, checkpoint, reduce_lr, early_stopping])
else:
if not os.path.exists(log_dir):
os.mkdir(log_dir)
for epoch in range(args.epochs):
num_batches = int(reader.train_size / args.batch_size)
start_index = 0
epoch_loss, epoch_acc = [], []
for i in range(num_batches):
start_index = i * args.batch_size
epoch_x, epoch_y = reader.get_next_batch(start_index,
args.batch_size)
[loss, acc] = model.train_on_batch(epoch_x, epoch_y)
epoch_loss.append(loss)
epoch_acc.append(acc)
num_batches = int(reader.val_size / args.batch_size)
start_index = 0
val_loss, val_acc = [], []
for i in range(num_batches):
start_index = i * args.batch_size
epoch_x, epoch_y = reader.get_next_val_batch(
start_index, args.batch_size)
[loss, acc] = model.test_on_batch(epoch_x, epoch_y)
val_loss.append(loss)
with tf.Session() as sess:
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}