def run(data, checkpoint_dir, eval_interval_secs, min_global_step, num_eval_examples): """Runs evaluation in a loop. Args: data: a pointer to teh MNIST data checkpoint_dir: Directory containing model checkpoints. eval_interval_secs: Interval between consecutive evaluations. min_global_step: Number of steps until the first evaluation. num_eval_examples: Number of examples to run the evaluation on. """ g = tf.Graph() with g.as_default(): # Build the model for evaluation. model_config = configuration.ModelConfig() the_model = model.DAE(model_config) the_model.build() # Create the Saver to restore model Variables. saver = tf.train.Saver() g.finalize() # Run a new evaluation run every eval_interval_secs. while True: start = time.time() # Run evaluation. run_once(data, the_model, saver, checkpoint_dir, min_global_step, num_eval_examples) time_to_next_eval = start + eval_interval_secs - time.time() # Wait until the time to next evaluation elapses if time_to_next_eval > 0: time.sleep(time_to_next_eval)
def main(unused_argv):
# Parse arguments.
parser = argparse.ArgumentParser()
args = parse_arguments(parser)
# Model configuration.
model_config = configuration.ModelConfig()
training_config = configuration.TrainingConfig()
# Create training directory.
train_dir = args.train_dir
if not tf.gfile.IsDirectory(train_dir):
tf.logging.info("Creating training directory: %s", train_dir)
tf.gfile.MakeDirs(train_dir)
# Load MNIST data.
mnist = input_data.read_data_sets('MNIST')
# Build the TensorFlow graph.
g = tf.Graph()
with g.as_default():
# Build the model.
the_model = model.DAE(model_config)
the_model.build()
# Set up the learning rate.
learning_rate = tf.constant(training_config.learning_rate)
# Set up the training ops.
train_op = tf.contrib.layers.optimize_loss(
loss=the_model.total_loss,
global_step=the_model.global_step,
learning_rate=learning_rate,
optimizer=training_config.optimizer)
# Set up the Saver for saving and restoring model checkpoints.
saver = tf.train.Saver()
# Run training.
print("Training")
with tf.Session() as sess:
print("Initializing parameters")
sess.run(tf.global_variables_initializer())
for step in range(1, args.number_of_steps):
# Read batch.
batch = mnist.train.next_batch(model_config.batch_size)[0]
# Create a noisy version of the batch.
noisy_batch = utils.add_noise(batch)
# Prepare the dictionnary to feed the data to the graph.
feed_dict = {
"images:0": batch,
"noisy_images:0": noisy_batch,
"phase_train:0": True
}
# Run training
_, loss = sess.run([train_op, the_model.total_loss],
feed_dict=feed_dict)
if step % 50 == 0:
# Save checkpoint.
ave_path = saver.save(sess, train_dir + '/model.ckpt')
# Print Loss.
print("Step:", '%06d' % (step), "cost=",
"{:.9f}".format(loss))
print('Finished training ...')
print('Start testing ...')
# load batch.
testing_data = mnist.test.images
# Plot the Original Image
# Plot the Denoised Image
# Create a noisy version of the data.
corrupted_testing = utils.add_noise(testing_data)
ori_plot = corrupted_testing[:10]
count = 1
for img in ori_plot:
name = 'ori_img' + str(count)
path = 'img/' + name
count += 1
plot_image(img.reshape((28, 28)), name, path)
# Prepare the dictionnary to feed the data to the graph.
feed_dict = {
"images:0": testing_data,
"noisy_images:0": corrupted_testing,
"phase_train:0": False
}
# Compute the loss
reconstruc, loss = sess.run(
[the_model.reconstructed_images, the_model.total_loss],
feed_dict=feed_dict)
ori_plot = reconstruc[:10]
count = 1
for img in ori_plot:
name = 'de_img' + str(count)
path = 'img/' + name
count += 1
plot_image(img.reshape((28, 28)), name, path)
print(loss)
print("Testing loss= ", loss)