# Learning rate
initial_learning_rate = 0.001 decay_steps = 250 decay_rate = 0.9
# Validation
output_steps = 10 # Number of steps to print output
eval_steps = 20 # Number of steps to perform evaluations
# Training
max_steps = 3000 # Number of steps to perform training
save_steps = 200 # Number of steps to perform saving checkpoints
num_tests = 5 # Number of times to test for test accuracy
max_checkpoints_to_keep = 3
save_dir = "data/checkpoints"
train_vars = 'models/fc8-pets/weights:0,models/fc8-pets/biases:0'
# Export
export_dir = "/tmp/export/"
export_name = "pet-model" export_version = 2
images, labels = datasets.input_pipeline(dataset_dir, batch_size, is_training=True)
test_images, test_labels = datasets.input_pipeline(dataset_dir, batch_size, is_training=False)
with tf.variable_scope("models") as scope:
logits = nets.inference(images, is_training=True)
scope.reuse_variables()
test_logits = nets.inference(test_images, is_training=False)
total_loss = models.compute_loss(logits, labels)
train_accuracy = models.compute_accuracy(logits, labels)
test_accuracy = models.compute_accuracy(test_logits, test_labels)
global_step = tf.Variable(0, trainable=False)
learning_rate = models.get_learning_rate(global_step, initial_learning_rate, decay_steps, decay_rate)
train_op = models.train(total_loss, learning_rate, global_step, train_vars)
saver = tf.train.Saver(max_to_keep=max_checkpoints_to_keep)
train_data_reader = lines_from_file(train_txt, repeat=True)
image_paths_placeholder = tf.placeholder(tf.string,
shape=(None, num_frames),
name='image_paths')
labels_placeholder = tf.placeholder(tf.int64, shape=(None, ), name='labels')
train_input_queue = data_flow_ops.FIFOQueue(capacity=10000,
dtypes=[tf.string, tf.int64],
shapes=[(num_frames, ), ()])
train_enqueue_op = train_input_queue.enqueue_many(
[image_paths_placeholder, labels_placeholder])
frames_batch, labels_batch = input_pipeline(train_input_queue,
batch_size=batch_size,
image_size=image_size)
with tf.variable_scope("models") as scope:
logits, _ = nets.inference(frames_batch, is_training=True)
total_loss, cross_entropy_loss, reg_loss = models.compute_loss(
logits, labels_batch)
train_accuracy = models.compute_accuracy(logits, labels_batch)
global_step = tf.Variable(0, trainable=False)
learning_rate = models.get_learning_rate(global_step, initial_learning_rate,
decay_steps, decay_rate)
train_op = models.train(total_loss, learning_rate, global_step)
tf.summary.scalar("learning_rate", learning_rate)