def test_can_convert_from_mat_to_tfrecord_and_read_tfrecord(self):
# Prepare paths.
data_directory = os.path.join('functional_tests', 'test_data')
mat_file_path = os.path.join(data_directory, 'nyud_micro.mat')
tfrecords_file_path = os.path.join(data_directory,
'nyud_micro.tfrecords')
# Run the conversion script.
go_data = GoData(data_directory=data_directory, data_name='nyud_micro')
go_data.convert_mat_to_tfrecord(mat_file_path)
# Check that the file is created.
assert os.path.isfile(tfrecords_file_path)
# Reload data.
images, depths = go_data.inputs(data_type='', batch_size=10)
# Check that magic values are correct when the data is reloaded.
magic_image_numbers = [
-0.17450979, -0.15882352, -0.15490195, -0.15098038, -0.14705881,
-0.14313725, -0.11960781, -0.056862712, 0.0058823824
]
magic_depth_numbers = [
1.1285654, 1.8865139, 2.104018, 2.1341071, 2.6960645, 3.318316,
3.4000545, 3.4783292, 3.7568643, 3.9500945
]
session = tf.Session()
coordinator = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=session, coord=coordinator)
try:
with session.as_default():
assert np.isclose(magic_image_numbers,
images.eval()[5, 10, 10, 1],
atol=0.00001).any()
assert np.isclose(magic_depth_numbers,
depths.eval()[5, 10, 10],
atol=0.00001).any()
except tf.errors.OutOfRangeError:
fail('Should not hit this.')
finally:
coordinator.request_stop()
coordinator.join(threads)
session.close()
# Clean up.
remove_file_if_exists(tfrecords_file_path)
class GoNet(multiprocessing.Process):
"""
The class to build and interact with the GoNet TensorFlow graph.
"""
def __init__(self, message_queue=None):
super().__init__()
# Common variables.
self.batch_size = 8
self.number_of_epochs = 50000
self.initial_learning_rate = 0.00001
self.data = GoData(data_name='nyud')
self.summary_step_period = 1
self.log_directory = "logs"
self.dropout_keep_probability = 0.5
# Internal setup.
self.moving_average_loss = None
self.moving_average_decay = 0.1
self.stop_signal = False
self.step = 0
self.saver = None
self.session = None
self.dropout_keep_probability_tensor = tf.placeholder(tf.float32)
self.queue = message_queue
def create_inference_op(self, images):
"""
Performs a forward pass estimating label maps from RGB images.
:param images: The RGB images tensor.
:type images: tf.Tensor
:return: The label maps tensor.
:rtype: tf.Tensor
"""
return self.create_linear_classifier_inference_op(images)
def create_deep_inference_op(self, images):
"""
Performs a forward pass estimating label maps from RGB images using a deep convolution net.
:param images: The RGB images tensor.
:type images: tf.Tensor
:return: The label maps tensor.
:rtype: tf.Tensor
"""
with tf.name_scope('conv1'):
w_conv = weight_variable([5, 5, 3, 32])
b_conv = bias_variable([32])
h_conv = leaky_relu(conv2d(images, w_conv) + b_conv)
with tf.name_scope('conv2'):
w_conv = weight_variable([5, 5, 32, 128])
b_conv = bias_variable([128])
h_conv = leaky_relu(conv2d(h_conv, w_conv) + b_conv)
for index in range(9):
with tf.name_scope('conv' + str(index + 3)):
w_conv = weight_variable([5, 5, 128, 128])
b_conv = bias_variable([128])
h_conv = leaky_relu(conv2d(h_conv, w_conv) + b_conv)
with tf.name_scope('conv12'):
w_conv = weight_variable([5, 5, 128, 32])
b_conv = bias_variable([32])
h_conv = leaky_relu(conv2d(h_conv, w_conv) + b_conv)
with tf.name_scope('fc1'):
fc0_size = self.data.height * self.data.width * 32
fc1_size = fc0_size // 4096
h_fc = tf.reshape(h_conv, [-1, fc0_size])
w_fc = weight_variable([fc0_size, fc1_size])
b_fc = bias_variable([fc1_size])
h_fc = leaky_relu(tf.matmul(h_fc, w_fc) + b_fc)
h_fc_drop = tf.nn.dropout(h_fc,
self.dropout_keep_probability_tensor)
with tf.name_scope('fc2'):
fc2_size = fc1_size // 2
w_fc = weight_variable([fc1_size, fc2_size])
b_fc = bias_variable([fc2_size])
h_fc = leaky_relu(tf.matmul(h_fc_drop, w_fc) + b_fc)
h_fc_drop = tf.nn.dropout(h_fc,
self.dropout_keep_probability_tensor)
with tf.name_scope('fc3'):
fc3_size = self.data.height * self.data.width
w_fc = weight_variable([fc2_size, fc3_size])
b_fc = bias_variable([fc3_size])
h_fc = leaky_relu(tf.matmul(h_fc_drop, w_fc) + b_fc)
predicted_labels = tf.reshape(
h_fc, [-1, self.data.height, self.data.width, 1])
return predicted_labels
def standard_net_inference(self, images):
"""
Performs a forward pass estimating label maps from RGB images using a AlexNet-like graph setup.
:param images: The RGB images tensor.
:type images: tf.Tensor
:return: The label maps tensor.
:rtype: tf.Tensor
"""
with tf.name_scope('conv1'):
w_conv = weight_variable([7, 7, 3, 16])
b_conv = bias_variable([16])
h_conv = leaky_relu(conv2d(images, w_conv) + b_conv)
with tf.name_scope('conv2'):
w_conv = weight_variable([7, 7, 16, 24])
b_conv = bias_variable([24])
h_conv = leaky_relu(conv2d(h_conv, w_conv, [1, 2, 2, 1]) + b_conv)
with tf.name_scope('conv3'):
w_conv = weight_variable([7, 7, 24, 32])
b_conv = bias_variable([32])
h_conv = leaky_relu(conv2d(h_conv, w_conv, [1, 2, 2, 1]) + b_conv)
with tf.name_scope('fc1'):
fc0_size = size_from_stride_two(
self.data.height, iterations=2) * size_from_stride_two(
self.data.width, iterations=2) * 32
fc1_size = fc0_size // 2
h_fc = tf.reshape(h_conv, [-1, fc0_size])
w_fc = weight_variable([fc0_size, fc1_size])
b_fc = bias_variable([fc1_size])
h_fc = leaky_relu(tf.matmul(h_fc, w_fc) + b_fc)
with tf.name_scope('fc2'):
fc2_size = fc1_size // 2
w_fc = weight_variable([fc1_size, fc2_size])
b_fc = bias_variable([fc2_size])
h_fc = leaky_relu(tf.matmul(h_fc, w_fc) + b_fc)
with tf.name_scope('fc3'):
fc3_size = self.data.height * self.data.width
w_fc = weight_variable([fc2_size, fc3_size])
b_fc = bias_variable([fc3_size])
h_fc = leaky_relu(tf.matmul(h_fc, w_fc) + b_fc)
predicted_labels = tf.reshape(
h_fc, [-1, self.data.height, self.data.width, 1])
return predicted_labels
def create_linear_classifier_inference_op(self, images):
"""
Performs a forward pass estimating label maps from RGB images using only a linear classifier.
:param images: The RGB images tensor.
:type images: tf.Tensor
:return: The label maps tensor.
:rtype: tf.Tensor
"""
pixel_count = self.data.height * self.data.width
flat_images = tf.reshape(images,
[-1, pixel_count * self.data.channels])
weights = weight_variable(
[pixel_count * self.data.channels, pixel_count], stddev=0.001)
biases = bias_variable([pixel_count], constant=0.001)
flat_predicted_labels = tf.matmul(flat_images, weights) + biases
predicted_labels = tf.reshape(
flat_predicted_labels, [-1, self.data.height, self.data.width, 1])
return predicted_labels
def create_loss_tensor(self, predicted_labels, labels):
"""
Create the loss op and add it to the graph.
:param predicted_labels: The labels predicted by the graph.
:type predicted_labels: tf.Tensor
:param labels: The ground truth labels.
:type labels: tf.Tensor
:return: The loss tensor.
:rtype: tf.Tensor
"""
return self.relative_differences(predicted_labels, labels)
@staticmethod
def relative_differences(predicted_labels, labels):
"""
Determines the absolute L1 relative differences between two label maps.
:param predicted_labels: The first label map tensor (usually the predicted labels).
:type predicted_labels: tf.Tensor
:param labels: The second label map tensor (usually the actual labels).
:type labels: tf.Tensor
:return: The difference tensor.
:rtype: tf.Tensor
"""
difference = tf.abs(predicted_labels - labels)
return difference / labels
def create_training_op(self, value_to_minimize):
"""
Create and add the training op to the graph.
:param value_to_minimize: The value to train on.
:type value_to_minimize: tf.Tensor
:return: The training op.
:rtype: tf.Operation
"""
return tf.train.AdamOptimizer(
self.initial_learning_rate).minimize(value_to_minimize)
@staticmethod
def convert_to_heat_map_rgb(tensor):
"""
Convert a tensor to a heat map.
:param tensor: The tensor values to be converted.
:type tensor: tf.Tensor
:return: The heat map image tensor.
:rtype: tf.Tensor
"""
maximum = tf.reduce_max(tensor)
minimum = tf.reduce_min(tensor)
ratio = 2 * (tensor - minimum) / (maximum - minimum)
b = tf.maximum(0.0, (1 - ratio))
r = tf.maximum(0.0, (ratio - 1))
g = 1 - b - r
return tf.concat(3, [r, g, b]) - 0.5
def image_comparison_summary(self, images, labels, predicted_labels,
label_differences):
"""
Combines the image, label, and difference tensors together into a presentable image. Then adds the
image summary op to the graph.
:param images: The original image.
:type images: tf.Tensor
:param labels: The tensor containing the actual label values.
:type labels: tf.Tensor
:param predicted_labels: The tensor containing the predicted labels.
:type predicted_labels: tf.Tensor
:param label_differences: The tensor containing the difference between the actual and predicted labels.
:type label_differences: tf.Tensor
"""
label_heat_map = self.convert_to_heat_map_rgb(labels)
predicted_label_heat_map = self.convert_to_heat_map_rgb(
predicted_labels)
label_difference_heat_map = self.convert_to_heat_map_rgb(
label_differences)
comparison_image = tf.concat(1, [
images, label_heat_map, predicted_label_heat_map,
label_difference_heat_map
])
tf.image_summary('comparison', comparison_image)
def interface_handler(self):
"""
Handle input from the user using the interface.
"""
if self.queue:
if not self.queue.empty():
message = self.queue.get(block=False)
if message == 'save':
save_path = self.saver.save(self.session,
os.path.join(
'models', 'depthnet.ckpt'),
global_step=self.step)
tf.train.write_graph(self.session.graph_def, 'models',
'depthnet.pb')
print("Model saved in file: %s" % save_path)
if message == 'quit':
self.stop_signal = True
def train(self):
"""
Adds the training operations and runs the training loop.
"""
print('Preparing data...')
# Setup the inputs.
images_tensor, labels_tensor = self.data.inputs(
data_type='',
batch_size=self.batch_size,
num_epochs=self.number_of_epochs)
print('Building graph...')
# Add the forward pass operations to the graph.
predicted_labels_tensor = self.create_inference_op(images_tensor)
# Add the loss operations to the graph.
with tf.name_scope('loss'):
loss_tensor = self.create_loss_tensor(predicted_labels_tensor,
labels_tensor)
loss_per_pixel_tensor = tf.reduce_mean(loss_tensor)
tf.scalar_summary("Loss per pixel", loss_per_pixel_tensor)
with tf.name_scope('comparison_summary'):
self.image_comparison_summary(images_tensor, labels_tensor,
predicted_labels_tensor, loss_tensor)
# Add the training operations to the graph.
training_op = self.create_training_op(
value_to_minimize=loss_per_pixel_tensor)
# The op for initializing the variables.
initialize_op = tf.initialize_all_variables()
# Prepare session.
self.session = tf.Session()
# Prepare the summary operations.
summaries_op = tf.merge_all_summaries()
summary_path = os.path.join(
self.log_directory,
datetime.datetime.now().strftime("y%Y_m%m_d%d_h%H_m%M_s%S"))
writer = tf.train.SummaryWriter(summary_path, self.session.graph)
# Prepare saver.
self.saver = tf.train.Saver()
print('Starting training...')
# Initialize the variables.
self.session.run(initialize_op)
# Start input enqueue threads.
coordinator = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=self.session,
coord=coordinator)
# Preform the training loop.
try:
while not coordinator.should_stop() and not self.stop_signal:
# Regular training step.
start_time = time.time()
_, loss, summaries = self.session.run(
[training_op, loss_per_pixel_tensor, summaries_op],
feed_dict={
self.dropout_keep_probability_tensor:
self.dropout_keep_probability
})
duration = time.time() - start_time
# Information print and summary write step.
if self.step % self.summary_step_period == 0:
writer.add_summary(summaries, self.step)
print('Step %d: Loss per pixel = %.5f (%.3f sec / step)' %
(self.step, loss, duration))
self.step += 1
# Handle interface messages from the user.
self.interface_handler()
except tf.errors.OutOfRangeError:
if self.step == 0:
print('Training data not found.')
else:
print('Done training for %d epochs, %d steps.' %
(self.number_of_epochs, self.step))
finally:
# When done, ask the threads to stop.
coordinator.request_stop()
# Wait for threads to finish.
coordinator.join(threads)
self.session.close()
def run(self):
"""
Allow for training the network from a multiprocessing standpoint.
"""
self.train()
class GoNet(multiprocessing.Process):
"""
The class to build and interact with the GoNet TensorFlow graph.
"""
def __init__(self, message_queue=None):
super().__init__()
# Common variables.
self.batch_size = 8
self.number_of_epochs = 50000
self.initial_learning_rate = 0.00001
self.data = GoData(data_name='nyud')
self.summary_step_period = 1
self.log_directory = "logs"
self.dropout_keep_probability = 0.5
# Internal setup.
self.moving_average_loss = None
self.moving_average_decay = 0.1
self.stop_signal = False
self.step = 0
self.saver = None
self.session = None
self.dropout_keep_probability_tensor = tf.placeholder(tf.float32)
self.queue = message_queue
def create_inference_op(self, images):
"""
Performs a forward pass estimating label maps from RGB images.
:param images: The RGB images tensor.
:type images: tf.Tensor
:return: The label maps tensor.
:rtype: tf.Tensor
"""
return self.create_linear_classifier_inference_op(images)
def create_deep_inference_op(self, images):
"""
Performs a forward pass estimating label maps from RGB images using a deep convolution net.
:param images: The RGB images tensor.
:type images: tf.Tensor
:return: The label maps tensor.
:rtype: tf.Tensor
"""
with tf.name_scope('conv1'):
w_conv = weight_variable([5, 5, 3, 32])
b_conv = bias_variable([32])
h_conv = leaky_relu(conv2d(images, w_conv) + b_conv)
with tf.name_scope('conv2'):
w_conv = weight_variable([5, 5, 32, 128])
b_conv = bias_variable([128])
h_conv = leaky_relu(conv2d(h_conv, w_conv) + b_conv)
for index in range(9):
with tf.name_scope('conv' + str(index + 3)):
w_conv = weight_variable([5, 5, 128, 128])
b_conv = bias_variable([128])
h_conv = leaky_relu(conv2d(h_conv, w_conv) + b_conv)
with tf.name_scope('conv12'):
w_conv = weight_variable([5, 5, 128, 32])
b_conv = bias_variable([32])
h_conv = leaky_relu(conv2d(h_conv, w_conv) + b_conv)
with tf.name_scope('fc1'):
fc0_size = self.data.height * self.data.width * 32
fc1_size = fc0_size // 4096
h_fc = tf.reshape(h_conv, [-1, fc0_size])
w_fc = weight_variable([fc0_size, fc1_size])
b_fc = bias_variable([fc1_size])
h_fc = leaky_relu(tf.matmul(h_fc, w_fc) + b_fc)
h_fc_drop = tf.nn.dropout(h_fc, self.dropout_keep_probability_tensor)
with tf.name_scope('fc2'):
fc2_size = fc1_size // 2
w_fc = weight_variable([fc1_size, fc2_size])
b_fc = bias_variable([fc2_size])
h_fc = leaky_relu(tf.matmul(h_fc_drop, w_fc) + b_fc)
h_fc_drop = tf.nn.dropout(h_fc, self.dropout_keep_probability_tensor)
with tf.name_scope('fc3'):
fc3_size = self.data.height * self.data.width
w_fc = weight_variable([fc2_size, fc3_size])
b_fc = bias_variable([fc3_size])
h_fc = leaky_relu(tf.matmul(h_fc_drop, w_fc) + b_fc)
predicted_labels = tf.reshape(h_fc, [-1, self.data.height, self.data.width, 1])
return predicted_labels
def standard_net_inference(self, images):
"""
Performs a forward pass estimating label maps from RGB images using a AlexNet-like graph setup.
:param images: The RGB images tensor.
:type images: tf.Tensor
:return: The label maps tensor.
:rtype: tf.Tensor
"""
with tf.name_scope('conv1'):
w_conv = weight_variable([7, 7, 3, 16])
b_conv = bias_variable([16])
h_conv = leaky_relu(conv2d(images, w_conv) + b_conv)
with tf.name_scope('conv2'):
w_conv = weight_variable([7, 7, 16, 24])
b_conv = bias_variable([24])
h_conv = leaky_relu(conv2d(h_conv, w_conv, [1, 2, 2, 1]) + b_conv)
with tf.name_scope('conv3'):
w_conv = weight_variable([7, 7, 24, 32])
b_conv = bias_variable([32])
h_conv = leaky_relu(conv2d(h_conv, w_conv, [1, 2, 2, 1]) + b_conv)
with tf.name_scope('fc1'):
fc0_size = size_from_stride_two(self.data.height, iterations=2) * size_from_stride_two(self.data.width,
iterations=2) * 32
fc1_size = fc0_size // 2
h_fc = tf.reshape(h_conv, [-1, fc0_size])
w_fc = weight_variable([fc0_size, fc1_size])
b_fc = bias_variable([fc1_size])
h_fc = leaky_relu(tf.matmul(h_fc, w_fc) + b_fc)
with tf.name_scope('fc2'):
fc2_size = fc1_size // 2
w_fc = weight_variable([fc1_size, fc2_size])
b_fc = bias_variable([fc2_size])
h_fc = leaky_relu(tf.matmul(h_fc, w_fc) + b_fc)
with tf.name_scope('fc3'):
fc3_size = self.data.height * self.data.width
w_fc = weight_variable([fc2_size, fc3_size])
b_fc = bias_variable([fc3_size])
h_fc = leaky_relu(tf.matmul(h_fc, w_fc) + b_fc)
predicted_labels = tf.reshape(h_fc, [-1, self.data.height, self.data.width, 1])
return predicted_labels
def create_linear_classifier_inference_op(self, images):
"""
Performs a forward pass estimating label maps from RGB images using only a linear classifier.
:param images: The RGB images tensor.
:type images: tf.Tensor
:return: The label maps tensor.
:rtype: tf.Tensor
"""
pixel_count = self.data.height * self.data.width
flat_images = tf.reshape(images, [-1, pixel_count * self.data.channels])
weights = weight_variable([pixel_count * self.data.channels, pixel_count], stddev=0.001)
biases = bias_variable([pixel_count], constant=0.001)
flat_predicted_labels = tf.matmul(flat_images, weights) + biases
predicted_labels = tf.reshape(flat_predicted_labels, [-1, self.data.height, self.data.width, 1])
return predicted_labels
def create_loss_tensor(self, predicted_labels, labels):
"""
Create the loss op and add it to the graph.
:param predicted_labels: The labels predicted by the graph.
:type predicted_labels: tf.Tensor
:param labels: The ground truth labels.
:type labels: tf.Tensor
:return: The loss tensor.
:rtype: tf.Tensor
"""
return self.relative_differences(predicted_labels, labels)
@staticmethod
def relative_differences(predicted_labels, labels):
"""
Determines the absolute L1 relative differences between two label maps.
:param predicted_labels: The first label map tensor (usually the predicted labels).
:type predicted_labels: tf.Tensor
:param labels: The second label map tensor (usually the actual labels).
:type labels: tf.Tensor
:return: The difference tensor.
:rtype: tf.Tensor
"""
difference = tf.abs(predicted_labels - labels)
return difference / labels
def create_training_op(self, value_to_minimize):
"""
Create and add the training op to the graph.
:param value_to_minimize: The value to train on.
:type value_to_minimize: tf.Tensor
:return: The training op.
:rtype: tf.Operation
"""
return tf.train.AdamOptimizer(self.initial_learning_rate).minimize(value_to_minimize)
@staticmethod
def convert_to_heat_map_rgb(tensor):
"""
Convert a tensor to a heat map.
:param tensor: The tensor values to be converted.
:type tensor: tf.Tensor
:return: The heat map image tensor.
:rtype: tf.Tensor
"""
maximum = tf.reduce_max(tensor)
minimum = tf.reduce_min(tensor)
ratio = 2 * (tensor - minimum) / (maximum - minimum)
b = tf.maximum(0.0, (1 - ratio))
r = tf.maximum(0.0, (ratio - 1))
g = 1 - b - r
return tf.concat(3, [r, g, b]) - 0.5
def image_comparison_summary(self, images, labels, predicted_labels, label_differences):
"""
Combines the image, label, and difference tensors together into a presentable image. Then adds the
image summary op to the graph.
:param images: The original image.
:type images: tf.Tensor
:param labels: The tensor containing the actual label values.
:type labels: tf.Tensor
:param predicted_labels: The tensor containing the predicted labels.
:type predicted_labels: tf.Tensor
:param label_differences: The tensor containing the difference between the actual and predicted labels.
:type label_differences: tf.Tensor
"""
label_heat_map = self.convert_to_heat_map_rgb(labels)
predicted_label_heat_map = self.convert_to_heat_map_rgb(predicted_labels)
label_difference_heat_map = self.convert_to_heat_map_rgb(label_differences)
comparison_image = tf.concat(1, [images, label_heat_map, predicted_label_heat_map, label_difference_heat_map])
tf.image_summary('comparison', comparison_image)
def interface_handler(self):
"""
Handle input from the user using the interface.
"""
if self.queue:
if not self.queue.empty():
message = self.queue.get(block=False)
if message == 'save':
save_path = self.saver.save(self.session, os.path.join('models', 'depthnet.ckpt'),
global_step=self.step)
tf.train.write_graph(self.session.graph_def, 'models', 'depthnet.pb')
print("Model saved in file: %s" % save_path)
if message == 'quit':
self.stop_signal = True
def train(self):
"""
Adds the training operations and runs the training loop.
"""
print('Preparing data...')
# Setup the inputs.
images_tensor, labels_tensor = self.data.inputs(data_type='', batch_size=self.batch_size,
num_epochs=self.number_of_epochs)
print('Building graph...')
# Add the forward pass operations to the graph.
predicted_labels_tensor = self.create_inference_op(images_tensor)
# Add the loss operations to the graph.
with tf.name_scope('loss'):
loss_tensor = self.create_loss_tensor(predicted_labels_tensor, labels_tensor)
loss_per_pixel_tensor = tf.reduce_mean(loss_tensor)
tf.scalar_summary("Loss per pixel", loss_per_pixel_tensor)
with tf.name_scope('comparison_summary'):
self.image_comparison_summary(images_tensor, labels_tensor, predicted_labels_tensor, loss_tensor)
# Add the training operations to the graph.
training_op = self.create_training_op(value_to_minimize=loss_per_pixel_tensor)
# The op for initializing the variables.
initialize_op = tf.initialize_all_variables()
# Prepare session.
self.session = tf.Session()
# Prepare the summary operations.
summaries_op = tf.merge_all_summaries()
summary_path = os.path.join(self.log_directory, datetime.datetime.now().strftime("y%Y_m%m_d%d_h%H_m%M_s%S"))
writer = tf.train.SummaryWriter(summary_path, self.session.graph)
# Prepare saver.
self.saver = tf.train.Saver()
print('Starting training...')
# Initialize the variables.
self.session.run(initialize_op)
# Start input enqueue threads.
coordinator = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=self.session, coord=coordinator)
# Preform the training loop.
try:
while not coordinator.should_stop() and not self.stop_signal:
# Regular training step.
start_time = time.time()
_, loss, summaries = self.session.run(
[training_op, loss_per_pixel_tensor, summaries_op],
feed_dict={self.dropout_keep_probability_tensor: self.dropout_keep_probability}
)
duration = time.time() - start_time
# Information print and summary write step.
if self.step % self.summary_step_period == 0:
writer.add_summary(summaries, self.step)
print('Step %d: Loss per pixel = %.5f (%.3f sec / step)' % (self.step, loss, duration))
self.step += 1
# Handle interface messages from the user.
self.interface_handler()
except tf.errors.OutOfRangeError:
if self.step == 0:
print('Training data not found.')
else:
print('Done training for %d epochs, %d steps.' % (self.number_of_epochs, self.step))
finally:
# When done, ask the threads to stop.
coordinator.request_stop()
# Wait for threads to finish.
coordinator.join(threads)
self.session.close()
def run(self):
"""
Allow for training the network from a multiprocessing standpoint.
"""
self.train()