def evaluate():
"""Eval FER2013 for a number of steps."""
with tf.Graph().as_default() as graph:
# Get images and labels for FER2013.
images, labels = fer2013.inputs(eval_data=FLAGS.eval_data,
input_file=TEST_INPUT_FILE)
keep_prob = 1
# Build a Graph that computes the logits predictions from the
# inference model.
logits = fer2013.inference(images, keep_prob, 128)
# Calculate predictions.
top_k_op = tf.nn.in_top_k(logits, labels, 1)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
fer2013.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, graph)
while True:
eval_once(saver, summary_writer, top_k_op, summary_op)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def evaluate():
"""Eval FER2013 for a number of steps."""
with tf.Graph().as_default():
# Get images and labels for FER2013.
eval_data = FLAGS.eval_data == 'test'
print(eval_data)
print("evaluating model...")
images, labels = fer2013.inputs(eval_data=eval_data)
# Build a Graph that computes the logits predictions from the
# inference model.
logits = fer2013.inference(images)
# Calculate predictions.
top_k_op = tf.nn.in_top_k(logits, labels, 1)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
fer2013.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
graph_def = tf.get_default_graph().as_graph_def()
summary_writer = tf.train.SummaryWriter(FLAGS.eval_dir,
graph_def=graph_def)
while True:
eval_once(saver, summary_writer, top_k_op, summary_op)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def evaluate():
"""Eval FER2013 for a number of steps."""
with tf.Graph().as_default():
# Get images and labels for FER2013.
eval_data = FLAGS.eval_data == 'test'
print(eval_data)
print("evaluating model...")
images, labels = fer2013.inputs(eval_data=eval_data)
# Build a Graph that computes the logits predictions from the
# inference model.
logits = fer2013.inference(images)
# Calculate predictions.
top_k_op = tf.nn.in_top_k(logits, labels, 1)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
fer2013.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
graph_def = tf.get_default_graph().as_graph_def()
summary_writer = tf.train.SummaryWriter(FLAGS.eval_dir,
graph_def=graph_def)
while True:
eval_once(saver, summary_writer, top_k_op, summary_op)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def tower_loss(scope):
"""Calculate the total loss on a single tower running the model.
Args:
scope: unique prefix string identifying the tower, e.g. 'tower_0'
Returns:
Tensor of shape [] containing the total loss for a batch of data
"""
# Get images and labels
images, labels = fer2013.distorted_inputs(TRAIN_INPUT_FILE)
keep_prob = 0.5
# Build inference Graph.
logits = fer2013.inference(images, keep_prob, FLAGS.train_batch_size)
# Build the portion of the Graph calculating the losses. Note that we will
# assemble the total_loss using a custom function below.
_ = fer2013.loss(logits, labels)
acc = fer2013.accuracy(logits, labels)
# Assemble all of the losses for the current tower only.
losses = tf.get_collection('losses', scope)
# Calculate the total loss for the current tower.
total_loss = tf.add_n(losses, name='total_loss')
# Compute the moving average of all individual losses and the total loss.
loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
loss_averages_op = loss_averages.apply(losses + [total_loss])
# Attach a scalar summary to all individual losses and the total loss; do the
# same for the averaged version of the losses.
for l in losses + [total_loss]:
# Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
# session. This helps the clarity of presentation on tensorboard.
loss_name = re.sub('%s_[0-9]*/' % fer2013.TOWER_NAME, '', l.op.name)
# Name each loss as '(raw)' and name the moving average version of the loss
# as the original loss name.
tf.summary.scalar(loss_name + ' (raw)', l)
tf.summary.scalar(loss_name, loss_averages.average(l))
with tf.control_dependencies([loss_averages_op]):
total_loss = tf.identity(total_loss)
return total_loss
def tower_loss(scope):
"""Calculate the total loss on a single tower running the CIFAR model.
Args:
scope: unique prefix string identifying the CIFAR tower, e.g. 'tower_0'
Returns:
Tensor of shape [] containing the total loss for a batch of data
"""
# Get images and labels for CIFAR-10.
images, labels = fer2013.distorted_inputs()
# Build inference Graph.
logits = fer2013.inference(images)
# Build the portion of the Graph calculating the losses. Note that we will
# assemble the total_loss using a custom function below.
_ = fer2013.loss(logits, labels)
# Assemble all of the losses for the current tower only.
losses = tf.get_collection('losses', scope)
# Calculate the total loss for the current tower.
total_loss = tf.add_n(losses, name='total_loss')
# Compute the moving average of all individual losses and the total loss.
loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
loss_averages_op = loss_averages.apply(losses + [total_loss])
# Attach a scalar summary to all individual losses and the total loss; do the
# same for the averaged version of the losses.
for l in losses + [total_loss]:
# Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
# session. This helps the clarity of presentation on tensorboard.
loss_name = re.sub('%s_[0-9]*/' % fer2013.TOWER_NAME, '', l.op.name)
# Name each loss as '(raw)' and name the moving average version of the loss
# as the original loss name.
tf.scalar_summary(loss_name +' (raw)', l)
tf.scalar_summary(loss_name, loss_averages.average(l))
with tf.control_dependencies([loss_averages_op]):
total_loss = tf.identity(total_loss)
return total_loss
def train():
"""Train FER-2013 for a number of steps."""
with tf.Graph().as_default():
global_step = tf.contrib.framework.get_or_create_global_step()
# Get images and labels for FER2013.
# Force input pipeline to CPU:0 to avoid operations sometimes ending up on
# GPU and resulting in a slow down.
with tf.device('/cpu:0'):
images, labels = fer2013.distorted_inputs(TRAIN_INPUT_FILE)
keep_prob = 0.7
# Build a Graph that computes the logits predictions from the
# inference model.
logits = fer2013.inference(images, keep_prob, FLAGS.train_batch_size)
# Visualize conv1 kernels
with tf.variable_scope('conv1'):
tf.get_variable_scope().reuse_variables()
weights = tf.get_variable('weights')
grid = put_kernels_on_grid(weights)
tf.summary.image('conv1/kernels', grid, max_outputs=1)
top_k_op = tf.nn.in_top_k(logits, labels, 1)
# Calculate loss.
loss = fer2013.loss(logits, labels)
acc = fer2013.accuracy(logits, labels)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = fer2013.train(loss, global_step)
# Create a saver.
saver = tf.train.Saver(tf.global_variables())
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.summary.merge_all()
# Build an initialization operation to run below.
init = tf.global_variables_initializer()
# Start running operations on the Graph.
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
# Start the queue runners.
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.summary.FileWriter(FLAGS.train_dir,
sess.graph)
epoch_size = int(fer2013.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.train_batch_size)
epoch_count = 0
total_sample_count = epoch_size * FLAGS.train_batch_size
init_local = tf.local_variables_initializer()
sess.run(init_local)
for step in xrange(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 10 == 0:
accu = sess.run([acc])
print('Acc: ', accu)
num_examples_per_step = FLAGS.train_batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print (format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
if step % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
# Save the model checkpoint periodically.
if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
if step % epoch_size == 0:
epoch_count += 1
print('epoch: ' + str(epoch_count))
print('Training finished')
true_count = 0
st = 0
while st < epoch_size:
predictions = sess.run([top_k_op])
true_count += np.sum(predictions)
st += 1
accuracy = true_count / total_sample_count
print("Accuracy: ", accuracy, ' right predicted: ', true_count)
def train():
"""Train FER2013 for a number of steps."""
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
# Get images and labels for FER2013.
images, labels = fer2013.distorted_inputs()
# Build a Graph that computes the logits predictions from the
# inference model.
logits = fer2013.inference(images)
# Calculate loss.
loss = fer2013.loss(logits, labels)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = fer2013.train(loss, global_step)
# Create a saver.
saver = tf.train.Saver(tf.all_variables())
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
# Start the queue runners.
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir,
graph_def=sess.graph_def)
for step in xrange(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_input_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print (format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
if step % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
# Save the model checkpoint periodically.
if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
def predict():
"""Eval FER2013 for a number of steps."""
with tf.Graph().as_default():
images, _ = fer2013.inputs(eval_data=FLAGS.eval_data,
input_file=input_image_csv)
keep_prob = 1
# Build a Graph that computes the logits predictions from the
# inference model.
logits = fer2013.inference(
images, keep_prob,
-1) # batch size -1: accepts dynamic batch sizes
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
fer2013.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# ---------------------- Version 1 with top k predictions --------------------
_, top_k_pred = tf.nn.top_k(logits, k=3)
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
print("checkpoint file found")
else:
print('No checkpoint file found')
return
# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
top_indices = sess.run([top_k_pred])
print("Predicted as TOP 3: ", top_indices[0][0],
" for your input image.")
print('Predicted as 1.: ', top_indices[0][0][0], ' -> ',
emotion_dict[top_indices[0][0][0]])
print('Predicted as 2.: ', top_indices[0][0][1], ' -> ',
emotion_dict[top_indices[0][0][1]])
print('Predicted as 3.: ', top_indices[0][0][2], ' -> ',
emotion_dict[top_indices[0][0][2]])
coord.request_stop()
coord.join(threads)
# -------------------- end Version 1 -----------------------
# Version 2 with argmax
# Restore the moving average version of the learned variables for eval.
"""
prediction = make_prediction(saver=saver, logits=logits)
print('Predicted emotion: ', prediction[0], ' -> ', emotion_dict[prediction[0]])
"""
# ------------------ end Version 2 --------------------
img = cv2.imread(local_directory + input_image_png, 0)
cv2.imshow("Emotion Image", img)
print(
"--------------- Press ENTER to return to Webcam ---------------")
key = cv2.waitKey(0)
if key == 13:
cv2.destroyAllWindows()
def train():
"""Train FER2013 for a number of steps."""
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
# Get images and labels for FER2013.
images, labels = fer2013.distorted_inputs()
# Build a Graph that computes the logits predictions from the
# inference model.
logits = fer2013.inference(images)
# Calculate loss.
loss = fer2013.loss(logits, labels)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = fer2013.train(loss, global_step)
# Create a saver.
saver = tf.train.Saver(tf.all_variables())
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
# Start the queue runners.
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir,
graph_def=sess.graph_def)
for step in xrange(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_input_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print (format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
if step % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
# Save the model checkpoint periodically.
if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)