I have modified existing cifar10 example to work as a siamese network. But I am facing some difficulties in training it.
Changes Made :
Here is my modified cifar10_train.py :
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import os.path
import time
import input_data
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
import cifar10
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('train_dir', 'tmp/cifar10_train',
"""Directory where to write event logs """
"""and checkpoint.""")
tf.app.flags.DEFINE_integer('max_steps', 1000000,
"""Number of batches to run.""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
"""Whether to log device placement.""")
def train():
"""Train CIFAR-10 for a number of steps."""
dataset = input_data.read()
image, image_p, label = dataset.train_dataset
image_size = dataset.image_size
batch_size = 28
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
# Get images and labels for CIFAR-10.
images = tf.placeholder(tf.float32, shape=(batch_size, image_size[0], image_size[1], image_size[2]))
images2 = tf.placeholder(tf.float32, shape=(batch_size, image_size[0], image_size[1], image_size[2]))
labels = tf.placeholder(tf.float32, shape=(batch_size))
tf.image_summary('images', images)
tf.image_summary('images2', images)
# Build a Graph that computes the logits predictions from the
# inference model.
with tf.variable_scope('inference') as scope:
logits = cifar10.inference(images)
scope.reuse_variables()
logits2 = cifar10.inference(images2)
# Calculate loss.
loss = cifar10.loss(logits, logits2, labels)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = cifar10.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()
offset = (step * batch_size) % (dataset.train_samples - batch_size)
_, loss_value = sess.run([train_op, loss], feed_dict={images: image[offset:(offset + batch_size)], images2: image_p[offset:(offset + batch_size)], labels: 1.0*label[offset:(offset + batch_size)]})
duration = time.time() - start_time
print(loss_value)
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 10 == 0:
num_examples_per_step = FLAGS.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)
def main(argv=None):
# pylint: disable=unused-argument
train()
if __name__ == '__main__':
tf.app.run()
Modified cifar10.py
"""Builds the CIFAR-10 network.
Summary of available functions:
# Compute input images and labels for training. If you would like to run
# evaluations, use inputs() instead.
inputs, labels = distorted_inputs()
# Compute inference on the model inputs to make a prediction.
predictions = inference(inputs)
# Compute the total loss of the prediction with respect to the labels.
loss = loss(predictions, labels)
# Create a graph to run one step of training with respect to the loss.
train_op = train(loss, global_step)
"""
# pylint: disable=missing-docstring
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gzip
import os
import re
import sys
import tarfile
from six.moves import urllib
import tensorflow as tf
import input_data
FLAGS = tf.app.flags.FLAGS
# Basic model parameters.
tf.app.flags.DEFINE_integer('batch_size', 28,
"""Number of images to process in a batch.""")
tf.app.flags.DEFINE_string('data_dir_p', '/tmp/cifar10_data',
"""Path to the CIFAR-10 data directory.""")
# Global constants describing the CIFAR-10 data set.
# IMAGE_SIZE = cifar10_input.IMAGE_SIZE
# NUM_CLASSES = cifar10_input.NUM_CLASSES
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = input_data.train_samples
# NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_EVAL
# Constants describing the training process.
MOVING_AVERAGE_DECAY = 0.9999 # The decay to use for the moving average.
NUM_EPOCHS_PER_DECAY = 350.0 # Epochs after which learning rate decays.
LEARNING_RATE_DECAY_FACTOR = 0.1 # Learning rate decay factor.
INITIAL_LEARNING_RATE = 0.001 # Initial learning rate.
Q = 360.6244
# If a model is trained with multiple GPU's prefix all Op names with tower_name
# to differentiate the operations. Note that this prefix is removed from the
# names of the summaries when visualizing a model.
TOWER_NAME = 'tower'
DATA_URL = 'http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'
def _activation_summary(x):
# Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
# session. This helps the clarity of presentation on tensorboard.
tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name)
tf.histogram_summary(tensor_name + '/activations', x)
tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x))
def _variable_on_cpu(name, shape, initializer):
with tf.device('/cpu:0'):
var = tf.get_variable(name, shape, initializer=initializer)
return var
def _variable_with_weight_decay(name, shape, stddev, wd):
var = _variable_on_cpu(name, shape, tf.truncated_normal_initializer(stddev=stddev))
if wd:
weight_decay = tf.mul(tf.nn.l2_loss(var), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
return var
def inference(data):
# We instantiate all variables using tf.get_variable() instead of
# tf.Variable() in order to share variables across multiple GPU training runs.
# If we only ran this model on a single GPU, we could simplify this function
# by replacing all instances of tf.get_variable() with tf.Variable().
#
# conv1
with tf.variable_scope('conv1') as scope:
kernel = _variable_with_weight_decay('weights', shape=[5, 5, 1, 20],
stddev=0.1, wd=0.0)
conv = tf.nn.conv2d(data, kernel, [1, 1, 1, 1], padding='VALID')
biases = _variable_on_cpu('biases', [20], tf.constant_initializer(0.0))
conv1 = tf.nn.bias_add(conv, biases)
_activation_summary(conv1)
# pool1
pool1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
padding='VALID', name='pool1')
# conv2
with tf.variable_scope('conv2') as scope:
kernel = _variable_with_weight_decay('weights', shape=[5, 5, 20, 50],
stddev=0.1, wd=0.0)
conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding='VALID')
biases = _variable_on_cpu('biases', [50], tf.constant_initializer(0.0))
conv2 = tf.nn.bias_add(conv, biases)
_activation_summary(conv2)
# pool2
pool2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='VALID', name='pool2')
# local3
with tf.variable_scope('local3') as scope:
# Move everything into depth so we can perform a single matrix multiply.
dim = 1
for d in pool2.get_shape()[1:].as_list():
dim *= d
reshape = tf.reshape(pool2, [pool2.get_shape()[0:].as_list()[0], dim])
weights = _variable_with_weight_decay('weights', shape=[dim, 500],
stddev=0.1, wd=0.0)
biases = _variable_on_cpu('biases', [500], tf.constant_initializer(0.10))
local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
_activation_summary(local3)
# local4
with tf.variable_scope('local4') as scope:
weights = _variable_with_weight_decay('weights', shape=[500, 10],
stddev=0.1, wd=0.0)
biases = _variable_on_cpu('biases', [10], tf.constant_initializer(0.0))
local4 = tf.add(tf.matmul(local3, weights), biases, name=scope.name)
_activation_summary(local4)
#local5
with tf.variable_scope('local5') as scope:
weights = _variable_with_weight_decay('weights', [10, 10],
stddev=0.1, wd=0.0)
biases = _variable_on_cpu('biases', [10],
tf.constant_initializer(0.0))
local5 = tf.add(tf.matmul(local4, weights), biases, name=scope.name)
_activation_summary(local5)
return local5
def loss(features1, features2, labels):
energy_square = (tf.reduce_sum(tf.pow(tf.sub(features1, features2), 2),1))
loss = tf.add(tf.mul(tf.pow(tf.sub(labels,1),2),energy_square),tf.mul(labels,tf.maximum(tf.sub(1.0,energy_square),0)))
loss = tf.reduce_sum(loss) / features1.get_shape()[0:].as_list()[0] / 2
# Calculate the average cross entropy loss across the batch.
# labels = tf.cast(labels, tf.int64)
# cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
# logits, labels, name='cross_entropy_per_example')
# cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', loss)
# The total loss is defined as the cross entropy loss plus all of the weight
# decay terms (L2 loss).
return tf.add_n(tf.get_collection('losses'), name='total_loss')
def _add_loss_summaries(total_loss):
# Compute the moving average of all individual losses and the total loss.
loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
losses = tf.get_collection('losses')
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]:
# Name each loss as '(raw)' and name the moving average version of the loss
# as the original loss name.
tf.scalar_summary(l.op.name +' (raw)', l)
tf.scalar_summary(l.op.name, loss_averages.average(l))
return loss_averages_op
def train(total_loss, global_step):
loss_averages_op = _add_loss_summaries(total_loss)
num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size
decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY)
# Decay the learning rate exponentially based on the number of steps.
lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE,
global_step,
decay_steps,
LEARNING_RATE_DECAY_FACTOR,
staircase=True)
tf.scalar_summary('learning_rate', lr)
# Generate moving averages of all losses and associated summaries.
loss_averages_op = _add_loss_summaries(total_loss)
# Compute gradients.
with tf.control_dependencies([loss_averages_op]):
opt = tf.train.GradientDescentOptimizer(lr)
grads = opt.compute_gradients(total_loss)
# Apply gradients.
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Add histograms for trainable variables.
for var in tf.trainable_variables():
tf.histogram_summary(var.op.name, var)
# Add histograms for gradients.
for grad, var in grads:
if grad:
tf.histogram_summary(var.op.name + '/gradients', grad)
# Track the moving averages of all trainable variables.
variable_averages = tf.train.ExponentialMovingAverage(
MOVING_AVERAGE_DECAY, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
with tf.control_dependencies([apply_gradient_op, variables_averages_op]):
train_op = tf.no_op(name='train')
return train_op
Error I am getting :
2016-03-01 15:56:59.483682: step 0, loss = 0.22 (9.7 examples/sec; 2.896 sec/batch)
W tensorflow/core/common_runtime/executor.cc:1102] 0x7fd2340e8b60 Compute status: Invalid argument: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [28,112,92,1]
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[28,112,92,1], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
W tensorflow/core/common_runtime/executor.cc:1102] 0x7fd2340e8b60 Compute status: Out of range: Nan in summary histogram for: HistogramSummary
[[Node: HistogramSummary = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](HistogramSummary/tag, inference/conv1/weights/read)]]
W tensorflow/core/common_runtime/executor.cc:1102] 0x7fd2340e8b60 Compute status: Out of range: Nan in summary histogram for: HistogramSummary_1
[[Node: HistogramSummary_1 = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](HistogramSummary_1/tag, inference/conv1/biases/read)]]
W tensorflow/core/common_runtime/executor.cc:1102] 0x7fd2340e8b60 Compute status: Invalid argument: You must feed a value for placeholder tensor 'Placeholder_2' with dtype float and shape [28]
[[Node: Placeholder_2 = Placeholder[dtype=DT_FLOAT, shape=[28], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
W tensorflow/core/common_runtime/executor.cc:1102] 0x7fd2340e8b60 Compute status: Out of range: Nan in summary histogram for: HistogramSummary_3
[[Node: HistogramSummary_3 = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](HistogramSummary_3/tag, inference/conv2/biases/read)]]
W tensorflow/core/common_runtime/executor.cc:1102] 0x7fd2340e8b60 Compute status: Out of range: Nan in summary histogram for: HistogramSummary_2
[[Node: HistogramSummary_2 = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](HistogramSummary_2/tag, inference/conv2/weights/read)]]
W tensorflow/core/common_runtime/executor.cc:1102] 0x7fd2340e8b60 Compute status: Out of range: Nan in summary histogram for: HistogramSummary_4
[[Node: HistogramSummary_4 = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](HistogramSummary_4/tag, inference/local3/weights/read)]]
W tensorflow/core/common_runtime/executor.cc:1102] 0x7fd2340e8b60 Compute status: Out of range: Nan in summary histogram for: HistogramSummary_5
[[Node: HistogramSummary_5 = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](HistogramSummary_5/tag, inference/local3/biases/read)]]
W tensorflow/core/common_runtime/executor.cc:1102] 0x7fd2340e8b60 Compute status: Out of range: Nan in summary histogram for: HistogramSummary_6
[[Node: HistogramSummary_6 = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](HistogramSummary_6/tag, inference/local4/weights/read)]]
W tensorflow/core/common_runtime/executor.cc:1102] 0x7fd2340e8b60 Compute status: Out of range: Nan in summary histogram for: HistogramSummary_7
[[Node: HistogramSummary_7 = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](HistogramSummary_7/tag, inference/local4/biases/read)]]
W tensorflow/core/common_runtime/executor.cc:1102] 0x7fd2340e8b60 Compute status: Out of range: Nan in summary histogram for: HistogramSummary_8
[[Node: HistogramSummary_8 = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](HistogramSummary_8/tag, inference/local5/weights/read)]]
W tensorflow/core/common_runtime/executor.cc:1102] 0x7fd2340e8b60 Compute status: Out of range: Nan in summary histogram for: HistogramSummary_9
[[Node: HistogramSummary_9 = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](HistogramSummary_9/tag, inference/local5/biases/read)]]
Traceback (most recent call last):
File "cifar10_train.py", line 110, in <module>
tf.app.run()
File "/Users/Macbull/Desktop/GITHUB/tensorflow/venv/lib/python2.7/site-packages/tensorflow/python/platform/default/_app.py", line 30, in run
sys.exit(main(sys.argv))
File "cifar10_train.py", line 106, in main
train()
File "cifar10_train.py", line 95, in train
summary_str = sess.run(summary_op)
File "/Users/Macbull/Desktop/GITHUB/tensorflow/venv/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 315, in run
return self._run(None, fetches, feed_dict)
File "/Users/Macbull/Desktop/GITHUB/tensorflow/venv/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 511, in _run
feed_dict_string)
File "/Users/Macbull/Desktop/GITHUB/tensorflow/venv/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 564, in _do_run
target_list)
File "/Users/Macbull/Desktop/GITHUB/tensorflow/venv/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 586, in _do_call
e.code)
tensorflow.python.framework.errors.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [28,112,92,1]
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[28,112,92,1], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op u'Placeholder', defined at:
File "cifar10_train.py", line 110, in <module>
tf.app.run()
File "/Users/Macbull/Desktop/GITHUB/tensorflow/venv/lib/python2.7/site-packages/tensorflow/python/platform/default/_app.py", line 30, in run
sys.exit(main(sys.argv))
File "cifar10_train.py", line 106, in main
train()
File "cifar10_train.py", line 36, in train
images = tf.placeholder(tf.float32, shape=(batch_size, image_size[0], image_size[1], image_size[2]))
File "/Users/Macbull/Desktop/GITHUB/tensorflow/venv/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 742, in placeholder
name=name)
File "/Users/Macbull/Desktop/GITHUB/tensorflow/venv/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 583, in _placeholder
name=name)
File "/Users/Macbull/Desktop/GITHUB/tensorflow/venv/lib/python2.7/site-packages/tensorflow/python/ops/op_def_library.py", line 655, in apply_op
op_def=op_def)
File "/Users/Macbull/Desktop/GITHUB/tensorflow/venv/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2040, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/Users/Macbull/Desktop/GITHUB/tensorflow/venv/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1087, in __init__
self._traceback = _extract_stack()
Also, when I comment out merge_all_summaries(), the model diverges with loss= NaN
The problem here is that some of the summaries in your graph—collected by tf.merge_all_summaries()— depend on your placeholders. For example, the code in cifar10.py creates summaries for various activations at each step, which depend on the training example used.
The solution is to feed the same training batch when you evaluate summary_op:
if step % 100 == 0:
summary_str = sess.run(summary_op, feed_dict={
images: image[offset:(offset + batch_size)],
images2: image_p[offset:(offset + batch_size)],
labels: 1.0 * label[offset:(offset + batch_size)]})
While this gives the smallest modification to your original code, it is slightly inefficient, because it will re-execute the training step every 100 steps. The best way to address this (although it will require some restructuring of your training loop) is to fetch the summaries in the same call to sess.run() that performs a training step:
if step % 100 == 0:
_, loss_value, summary_str = sess.run([train_op, loss, summary_op], feed_dict={
images: image[offset:(offset + batch_size)],
images2: image_p[offset:(offset + batch_size)],
labels: 1.0 * label[offset:(offset + batch_size)]})