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"""Converts MNIST data to TFRecords file format with Example protos."""
from __future__ import print_function
import argparse
import os.path
import sys
import time
import os
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline
# plt configuration
plt.rcParams['figure.figsize'] = (10, 10) # size of images
plt.rcParams['image.interpolation'] = 'nearest' # show exact image
plt.rcParams['image.cmap'] = 'gray' # use grayscale
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from tensorflow.contrib.learn.python.learn.datasets import mnist
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def convert_to(data_set, name):
"""Converts a dataset to tfrecords."""
images = data_set.images
labels = data_set.labels
num_examples = data_set.num_examples
rows = images.shape[1]
cols = images.shape[2]
depth = images.shape[3]
filename = os.path.join('/tmp/data', name + '.tfrecords')
print('Writing', filename)
writer = tf.python_io.TFRecordWriter(filename)
for index in range(num_examples):
image_raw = images[index].tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'height': _int64_feature(rows),
'width' : _int64_feature(cols),
'depth' : _int64_feature(depth),
'label' : _int64_feature(int(labels[index])),
'image_raw': _bytes_feature(image_raw)}))
writer.write(example.SerializeToString())
writer.close()
# Get the data.
data_sets = mnist.read_data_sets('/tmp/data',
dtype=tf.uint8,
reshape=False,
validation_size=5000)
# Convert to Examples and write the result to TFRecords.
convert_to(data_sets.train, 'train')
convert_to(data_sets.validation, 'validation')
convert_to(data_sets.test, 'test')
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def read_and_decode(filename_queue):
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64),
})
# Convert from a scalar string tensor (whose single string has
# length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
# [mnist.IMAGE_PIXELS].
image = tf.decode_raw(features['image_raw'], tf.uint8)
print(image)
image.set_shape(784)
# OPTIONAL: Could reshape into a 28x28 image and apply distortions
# here. Since we are not applying any distortions in this
# example, and the next step expects the image to be flattened
# into a vector, we don't bother.
# Convert from [0, 255] -> [-0.5, 0.5] floats.
image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
# Convert label from a scalar uint8 tensor to an int32 scalar.
label = tf.cast(features['label'], tf.int32)
return image, label
def inputs(train, batch_size, num_epochs):
"""Reads input data num_epochs times.
Args:
train: Selects between the training (True) and validation (False) data.
batch_size: Number of examples per returned batch.
num_epochs: Number of times to read the input data, or 0/None to
train forever.
Returns:
A tuple (images, labels), where:
* images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS]
in the range [-0.5, 0.5].
* labels is an int32 tensor with shape [batch_size] with the true label,
a number in the range [0, mnist.NUM_CLASSES).
Note that an tf.train.QueueRunner is added to the graph, which
must be run using e.g. tf.train.start_queue_runners().
"""
if not num_epochs: num_epochs = None
filename = os.path.join('/tmp/data',
'train.tfrecords' if train else 'validation.tfrecords')
with tf.name_scope('input'):
filename_queue = tf.train.string_input_producer(
[filename], num_epochs=num_epochs)
# Even when reading in multiple threads, share the filename
# queue.
image, label = read_and_decode(filename_queue)
# Shuffle the examples and collect them into batch_size batches.
# (Internally uses a RandomShuffleQueue.)
# We run this in two threads to avoid being a bottleneck.
images, sparse_labels = tf.train.shuffle_batch(
[image, label], batch_size=batch_size, num_threads=2,
capacity=1000 + 3 * batch_size,
# Ensures a minimum amount of shuffling of examples.
min_after_dequeue=1000)
return images, sparse_labels
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#Show images
filename = os.path.join('/tmp/data', TRAIN_FILE)
filename_queue = tf.train.string_input_producer([filename], num_epochs=1)
image, target = read_and_decode(filename_queue)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session() as sess:
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
# Let's read off 3 batches just for example
for i in range(3):
img, label = sess.run([image, target])
print(label)
plt.imshow(np.reshape(img,[28,28]))
plt.show()
coord.request_stop()
coord.join(threads)
image, label = read_and_decode(filename_queue)
print(image.shape)
print(label)
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from __future__ import print_function
#Basic libraries
import numpy as np
import tensorflow as tf
import time
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="0"
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Input images and labels.
images, labels = inputs(train=True, batch_size=256,
num_epochs=2)
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y_pred = tf.nn.softmax(tf.matmul(images,W) + b)
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y_pred, labels=labels)
train_op = tf.train.AdamOptimizer(0.01).minimize(loss)
# The op for initializing the variables.
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
# Create a session for running operations in the Graph.
gpu_options = tf.GPUOptions(allow_growth = True)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
# Initialize the variables (the trained variables and the
# epoch counter).
sess.run(init_op)
# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
step = 0
while not coord.should_stop():
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
# Print an overview fairly often.
if step % 100 == 0:
print('Step %d: loss = %.2f (%.3f sec)' % (step, np.mean(loss_value), duration))
step += 1
except tf.errors.OutOfRangeError:
print('Done training for %d epochs, %d steps.' % (2, step))
finally:
# When done, ask the threads to stop.
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
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