cifar10_input.py
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Routine for decoding the CIFAR-10 binary file format."""
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from __future__ import absolute_import |
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from __future__ import division |
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from __future__ import print_function |
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import os |
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from six.moves import xrange # pylint: disable=redefined-builtin |
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import tensorflow as tf |
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# Process images of this size. Note that this differs from the original CIFAR
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# image size of 32 x 32. If one alters this number, then the entire model
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# architecture will change and any model would need to be retrained.
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IMAGE_SIZE = 24
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# Global constants describing the CIFAR-10 data set.
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NUM_CLASSES = 10
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NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
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NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000
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def read_cifar10(filename_queue): |
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"""Reads and parses examples from CIFAR10 data files.
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Recommendation: if you want N-way read parallelism, call this function
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N times. This will give you N independent Readers reading different
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files & positions within those files, which will give better mixing of
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examples.
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Args:
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filename_queue: A queue of strings with the filenames to read from.
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Returns:
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An object representing a single example, with the following fields:
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height: number of rows in the result (32)
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width: number of columns in the result (32)
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depth: number of color channels in the result (3)
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key: a scalar string Tensor describing the filename & record number
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for this example.
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label: an int32 Tensor with the label in the range 0..9.
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uint8image: a [height, width, depth] uint8 Tensor with the image data
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"""
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class CIFAR10Record(object): |
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pass
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result = CIFAR10Record() |
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# Dimensions of the images in the CIFAR-10 dataset.
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# See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the
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# input format.
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label_bytes = 1 # 2 for CIFAR-100 |
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result.height = 32
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result.width = 32
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result.depth = 3
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image_bytes = result.height * result.width * result.depth |
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# Every record consists of a label followed by the image, with a
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# fixed number of bytes for each.
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record_bytes = label_bytes + image_bytes |
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# Read a record, getting filenames from the filename_queue. No
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# header or footer in the CIFAR-10 format, so we leave header_bytes
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# and footer_bytes at their default of 0.
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reader = tf.FixedLengthRecordReader(record_bytes=record_bytes) |
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result.key, value = reader.read(filename_queue) |
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# Convert from a string to a vector of uint8 that is record_bytes long.
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record_bytes = tf.decode_raw(value, tf.uint8) |
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# The first bytes represent the label, which we convert from uint8->int32.
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result.label = tf.cast( |
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tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)
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# The remaining bytes after the label represent the image, which we reshape
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# from [depth * height * width] to [depth, height, width].
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depth_major = tf.reshape( |
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tf.strided_slice(record_bytes, [label_bytes], |
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[label_bytes + image_bytes]), |
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[result.depth, result.height, result.width]) |
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# Convert from [depth, height, width] to [height, width, depth].
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result.uint8image = tf.transpose(depth_major, [1, 2, 0]) |
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return result
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def _generate_image_and_label_batch(image, label, min_queue_examples, |
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batch_size, shuffle): |
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"""Construct a queued batch of images and labels.
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Args:
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image: 3-D Tensor of [height, width, 3] of type.float32.
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label: 1-D Tensor of type.int32
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min_queue_examples: int32, minimum number of samples to retain
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in the queue that provides of batches of examples.
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batch_size: Number of images per batch.
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shuffle: boolean indicating whether to use a shuffling queue.
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Returns:
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images: Images. 4D tensor of [batch_size, height, width, 3] size.
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labels: Labels. 1D tensor of [batch_size] size.
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"""
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# Create a queue that shuffles the examples, and then
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# read 'batch_size' images + labels from the example queue.
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num_preprocess_threads = 16
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if shuffle:
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images, label_batch = tf.train.shuffle_batch( |
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[image, label], |
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batch_size=batch_size, |
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num_threads=num_preprocess_threads, |
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capacity=min_queue_examples + 3 * batch_size,
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min_after_dequeue=min_queue_examples) |
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else:
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images, label_batch = tf.train.batch( |
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[image, label], |
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batch_size=batch_size, |
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num_threads=num_preprocess_threads, |
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capacity=min_queue_examples + 3 * batch_size)
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# Display the training images in the visualizer.
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tf.summary.image('images', images)
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return images, tf.reshape(label_batch, [batch_size])
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def distorted_inputs(data_dir, batch_size): |
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"""Construct distorted input for CIFAR training using the Reader ops.
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Args:
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data_dir: Path to the CIFAR-10 data directory.
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batch_size: Number of images per batch.
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Returns:
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images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
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labels: Labels. 1D tensor of [batch_size] size.
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"""
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filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
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for i in xrange(1, 6)] |
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for f in filenames: |
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if not tf.gfile.Exists(f): |
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raise ValueError('Failed to find file: ' + f) |
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# Create a queue that produces the filenames to read.
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filename_queue = tf.train.string_input_producer(filenames) |
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# Read examples from files in the filename queue.
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read_input = read_cifar10(filename_queue) |
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reshaped_image = tf.cast(read_input.uint8image, tf.float32) |
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height = IMAGE_SIZE |
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width = IMAGE_SIZE |
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# Image processing for training the network. Note the many random
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# distortions applied to the image.
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# Randomly crop a [height, width] section of the image.
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distorted_image = tf.random_crop(reshaped_image, [height, width, 3])
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# Randomly flip the image horizontally.
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distorted_image = tf.image.random_flip_left_right(distorted_image) |
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# Because these operations are not commutative, consider randomizing
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# the order their operation.
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# NOTE: since per_image_standardization zeros the mean and makes
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# the stddev unit, this likely has no effect see tensorflow#1458.
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distorted_image = tf.image.random_brightness(distorted_image, |
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max_delta=63)
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distorted_image = tf.image.random_contrast(distorted_image, |
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lower=0.2, upper=1.8) |
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# Subtract off the mean and divide by the variance of the pixels.
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float_image = tf.image.per_image_standardization(distorted_image) |
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# Set the shapes of tensors.
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float_image.set_shape([height, width, 3])
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read_input.label.set_shape([1])
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# Ensure that the random shuffling has good mixing properties.
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min_fraction_of_examples_in_queue = 0.4
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min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN *
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min_fraction_of_examples_in_queue) |
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print ('Filling queue with %d CIFAR images before starting to train. ' |
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'This will take a few minutes.' % min_queue_examples)
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# Generate a batch of images and labels by building up a queue of examples.
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return _generate_image_and_label_batch(float_image, read_input.label,
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min_queue_examples, batch_size, |
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shuffle=True)
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def inputs(eval_data, data_dir, batch_size): |
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"""Construct input for CIFAR evaluation using the Reader ops.
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Args:
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eval_data: bool, indicating if one should use the train or eval data set.
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data_dir: Path to the CIFAR-10 data directory.
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batch_size: Number of images per batch.
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Returns:
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images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
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labels: Labels. 1D tensor of [batch_size] size.
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"""
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if not eval_data: |
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filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
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for i in xrange(1, 6)] |
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num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN |
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else:
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filenames = [os.path.join(data_dir, 'test_batch.bin')]
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num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL |
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for f in filenames: |
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if not tf.gfile.Exists(f): |
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raise ValueError('Failed to find file: ' + f) |
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# Create a queue that produces the filenames to read.
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filename_queue = tf.train.string_input_producer(filenames) |
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# Read examples from files in the filename queue.
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read_input = read_cifar10(filename_queue) |
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reshaped_image = tf.cast(read_input.uint8image, tf.float32) |
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height = IMAGE_SIZE |
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width = IMAGE_SIZE |
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# Image processing for evaluation.
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# Crop the central [height, width] of the image.
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resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image, |
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height, width) |
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# Subtract off the mean and divide by the variance of the pixels.
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float_image = tf.image.per_image_standardization(resized_image) |
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# Set the shapes of tensors.
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float_image.set_shape([height, width, 3])
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read_input.label.set_shape([1])
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# Ensure that the random shuffling has good mixing properties.
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min_fraction_of_examples_in_queue = 0.4
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min_queue_examples = int(num_examples_per_epoch *
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min_fraction_of_examples_in_queue) |
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# Generate a batch of images and labels by building up a queue of examples.
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return _generate_image_and_label_batch(float_image, read_input.label,
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min_queue_examples, batch_size, |
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shuffle=False)
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