TensorFlow/MNIST
From charlesreid1
Contents
Simple MNIST Convolutional Network
Input Function
Define an input function. This has an internal function that parses the example data (one piece of data at a time) and one-hot encodes the labeled images with the digit it corresponds to.
def input_fn(mode, batch_size=1):
"""A simple input_fn using the contrib.data input pipeline."""
def example_parser(serialized_example):
"""Parses a single tf.Example into image and label tensors."""
features = tf.parse_single_example(
serialized_example,
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64),
})
image = tf.decode_raw(features['image_raw'], tf.uint8)
image.set_shape([28 * 28])
# Normalize the values of the image from the range [0, 255] to [-0.5, 0.5]
image = tf.cast(image, tf.float32) / 255 - 0.5
label = tf.cast(features['label'], tf.int32)
return image, tf.one_hot(label, 10)
if mode == tf.estimator.ModeKeys.TRAIN:
tfrecords_file = os.path.join(FLAGS.data_dir, 'train.tfrecords')
else:
assert mode == tf.estimator.ModeKeys.EVAL, 'invalid mode'
tfrecords_file = os.path.join(FLAGS.data_dir, 'test.tfrecords')
assert tf.gfile.Exists(tfrecords_file), (
'Run convert_to_records.py first to convert the MNIST data to TFRecord '
'file format.')
dataset = tf.contrib.data.TFRecordDataset([tfrecords_file])
# For training, repeat the dataset forever
if mode == tf.estimator.ModeKeys.TRAIN:
dataset = dataset.repeat()
# Map example_parser over dataset, and batch results by up to batch_size
dataset = dataset.map(
example_parser, num_threads=1, output_buffer_size=batch_size)
dataset = dataset.batch(batch_size)
images, labels = dataset.make_one_shot_iterator().get_next()
return images, labels
Prepare Model
def mnist_model(inputs, mode):
"""Takes the MNIST inputs and mode and outputs a tensor of logits."""
# Input Layer
# Reshape X to 4-D tensor: [batch_size, width, height, channels]
# MNIST images are 28x28 pixels, and have one color channel
inputs = tf.reshape(inputs, [-1, 28, 28, 1])
data_format = FLAGS.data_format
if data_format is None:
# When running on GPU, transpose the data from channels_last (NHWC) to
# channels_first (NCHW) to improve performance.
# See https://www.tensorflow.org/performance/performance_guide#data_formats
data_format = ('channels_first' if tf.test.is_built_with_cuda() else
'channels_last')
if data_format == 'channels_first':
inputs = tf.transpose(inputs, [0, 3, 1, 2])
Construct Model
# Convolutional Layer #1
# Computes 32 features using a 5x5 filter with ReLU activation.
# Padding is added to preserve width and height.
# Input Tensor Shape: [batch_size, 28, 28, 1]
# Output Tensor Shape: [batch_size, 28, 28, 32]
conv1 = tf.layers.conv2d(
inputs=inputs,
filters=32,
kernel_size=[5, 5],
padding='same',
activation=tf.nn.relu,
data_format=data_format)
# Pooling Layer #1
# First max pooling layer with a 2x2 filter and stride of 2
# Input Tensor Shape: [batch_size, 28, 28, 32]
# Output Tensor Shape: [batch_size, 14, 14, 32]
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2,
data_format=data_format)
# Convolutional Layer #2
# Computes 64 features using a 5x5 filter.
# Padding is added to preserve width and height.
# Input Tensor Shape: [batch_size, 14, 14, 32]
# Output Tensor Shape: [batch_size, 14, 14, 64]
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding='same',
activation=tf.nn.relu,
data_format=data_format)
# Pooling Layer #2
# Second max pooling layer with a 2x2 filter and stride of 2
# Input Tensor Shape: [batch_size, 14, 14, 64]
# Output Tensor Shape: [batch_size, 7, 7, 64]
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2,
data_format=data_format)
# Flatten tensor into a batch of vectors
# Input Tensor Shape: [batch_size, 7, 7, 64]
# Output Tensor Shape: [batch_size, 7 * 7 * 64]
pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
# Dense Layer
# Densely connected layer with 1024 neurons
# Input Tensor Shape: [batch_size, 7 * 7 * 64]
# Output Tensor Shape: [batch_size, 1024]
dense = tf.layers.dense(inputs=pool2_flat, units=1024,
activation=tf.nn.relu)
# Add dropout operation; 0.6 probability that element will be kept
dropout = tf.layers.dropout(
inputs=dense, rate=0.4, training=(mode == tf.estimator.ModeKeys.TRAIN))
# Logits layer
# Input Tensor Shape: [batch_size, 1024]
# Output Tensor Shape: [batch_size, 10]
logits = tf.layers.dense(inputs=dropout, units=10)
return logits
Get Estimator
def mnist_model_fn(features, labels, mode):
"""Model function for MNIST."""
logits = mnist_model(features, mode)
predictions = {
'classes': tf.argmax(input=logits, axis=1),
'probabilities': tf.nn.softmax(logits, name='softmax_tensor')
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
loss = tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=logits)
# Configure the training op
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.AdamOptimizer(learning_rate=1e-4)
train_op = optimizer.minimize(loss, tf.train.get_or_create_global_step())
else:
train_op = None
accuracy = tf.metrics.accuracy(
tf.argmax(labels, axis=1), predictions['classes'])
metrics = {'accuracy': accuracy}
# Create a tensor named train_accuracy for logging purposes
tf.identity(accuracy[1], name='train_accuracy')
tf.summary.scalar('train_accuracy', accuracy[1])
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=metrics)
Main Function
def main(unused_argv):
# Create the Estimator
mnist_classifier = tf.estimator.Estimator(
model_fn=mnist_model_fn, model_dir=FLAGS.model_dir)
# Train the model
tensors_to_log = {
'train_accuracy': 'train_accuracy'
}
logging_hook = tf.train.LoggingTensorHook(
tensors=tensors_to_log, every_n_iter=100)
batches_per_epoch = _NUM_IMAGES['train'] / FLAGS.batch_size
mnist_classifier.train(
input_fn=lambda: input_fn(tf.estimator.ModeKeys.TRAIN, FLAGS.batch_size),
steps=FLAGS.train_epochs * batches_per_epoch,
hooks=[logging_hook])
# Evaluate the model and print results
eval_results = mnist_classifier.evaluate(
input_fn=lambda: input_fn(tf.estimator.ModeKeys.EVAL))
print()
print('Evaluation results:\n %s' % eval_results)