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# MNIST in TensorFlow
This directory builds a convolutional neural net to classify the [MNIST
dataset](http://yann.lecun.com/exdb/mnist/) using the
[tf.data](https://www.tensorflow.org/api_docs/python/tf/data),
[tf.estimator.Estimator](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator),
and
[tf.layers](https://www.tensorflow.org/api_docs/python/tf/layers)
APIs.
## Setup
To begin, you'll simply need the latest version of TensorFlow installed.
First make sure you've [added the models folder to your Python path]:
```shell
export PYTHONPATH="$PYTHONPATH:/path/to/models"
```
Otherwise you may encounter an error like `ImportError: No module named official.mnist`.
Then to train the model, run the following:
```
python mnist.py
```
The model will begin training and will automatically evaluate itself on the
validation data.
Illustrative unit tests and benchmarks can be run with:
```
python mnist_test.py
python mnist_test.py --benchmarks=.
```
## Exporting the model
You can export the model into Tensorflow [SavedModel](https://www.tensorflow.org/guide/saved_model) format by using the argument `--export_dir`:
```
python mnist.py --export_dir /tmp/mnist_saved_model
```
The SavedModel will be saved in a timestamped directory under `/tmp/mnist_saved_model/` (e.g. `/tmp/mnist_saved_model/1513630966/`).
**Getting predictions with SavedModel**
Use [`saved_model_cli`](https://www.tensorflow.org/guide/saved_model#cli_to_inspect_and_execute_savedmodel) to inspect and execute the SavedModel.
```
saved_model_cli run --dir /tmp/mnist_saved_model/TIMESTAMP --tag_set serve --signature_def classify --inputs image=examples.npy
```
`examples.npy` contains the data from `example5.png` and `example3.png` in a numpy array, in that order. The array values are normalized to values between 0 and 1.
The output should look similar to below:
```
Result for output key classes:
[5 3]
Result for output key probabilities:
[[ 1.53558474e-07 1.95694142e-13 1.31193523e-09 5.47467265e-03
5.85711526e-22 9.94520664e-01 3.48423509e-06 2.65365645e-17
9.78631419e-07 3.15522470e-08]
[ 1.22413359e-04 5.87615965e-08 1.72251271e-06 9.39960718e-01
3.30306928e-11 2.87386645e-02 2.82353517e-02 8.21146413e-18
2.52568233e-03 4.15460236e-04]]
```
## Experimental: Eager Execution
[Eager execution](https://research.googleblog.com/2017/10/eager-execution-imperative-define-by.html)
(an preview feature in TensorFlow 1.5) is an imperative interface to TensorFlow.
The exact same model defined in `mnist.py` can be trained without creating a
TensorFlow graph using:
```
python mnist_eager.py
```
## Experimental: TPU Acceleration
`mnist.py` (and `mnist_eager.py`) demonstrate training a neural network to
classify digits on CPUs and GPUs. `mnist_tpu.py` can be used to train the
same model using TPUs for hardware acceleration. More information in
the [tensorflow/tpu](https://github.com/tensorflow/tpu) repository.
official/mnist/dataset.py deleted 100644 → 0
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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""tf.data.Dataset interface to the MNIST dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gzip
import os
import shutil
import tempfile
import numpy as np
from six.moves import urllib
import tensorflow as tf
def read32(bytestream):
"""Read 4 bytes from bytestream as an unsigned 32-bit integer."""
dt = np.dtype(np.uint32).newbyteorder('>')
return np.frombuffer(bytestream.read(4), dtype=dt)[0]
def check_image_file_header(filename):
"""Validate that filename corresponds to images for the MNIST dataset."""
with tf.io.gfile.GFile(filename, 'rb') as f:
magic = read32(f)
read32(f) # num_images, unused
rows = read32(f)
cols = read32(f)
if magic != 2051:
raise ValueError('Invalid magic number %d in MNIST file %s' % (magic,
f.name))
if rows != 28 or cols != 28:
raise ValueError(
'Invalid MNIST file %s: Expected 28x28 images, found %dx%d' %
(f.name, rows, cols))
def check_labels_file_header(filename):
"""Validate that filename corresponds to labels for the MNIST dataset."""
with tf.io.gfile.GFile(filename, 'rb') as f:
magic = read32(f)
read32(f) # num_items, unused
if magic != 2049:
raise ValueError('Invalid magic number %d in MNIST file %s' % (magic,
f.name))
def download(directory, filename):
"""Download (and unzip) a file from the MNIST dataset if not already done."""
filepath = os.path.join(directory, filename)
if tf.io.gfile.exists(filepath):
return filepath
if not tf.io.gfile.exists(directory):
tf.io.gfile.makedirs(directory)
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
url = 'https://storage.googleapis.com/cvdf-datasets/mnist/' + filename + '.gz'
_, zipped_filepath = tempfile.mkstemp(suffix='.gz')
print('Downloading %s to %s' % (url, zipped_filepath))
urllib.request.urlretrieve(url, zipped_filepath)
with gzip.open(zipped_filepath, 'rb') as f_in, \
tf.io.gfile.GFile(filepath, 'wb') as f_out:
shutil.copyfileobj(f_in, f_out)
os.remove(zipped_filepath)
return filepath
def dataset(directory, images_file, labels_file):
"""Download and parse MNIST dataset."""
images_file = download(directory, images_file)
labels_file = download(directory, labels_file)
check_image_file_header(images_file)
check_labels_file_header(labels_file)
def decode_image(image):
# Normalize from [0, 255] to [0.0, 1.0]
image = tf.io.decode_raw(image, tf.uint8)
image = tf.cast(image, tf.float32)
image = tf.reshape(image, [784])
return image / 255.0
def decode_label(label):
label = tf.io.decode_raw(label, tf.uint8) # tf.string -> [tf.uint8]
label = tf.reshape(label, []) # label is a scalar
return tf.cast(label, tf.int32)
images = tf.data.FixedLengthRecordDataset(
images_file, 28 * 28, header_bytes=16).map(decode_image)
labels = tf.data.FixedLengthRecordDataset(
labels_file, 1, header_bytes=8).map(decode_label)
return tf.data.Dataset.zip((images, labels))
def train(directory):
"""tf.data.Dataset object for MNIST training data."""
return dataset(directory, 'train-images-idx3-ubyte',
'train-labels-idx1-ubyte')
def test(directory):
"""tf.data.Dataset object for MNIST test data."""
return dataset(directory, 't10k-images-idx3-ubyte', 't10k-labels-idx1-ubyte')
official/mnist/mnist.py deleted 100644 → 0
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convolutional Neural Network Estimator for MNIST, built with tf.layers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app as absl_app
from absl import flags
from six.moves import range
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.mnist import dataset
from official.utils.flags import core as flags_core
from official.utils.logs import hooks_helper
from official.utils.misc import distribution_utils
from official.utils.misc import model_helpers
LEARNING_RATE = 1e-4
def create_model(data_format):
"""Model to recognize digits in the MNIST dataset.
Network structure is equivalent to:
https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/examples/tutorials/mnist/mnist_deep.py
and
https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py
But uses the tf.keras API.
Args:
data_format: Either 'channels_first' or 'channels_last'. 'channels_first' is
typically faster on GPUs while 'channels_last' is typically faster on
CPUs. See
https://www.tensorflow.org/performance/performance_guide#data_formats
Returns:
A tf.keras.Model.
"""
if data_format == 'channels_first':
input_shape = [1, 28, 28]
else:
assert data_format == 'channels_last'
input_shape = [28, 28, 1]
l = tf.keras.layers
max_pool = l.MaxPooling2D(
(2, 2), (2, 2), padding='same', data_format=data_format)
# The model consists of a sequential chain of layers, so tf.keras.Sequential
# (a subclass of tf.keras.Model) makes for a compact description.
return tf.keras.Sequential(
[
l.Reshape(
target_shape=input_shape,
input_shape=(28 * 28,)),
l.Conv2D(
32,
5,
padding='same',
data_format=data_format,
activation=tf.nn.relu),
max_pool,
l.Conv2D(
64,
5,
padding='same',
data_format=data_format,
activation=tf.nn.relu),
max_pool,
l.Flatten(),
l.Dense(1024, activation=tf.nn.relu),
l.Dropout(0.4),
l.Dense(10)
])
def define_mnist_flags():
"""Defines flags for mnist."""
flags_core.define_base(clean=True, train_epochs=True,
epochs_between_evals=True, stop_threshold=True,
num_gpu=True, hooks=True, export_dir=True,
distribution_strategy=True)
flags_core.define_performance(inter_op=True, intra_op=True,
num_parallel_calls=False,
all_reduce_alg=True)
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags_core.set_defaults(data_dir='/tmp/mnist_data',
model_dir='/tmp/mnist_model',
batch_size=100,
train_epochs=40)
def model_fn(features, labels, mode, params):
"""The model_fn argument for creating an Estimator."""
model = create_model(params['data_format'])
image = features
if isinstance(image, dict):
image = features['image']
if mode == tf.estimator.ModeKeys.PREDICT:
logits = model(image, training=False)
predictions = {
'classes': tf.argmax(logits, axis=1),
'probabilities': tf.nn.softmax(logits),
}
return tf.estimator.EstimatorSpec(
mode=tf.estimator.ModeKeys.PREDICT,
predictions=predictions,
export_outputs={
'classify': tf.estimator.export.PredictOutput(predictions)
})
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=LEARNING_RATE)
logits = model(image, training=True)
loss = tf.compat.v1.losses.sparse_softmax_cross_entropy(labels=labels,
logits=logits)
accuracy = tf.compat.v1.metrics.accuracy(
labels=labels, predictions=tf.argmax(logits, axis=1))
# Name tensors to be logged with LoggingTensorHook.
tf.identity(LEARNING_RATE, 'learning_rate')
tf.identity(loss, 'cross_entropy')
tf.identity(accuracy[1], name='train_accuracy')
# Save accuracy scalar to Tensorboard output.
tf.summary.scalar('train_accuracy', accuracy[1])
return tf.estimator.EstimatorSpec(
mode=tf.estimator.ModeKeys.TRAIN,
loss=loss,
train_op=optimizer.minimize(
loss,
tf.compat.v1.train.get_or_create_global_step()))
if mode == tf.estimator.ModeKeys.EVAL:
logits = model(image, training=False)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
return tf.estimator.EstimatorSpec(
mode=tf.estimator.ModeKeys.EVAL,
loss=loss,
eval_metric_ops={
'accuracy':
tf.metrics.accuracy(
labels=labels, predictions=tf.argmax(logits, axis=1)),
})
def run_mnist(flags_obj):
"""Run MNIST training and eval loop.
Args:
flags_obj: An object containing parsed flag values.
"""
model_helpers.apply_clean(flags_obj)
model_function = model_fn
session_config = tf.compat.v1.ConfigProto(
inter_op_parallelism_threads=flags_obj.inter_op_parallelism_threads,
intra_op_parallelism_threads=flags_obj.intra_op_parallelism_threads,
allow_soft_placement=True)
distribution_strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=flags_obj.distribution_strategy,
num_gpus=flags_core.get_num_gpus(flags_obj),
all_reduce_alg=flags_obj.all_reduce_alg)
run_config = tf.estimator.RunConfig(
train_distribute=distribution_strategy, session_config=session_config)
data_format = flags_obj.data_format
if data_format is None:
data_format = ('channels_first'
if tf.test.is_built_with_cuda() else 'channels_last')
mnist_classifier = tf.estimator.Estimator(
model_fn=model_function,
model_dir=flags_obj.model_dir,
config=run_config,
params={
'data_format': data_format,
})
# Set up training and evaluation input functions.
def train_input_fn():
"""Prepare data for training."""
# When choosing shuffle buffer sizes, larger sizes result in better
# randomness, while smaller sizes use less memory. MNIST is a small
# enough dataset that we can easily shuffle the full epoch.
ds = dataset.train(flags_obj.data_dir)
ds = ds.cache().shuffle(buffer_size=50000).batch(flags_obj.batch_size)
# Iterate through the dataset a set number (`epochs_between_evals`) of times
# during each training session.
ds = ds.repeat(flags_obj.epochs_between_evals)
return ds
def eval_input_fn():
return dataset.test(flags_obj.data_dir).batch(
flags_obj.batch_size).make_one_shot_iterator().get_next()
# Set up hook that outputs training logs every 100 steps.
train_hooks = hooks_helper.get_train_hooks(
flags_obj.hooks, model_dir=flags_obj.model_dir,
batch_size=flags_obj.batch_size)
# Train and evaluate model.
for _ in range(flags_obj.train_epochs // flags_obj.epochs_between_evals):
mnist_classifier.train(input_fn=train_input_fn, hooks=train_hooks)
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print('\nEvaluation results:\n\t%s\n' % eval_results)
if model_helpers.past_stop_threshold(flags_obj.stop_threshold,
eval_results['accuracy']):
break
# Export the model
if flags_obj.export_dir is not None:
image = tf.compat.v1.placeholder(tf.float32, [None, 28, 28])
input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({
'image': image,
})
mnist_classifier.export_savedmodel(flags_obj.export_dir, input_fn,
strip_default_attrs=True)
def main(_):
run_mnist(flags.FLAGS)
if __name__ == '__main__':
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
define_mnist_flags()
absl_app.run(main)
official/mnist/mnist_eager.py deleted 100644 → 0
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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""MNIST model training with TensorFlow eager execution.
See:
https://research.googleblog.com/2017/10/eager-execution-imperative-define-by.html
This program demonstrates training of the convolutional neural network model
defined in mnist.py with eager execution enabled.
If you are not interested in eager execution, you should ignore this file.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
# pylint: disable=g-bad-import-order
from absl import app as absl_app
from absl import flags
import tensorflow as tf
from tensorflow.python import eager as tfe
# pylint: enable=g-bad-import-order
from official.mnist import dataset as mnist_dataset
from official.mnist import mnist
from official.utils.flags import core as flags_core
from official.utils.misc import model_helpers
def loss(logits, labels):
return tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=labels))
def compute_accuracy(logits, labels):
predictions = tf.argmax(logits, axis=1, output_type=tf.int64)
labels = tf.cast(labels, tf.int64)
batch_size = int(logits.shape[0])
return tf.reduce_sum(
tf.cast(tf.equal(predictions, labels), dtype=tf.float32)) / batch_size
def train(model, optimizer, dataset, step_counter, log_interval=None):
"""Trains model on `dataset` using `optimizer`."""
start = time.time()
for (batch, (images, labels)) in enumerate(dataset):
with tf.contrib.summary.record_summaries_every_n_global_steps(
10, global_step=step_counter):
# Record the operations used to compute the loss given the input,
# so that the gradient of the loss with respect to the variables
# can be computed.
with tf.GradientTape() as tape:
logits = model(images, training=True)
loss_value = loss(logits, labels)
tf.contrib.summary.scalar('loss', loss_value)
tf.contrib.summary.scalar('accuracy', compute_accuracy(logits, labels))
grads = tape.gradient(loss_value, model.variables)
optimizer.apply_gradients(
zip(grads, model.variables), global_step=step_counter)
if log_interval and batch % log_interval == 0:
rate = log_interval / (time.time() - start)
print('Step #%d\tLoss: %.6f (%d steps/sec)' % (batch, loss_value, rate))
start = time.time()
def test(model, dataset):
"""Perform an evaluation of `model` on the examples from `dataset`."""
avg_loss = tf.keras.metrics.Mean('loss', dtype=tf.float32)
accuracy = tf.keras.metrics.Accuracy('accuracy', dtype=tf.float32)
for (images, labels) in dataset:
logits = model(images, training=False)
avg_loss.update_state(loss(logits, labels))
accuracy.update_state(
tf.argmax(logits, axis=1, output_type=tf.int64),
tf.cast(labels, tf.int64))
print('Test set: Average loss: %.4f, Accuracy: %4f%%\n' %
(avg_loss.result(), 100 * accuracy.result()))
with tf.contrib.summary.always_record_summaries():
tf.contrib.summary.scalar('loss', avg_loss.result())
tf.contrib.summary.scalar('accuracy', accuracy.result())
def run_mnist_eager(flags_obj):
"""Run MNIST training and eval loop in eager mode.
Args:
flags_obj: An object containing parsed flag values.
"""
tf.enable_eager_execution()
model_helpers.apply_clean(flags.FLAGS)
# Automatically determine device and data_format
(device, data_format) = ('/gpu:0', 'channels_first')
if flags_obj.no_gpu or not tf.test.is_gpu_available():
(device, data_format) = ('/cpu:0', 'channels_last')
# If data_format is defined in FLAGS, overwrite automatically set value.
if flags_obj.data_format is not None:
data_format = flags_obj.data_format
print('Using device %s, and data format %s.' % (device, data_format))
# Load the datasets
train_ds = mnist_dataset.train(flags_obj.data_dir).shuffle(60000).batch(
flags_obj.batch_size)
test_ds = mnist_dataset.test(flags_obj.data_dir).batch(
flags_obj.batch_size)
# Create the model and optimizer
model = mnist.create_model(data_format)
optimizer = tf.train.MomentumOptimizer(flags_obj.lr, flags_obj.momentum)
# Create file writers for writing TensorBoard summaries.
if flags_obj.output_dir:
# Create directories to which summaries will be written
# tensorboard --logdir=<output_dir>
# can then be used to see the recorded summaries.
train_dir = os.path.join(flags_obj.output_dir, 'train')
test_dir = os.path.join(flags_obj.output_dir, 'eval')
tf.gfile.MakeDirs(flags_obj.output_dir)
else:
train_dir = None
test_dir = None
summary_writer = tf.contrib.summary.create_file_writer(
train_dir, flush_millis=10000)
test_summary_writer = tf.contrib.summary.create_file_writer(
test_dir, flush_millis=10000, name='test')
# Create and restore checkpoint (if one exists on the path)
checkpoint_prefix = os.path.join(flags_obj.model_dir, 'ckpt')
step_counter = tf.train.get_or_create_global_step()
checkpoint = tf.train.Checkpoint(
model=model, optimizer=optimizer, step_counter=step_counter)
# Restore variables on creation if a checkpoint exists.
checkpoint.restore(tf.train.latest_checkpoint(flags_obj.model_dir))
# Train and evaluate for a set number of epochs.
with tf.device(device):
for _ in range(flags_obj.train_epochs):
start = time.time()
with summary_writer.as_default():
train(model, optimizer, train_ds, step_counter,
flags_obj.log_interval)
end = time.time()
print('\nTrain time for epoch #%d (%d total steps): %f' %
(checkpoint.save_counter.numpy() + 1,
step_counter.numpy(),
end - start))
with test_summary_writer.as_default():
test(model, test_ds)
checkpoint.save(checkpoint_prefix)
def define_mnist_eager_flags():
"""Defined flags and defaults for MNIST in eager mode."""
flags_core.define_base(clean=True, train_epochs=True, export_dir=True,
distribution_strategy=True)
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name='log_interval', short_name='li', default=10,
help=flags_core.help_wrap('batches between logging training status'))
flags.DEFINE_string(
name='output_dir', short_name='od', default=None,
help=flags_core.help_wrap('Directory to write TensorBoard summaries'))
flags.DEFINE_float(name='learning_rate', short_name='lr', default=0.01,
help=flags_core.help_wrap('Learning rate.'))
flags.DEFINE_float(name='momentum', short_name='m', default=0.5,
help=flags_core.help_wrap('SGD momentum.'))
flags.DEFINE_bool(name='no_gpu', short_name='nogpu', default=False,
help=flags_core.help_wrap(
'disables GPU usage even if a GPU is available'))
flags_core.set_defaults(
data_dir='/tmp/tensorflow/mnist/input_data',
model_dir='/tmp/tensorflow/mnist/checkpoints/',
batch_size=100,
train_epochs=10,
)
def main(_):
run_mnist_eager(flags.FLAGS)
if __name__ == '__main__':
define_mnist_eager_flags()
absl_app.run(main=main)
official/mnist/mnist_eager_test.py deleted 100644 → 0
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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import tensorflow as tf # pylint: disable=g-bad-import-order
from tensorflow.python import eager as tfe # pylint: disable=g-bad-import-order
from official.mnist import mnist
from official.mnist import mnist_eager
from official.utils.misc import keras_utils
def device():
return '/device:GPU:0' if tfe.context.num_gpus() else '/device:CPU:0'
def data_format():
return 'channels_first' if tfe.context.num_gpus() else 'channels_last'
def random_dataset():
batch_size = 64
images = tf.random_normal([batch_size, 784])
labels = tf.random_uniform([batch_size], minval=0, maxval=10, dtype=tf.int32)
return tf.data.Dataset.from_tensors((images, labels))
def train(defun=False):
model = mnist.create_model(data_format())
if defun:
model.call = tf.function(model.call)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
dataset = random_dataset()
with tf.device(device()):
mnist_eager.train(model, optimizer, dataset,
step_counter=tf.train.get_or_create_global_step())
def evaluate(defun=False):
model = mnist.create_model(data_format())
dataset = random_dataset()
if defun:
model.call = tf.function(model.call)
with tf.device(device()):
mnist_eager.test(model, dataset)
class MNISTTest(tf.test.TestCase):
"""Run tests for MNIST eager loop.
MNIST eager uses contrib and will not work with TF 2.0. All tests are
disabled if using TF 2.0.
"""
def setUp(self):
if not keras_utils.is_v2_0():
tf.compat.v1.enable_v2_behavior()
super(MNISTTest, self).setUp()
@unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
def test_train(self):
train(defun=False)
@unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
def test_evaluate(self):
evaluate(defun=False)
@unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
def test_train_with_defun(self):
train(defun=True)
@unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
def test_evaluate_with_defun(self):
evaluate(defun=True)
if __name__ == '__main__':
tf.test.main()
official/mnist/mnist_test.py deleted 100644 → 0
View file @ 28c5f526
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import unittest
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.mnist import mnist
from official.utils.misc import keras_utils
BATCH_SIZE = 100
def dummy_input_fn():
image = tf.random.uniform([BATCH_SIZE, 784])
labels = tf.random.uniform([BATCH_SIZE, 1], maxval=9, dtype=tf.int32)
return image, labels
def make_estimator():
data_format = 'channels_last'
if tf.test.is_built_with_cuda():
data_format = 'channels_first'
return tf.estimator.Estimator(
model_fn=mnist.model_fn, params={
'data_format': data_format
})
class Tests(tf.test.TestCase):
"""Run tests for MNIST model.
MNIST uses contrib and will not work with TF 2.0. All tests are disabled if
using TF 2.0.
"""
@unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
def test_mnist(self):
classifier = make_estimator()
classifier.train(input_fn=dummy_input_fn, steps=2)
eval_results = classifier.evaluate(input_fn=dummy_input_fn, steps=1)
loss = eval_results['loss']
global_step = eval_results['global_step']
accuracy = eval_results['accuracy']
self.assertEqual(loss.shape, ())
self.assertEqual(2, global_step)
self.assertEqual(accuracy.shape, ())
input_fn = lambda: tf.random.uniform([3, 784])
predictions_generator = classifier.predict(input_fn)
for _ in range(3):
predictions = next(predictions_generator)
self.assertEqual(predictions['probabilities'].shape, (10,))
self.assertEqual(predictions['classes'].shape, ())
@unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
def mnist_model_fn_helper(self, mode, multi_gpu=False):
features, labels = dummy_input_fn()
image_count = features.shape[0]
spec = mnist.model_fn(features, labels, mode, {
'data_format': 'channels_last',
'multi_gpu': multi_gpu
})
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = spec.predictions
self.assertAllEqual(predictions['probabilities'].shape, (image_count, 10))
self.assertEqual(predictions['probabilities'].dtype, tf.float32)
self.assertAllEqual(predictions['classes'].shape, (image_count,))
self.assertEqual(predictions['classes'].dtype, tf.int64)
if mode != tf.estimator.ModeKeys.PREDICT:
loss = spec.loss
self.assertAllEqual(loss.shape, ())
self.assertEqual(loss.dtype, tf.float32)
if mode == tf.estimator.ModeKeys.EVAL:
eval_metric_ops = spec.eval_metric_ops
self.assertAllEqual(eval_metric_ops['accuracy'][0].shape, ())
self.assertAllEqual(eval_metric_ops['accuracy'][1].shape, ())
self.assertEqual(eval_metric_ops['accuracy'][0].dtype, tf.float32)
self.assertEqual(eval_metric_ops['accuracy'][1].dtype, tf.float32)
@unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
def test_mnist_model_fn_train_mode(self):
self.mnist_model_fn_helper(tf.estimator.ModeKeys.TRAIN)
@unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
def test_mnist_model_fn_train_mode_multi_gpu(self):
self.mnist_model_fn_helper(tf.estimator.ModeKeys.TRAIN, multi_gpu=True)
@unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
def test_mnist_model_fn_eval_mode(self):
self.mnist_model_fn_helper(tf.estimator.ModeKeys.EVAL)
@unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
def test_mnist_model_fn_predict_mode(self):
self.mnist_model_fn_helper(tf.estimator.ModeKeys.PREDICT)
class Benchmarks(tf.test.Benchmark):
"""Simple speed benchmarking for MNIST."""
def benchmark_train_step_time(self):
classifier = make_estimator()
# Run one step to warmup any use of the GPU.
classifier.train(input_fn=dummy_input_fn, steps=1)
have_gpu = tf.test.is_gpu_available()
num_steps = 1000 if have_gpu else 100
name = 'train_step_time_%s' % ('gpu' if have_gpu else 'cpu')
start = time.time()
classifier.train(input_fn=dummy_input_fn, steps=num_steps)
end = time.time()
wall_time = (end - start) / num_steps
self.report_benchmark(
iters=num_steps,
wall_time=wall_time,
name=name,
extras={
'examples_per_sec': BATCH_SIZE / wall_time
})
if __name__ == '__main__':
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
tf.test.main()
official/mnist/mnist_tpu.py deleted 100644 → 0
View file @ 28c5f526
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""MNIST model training using TPUs.
This program demonstrates training of the convolutional neural network model
defined in mnist.py on Google Cloud TPUs (https://cloud.google.com/tpu/).
If you are not interested in TPUs, you should ignore this file.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
# pylint: disable=g-bad-import-order
from absl import app as absl_app # pylint: disable=unused-import
import tensorflow as tf
# pylint: enable=g-bad-import-order
# For open source environment, add grandparent directory for import
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(sys.path[0]))))
from official.mnist import dataset # pylint: disable=wrong-import-position
from official.mnist import mnist # pylint: disable=wrong-import-position
# Cloud TPU Cluster Resolver flags
tf.flags.DEFINE_string(
"tpu", default=None,
help="The Cloud TPU to use for training. This should be either the name "
"used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
"url.")
tf.flags.DEFINE_string(
"tpu_zone", default=None,
help="[Optional] GCE zone where the Cloud TPU is located in. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
tf.flags.DEFINE_string(
"gcp_project", default=None,
help="[Optional] Project name for the Cloud TPU-enabled project. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
# Model specific parameters
tf.flags.DEFINE_string("data_dir", "",
"Path to directory containing the MNIST dataset")
tf.flags.DEFINE_string("model_dir", None, "Estimator model_dir")
tf.flags.DEFINE_integer("batch_size", 1024,
"Mini-batch size for the training. Note that this "
"is the global batch size and not the per-shard batch.")
tf.flags.DEFINE_integer("train_steps", 1000, "Total number of training steps.")
tf.flags.DEFINE_integer("eval_steps", 0,
"Total number of evaluation steps. If `0`, evaluation "
"after training is skipped.")
tf.flags.DEFINE_float("learning_rate", 0.05, "Learning rate.")
tf.flags.DEFINE_bool("use_tpu", True, "Use TPUs rather than plain CPUs")
tf.flags.DEFINE_bool("enable_predict", True, "Do some predictions at the end")
tf.flags.DEFINE_integer("iterations", 50,
"Number of iterations per TPU training loop.")
tf.flags.DEFINE_integer("num_shards", 8, "Number of shards (TPU chips).")
FLAGS = tf.flags.FLAGS
def metric_fn(labels, logits):
accuracy = tf.metrics.accuracy(
labels=labels, predictions=tf.argmax(logits, axis=1))
return {"accuracy": accuracy}
def model_fn(features, labels, mode, params):
"""model_fn constructs the ML model used to predict handwritten digits."""
del params
image = features
if isinstance(image, dict):
image = features["image"]
model = mnist.create_model("channels_last")
if mode == tf.estimator.ModeKeys.PREDICT:
logits = model(image, training=False)
predictions = {
'class_ids': tf.argmax(logits, axis=1),
'probabilities': tf.nn.softmax(logits),
}
return tf.contrib.tpu.TPUEstimatorSpec(mode, predictions=predictions)
logits = model(image, training=(mode == tf.estimator.ModeKeys.TRAIN))
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
if mode == tf.estimator.ModeKeys.TRAIN:
learning_rate = tf.train.exponential_decay(
FLAGS.learning_rate,
tf.train.get_global_step(),
decay_steps=100000,
decay_rate=0.96)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
if FLAGS.use_tpu:
optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)
return tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=loss,
train_op=optimizer.minimize(loss, tf.train.get_global_step()))
if mode == tf.estimator.ModeKeys.EVAL:
return tf.contrib.tpu.TPUEstimatorSpec(
mode=mode, loss=loss, eval_metrics=(metric_fn, [labels, logits]))
def train_input_fn(params):
"""train_input_fn defines the input pipeline used for training."""
batch_size = params["batch_size"]
data_dir = params["data_dir"]
# Retrieves the batch size for the current shard. The # of shards is
# computed according to the input pipeline deployment. See
# `tf.contrib.tpu.RunConfig` for details.
ds = dataset.train(data_dir).cache().repeat().shuffle(
buffer_size=50000).batch(batch_size, drop_remainder=True)
return ds
def eval_input_fn(params):
batch_size = params["batch_size"]
data_dir = params["data_dir"]
ds = dataset.test(data_dir).batch(batch_size, drop_remainder=True)
return ds
def predict_input_fn(params):
batch_size = params["batch_size"]
data_dir = params["data_dir"]
# Take out top 10 samples from test data to make the predictions.
ds = dataset.test(data_dir).take(10).batch(batch_size)
return ds
def main(argv):
del argv # Unused.
tf.logging.set_verbosity(tf.logging.INFO)
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu,
zone=FLAGS.tpu_zone,
project=FLAGS.gcp_project
)
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=FLAGS.model_dir,
session_config=tf.ConfigProto(
allow_soft_placement=True, log_device_placement=True),
tpu_config=tf.contrib.tpu.TPUConfig(FLAGS.iterations, FLAGS.num_shards),
)
estimator = tf.contrib.tpu.TPUEstimator(
model_fn=model_fn,
use_tpu=FLAGS.use_tpu,
train_batch_size=FLAGS.batch_size,
eval_batch_size=FLAGS.batch_size,
predict_batch_size=FLAGS.batch_size,
params={"data_dir": FLAGS.data_dir},
config=run_config)
# TPUEstimator.train *requires* a max_steps argument.
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.train_steps)
# TPUEstimator.evaluate *requires* a steps argument.
# Note that the number of examples used during evaluation is
# --eval_steps * --batch_size.
# So if you change --batch_size then change --eval_steps too.
if FLAGS.eval_steps:
estimator.evaluate(input_fn=eval_input_fn, steps=FLAGS.eval_steps)
# Run prediction on top few samples of test data.
if FLAGS.enable_predict:
predictions = estimator.predict(input_fn=predict_input_fn)
for pred_dict in predictions:
template = ('Prediction is "{}" ({:.1f}%).')
class_id = pred_dict['class_ids']
probability = pred_dict['probabilities'][class_id]
print(template.format(class_id, 100 * probability))
if __name__ == "__main__":
absl_app.run(main)
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