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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.
# ==============================================================================
"""MNIST training in imperative mode TensorFlow."""
# pylint: disable=redefined-outer-name
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.contrib.imperative as tf
from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
IMAGE_SIZE = 28
IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE
NUM_CLASSES = 10
BATCH_SIZE = 100
NUM_EPOCHS = 2
LEARNING_RATE = 0.1
class Model(object):
"""Fully connected model for MNIST."""
def __init__(self, hidden1_units, hidden2_units):
"""Create the model parameters."""
self.params = []
# Hidden 1
with tf.name_scope('hidden1'):
self.weights1 = tf.Variable(
np.random.normal(scale=1.0 / np.sqrt(float(IMAGE_PIXELS)),
size=[IMAGE_PIXELS, hidden1_units]),
dtype=tf.float32,
name='weights')
self.biases1 = tf.Variable(
np.zeros([hidden1_units]),
dtype=tf.float32,
name='biases')
# Hidden 2
with tf.name_scope('hidden2'):
self.weights2 = tf.Variable(
np.random.normal(scale=1.0 / np.sqrt(float(hidden1_units)),
size=[hidden1_units, hidden2_units]),
dtype=tf.float32,
name='weights')
self.biases2 = tf.Variable(
np.zeros([hidden2_units]),
dtype=tf.float32,
name='biases')
# Linear
with tf.name_scope('softmax_linear'):
self.sm_w = tf.Variable(
np.random.normal(scale=1.0 / np.sqrt(float(hidden2_units)),
size=[hidden2_units, NUM_CLASSES]),
dtype=tf.float32,
name='weights')
self.sm_b = tf.Variable(
np.zeros([NUM_CLASSES]),
dtype=tf.float32,
name='biases')
self.params = [self.weights1, self.biases1,
self.weights2, self.biases2,
self.sm_w, self.sm_b]
def __call__(self, images):
"""Run the model's forward prop on `images`."""
hidden1 = tf.nn.relu(tf.matmul(images, self.weights1) + self.biases1)
hidden2 = tf.nn.relu(tf.matmul(hidden1, self.weights2) + self.biases2)
logits = tf.matmul(hidden2, self.sm_w) + self.sm_b
return logits
model = Model(128, 32)
data = read_data_sets('/tmp/mnist_train')
def get_test_accuracy():
"""Gets the model's classification accuracy on test data."""
num_examples = data.test.num_examples
test_images = np.split(data.test.images, num_examples/BATCH_SIZE)
test_labels = np.split(data.test.labels.astype(np.int32),
num_examples/BATCH_SIZE)
num_correct = 0
for _, (images, labels) in enumerate(zip(test_images, test_labels)):
with tf.new_step():
logits = model(images)
predictions = tf.argmax(tf.nn.softmax(logits), axis=1)
num_correct += np.sum(predictions.value == labels)
return float(num_correct) / float(num_examples)
num_examples = data.train.num_examples
train_images = np.split(data.train.images, num_examples/BATCH_SIZE)
train_labels = np.split(data.train.labels.astype(np.int32),
num_examples/BATCH_SIZE)
for epoch in range(NUM_EPOCHS):
for i, (images, labels) in enumerate(zip(train_images, train_labels)):
with tf.new_step() as step:
logits = model(images)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels, logits=logits, name='xentropy')
loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
gradients = tf.gradients(loss, model.params)
step.run([v.assign_sub(LEARNING_RATE * g)
for g, v in zip(gradients, model.params)])
if i % 10 == 0:
print('Loss after {} steps = {}'.format(i, loss))
if i % 100 == 0:
print('Test accuracy after {} steps = {}'
.format(i, get_test_accuracy()))
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