# SPDX-License-Identifier: Apache-2.0

# Copyright 2015 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.

# ==============================================================================

"""A deep MNIST classifier using convolutional layers.

See extensive documentation at

https://www.tensorflow.org/get_started/mnist/pros

"""

# Disable linter warnings to maintain consistency with tutorial.

# pylint: disable=invalid-name

# pylint: disable=g-bad-import-order

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import argparse

import sys

import os

import shutil

import tempfile

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

def add(x, y):

return tf.nn.bias_add(x, y, data_format="NHWC")

def deepnn(x):

"""deepnn builds the graph for a deep net for classifying digits.

Args:

x: an input tensor with the dimensions (N_examples, 784), where 784 is the

number of pixels in a standard MNIST image.

Returns:

A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values

equal to the logits of classifying the digit into one of 10 classes (the

digits 0-9). keep_prob is a scalar placeholder for the probability of dropout.

"""

# Reshape to use within a convolutional neural net.

# Last dimension is for "features" - there is only one here, since images are

# grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.

with tf.name_scope('reshape'):

x_image = tf.reshape(x, [-1, 1, 28, 28])

x_image = tf.transpose(x_image, [0, 2, 3, 1])

# First convolutional layer - maps one grayscale image to 32 feature maps.

with tf.name_scope('conv1'):

w_conv1 = weight_variable([5, 5, 1, 32])

b_conv1 = bias_variable([32])

h_conv1 = tf.nn.relu(add(conv2d(x_image, w_conv1), b_conv1))

# Pooling layer - downsamples by 2X.

with tf.name_scope('pool1'):

h_pool1 = max_pool_2x2(h_conv1)

# Second convolutional layer -- maps 32 feature maps to 64.

with tf.name_scope('conv2'):

w_conv2 = weight_variable([5, 5, 32, 64])

b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(add(conv2d(h_pool1, w_conv2), b_conv2))

# Second pooling layer.

with tf.name_scope('pool2'):

h_pool2 = max_pool_2x2(h_conv2)

# Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image

# is down to 7x7x64 feature maps -- maps this to 1024 features.

with tf.name_scope('fc1'):

w_fc1 = weight_variable([7 * 7 * 64, 1024])

b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])

h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)

# Map the 1024 features to 10 classes, one for each digit

with tf.name_scope('fc2'):

w_fc2 = weight_variable([1024, 10])

b_fc2 = bias_variable([10])

y_conv = tf.matmul(h_fc1, w_fc2) + b_fc2

return y_conv

def conv2d(x, w):

"""conv2d returns a 2d convolution layer with full stride."""

return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME', data_format="NHWC")

def max_pool_2x2(x):

"""max_pool_2x2 downsamples a feature map by 2X."""

return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],

strides=[1, 2, 2, 1], padding='SAME', data_format="NHWC")

def weight_variable(shape):

"""weight_variable generates a weight variable of a given shape."""

initial = tf.truncated_normal(shape, stddev=0.1)

return tf.Variable(initial)

def bias_variable(shape):

"""bias_variable generates a bias variable of a given shape."""

initial = tf.constant(0.1, shape=shape)

return tf.Variable(initial)

def create_and_train_mnist():

tf.logging.set_verbosity(tf.logging.ERROR)

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

# Import data

data_dir = r"/tmp/tensorflow/mnist/input_data"

mnist = input_data.read_data_sets(data_dir)

# Create the model

tf.reset_default_graph()

input_tensor = tf.placeholder(tf.float32, [None, 784], name="input")

# Build the graph for the deep net

y_conv = deepnn(input_tensor)

output_tensor = tf.identity(y_conv, "result")

with open("./output/graph.proto", "wb") as file:

graph = tf.get_default_graph().as_graph_def(add_shapes=True)

file.write(graph.SerializeToString())

# Define loss and optimizer

y_ = tf.placeholder(tf.int64, [None])

with tf.name_scope('loss'):

cross_entropy = tf.losses.sparse_softmax_cross_entropy(

labels=y_, logits=y_conv)

cross_entropy = tf.reduce_mean(cross_entropy)

with tf.name_scope('adam_optimizer'):

train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

with tf.name_scope('accuracy'):

correct_prediction = tf.equal(tf.argmax(y_conv, 1), y_)

correct_prediction = tf.cast(correct_prediction, tf.float32)

accuracy = tf.reduce_mean(correct_prediction)

saver = tf.train.Saver()

config = tf.ConfigProto()

config.gpu_options.allow_growth = True

sess = tf.Session(config=config)

sess.run(tf.global_variables_initializer())

for i in range(5000):

batch = mnist.train.next_batch(50)

if i % 1000 == 0:

train_accuracy = accuracy.eval(session=sess, feed_dict={input_tensor: batch[0], y_: batch[1]})

print('step %d, training accuracy %g' % (i, train_accuracy))

train_step.run(session=sess, feed_dict={input_tensor: batch[0], y_: batch[1]})

print('test accuracy %g' % accuracy.eval(session=sess, feed_dict={input_tensor: mnist.test.images[:1000], y_: mnist.test.labels[:1000]}))

return sess, saver, input_tensor, output_tensor

def save_model_to_checkpoint(saver, sess):

print("save model to checkpoint")

save_path = saver.save(sess, "./output/ckpt/model.ckpt")

def save_model_to_frozen_proto(sess):

print('save model to frozen graph')

frozen_graph = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ["result"])

with open("./output/mnist_frozen.pb", "wb") as file:

file.write(frozen_graph.SerializeToString())

def save_model_to_saved_model(sess, input_tensor, output_tensor):

print('save model to saved_model')

from tensorflow.saved_model import simple_save

save_path = r"./output/saved_model"

if os.path.exists(save_path):

shutil.rmtree(save_path)

simple_save(sess, save_path, {input_tensor.name: input_tensor}, {output_tensor.name: output_tensor})