mnist.py文件

# coding=utf-8
'''
Created on Feb 11, 2019

@author: zhongzhu
'''
# 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.
# ==============================================================================

"""Builds the MNIST network.

Implements the inference/loss/training pattern for model building.

1. inference() - Builds the model as far as required for running the network
forward to make predictions.
2. loss() - Adds to the inference model the layers required to generate loss.
3. training() - Adds to the loss model the Ops required to generate and
apply gradients.

This file is used by the various "fully_connected_*.py" files and not meant to
be run.
"""

import math

import tensorflow as tf

# The MNIST dataset has 10 classes, representing the digits 0 through 9.
NUM_CLASSES = 10

# The MNIST images are always 28x28 pixels.
IMAGE_SIZE = 28
IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE


def inference(images, hidden1_units, hidden2_units):
  """Build the MNIST model up to where it may be used for inference.

  Args:
    images: Images placeholder, from inputs().
    hidden1_units: Size of the first hidden layer.
    hidden2_units: Size of the second hidden layer.

  Returns:
    softmax_linear: Output tensor with the computed logits.
  """
  # Hidden 1
  with tf.name_scope('hidden1'):
    weights = tf.Variable(
        tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
                            stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),
        name='weights')
    biases = tf.Variable(tf.zeros([hidden1_units]),
                         name='biases')
    hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
  # Hidden 2
  with tf.name_scope('hidden2'):
    weights = tf.Variable(
        tf.truncated_normal([hidden1_units, hidden2_units],
                            stddev=1.0 / math.sqrt(float(hidden1_units))),
        name='weights')
    biases = tf.Variable(tf.zeros([hidden2_units]),
                         name='biases')
    hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
  # Linear
  with tf.name_scope('softmax_linear'):
    weights = tf.Variable(
        tf.truncated_normal([hidden2_units, NUM_CLASSES],
                            stddev=1.0 / math.sqrt(float(hidden2_units))),
        name='weights')
    biases = tf.Variable(tf.zeros([NUM_CLASSES]),
                         name='biases')
    logits = tf.matmul(hidden2, weights) + biases
  return logits


def loss(logits, labels):
  """Calculates the loss from the logits and the labels.

  Args:
    logits: Logits tensor, float - [batch_size, NUM_CLASSES].
    labels: Labels tensor, int32 - [batch_size].

  Returns:
    loss: Loss tensor of type float.
  """
  labels = tf.to_int64(labels)
  return tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)


def training(loss, learning_rate):
  """Sets up the training Ops.

  Creates a summarizer to track the loss over time in TensorBoard.

  Creates an optimizer and applies the gradients to all trainable variables.

  The Op returned by this function is what must be passed to the
  `sess.run()` call to cause the model to train.

  Args:
    loss: Loss tensor, from loss().
    learning_rate: The learning rate to use for gradient descent.

  Returns:
    train_op: The Op for training.
  """
  
  with tf.name_scope('scalar_summaries'):
      # Add a scalar summary for the snapshot loss.
      tf.summary.scalar('loss', loss)
      tf.summary.scalar('learning_rate', learning_rate)

  # Create the gradient descent optimizer with the given learning rate.
  optimizer = tf.train.GradientDescentOptimizer(learning_rate)
  # Create a variable to track the global step.
  global_step = tf.Variable(0, name='global_step', trainable=False)
  # Use the optimizer to apply the gradients that minimize the loss
  # (and also increment the global step counter) as a single training step.
 
  train_op = optimizer.minimize(loss, global_step=global_step)
  return train_op


def evaluation(logits, labels):
  """Evaluate the quality of the logits at predicting the label.

  Args:
    logits: Logits tensor, float - [batch_size, NUM_CLASSES].
    labels: Labels tensor, int32 - [batch_size], with values in the
      range [0, NUM_CLASSES).

  Returns:
    A scalar int32 tensor with the number of examples (out of batch_size)
    that were predicted correctly.
  """
  # For a classifier model, we can use the in_top_k Op.
  # It returns a bool tensor with shape [batch_size] that is true for
  # the examples where the label is in the top k (here k=1)
  # of all logits for that example.
  correct = tf.nn.in_top_k(logits, labels, 1)
  # Return the number of true entries.
  return tf.reduce_sum(tf.cast(correct, tf.int32))

fully_connected_toturol.py文件

# coding=utf-8
'''
Created on Feb 11, 2019

@author: zhongzhu
'''
import argparse
import os.path
import sys
import time
import os
from six.moves import xrange
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data
# from tensorflow.examples.tutorials.mnist import mnist
import mnist
#過濾警告信息
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# 全局變量恒削，用來存放基本的模型(超)參數(shù).
FLAGS = None


#產(chǎn)生 placeholder variables來表達(dá)輸入張量
def placeholder_inputs(batch_size):
    images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, mnist.IMAGE_PIXELS))
    labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))
    return images_placeholder, labels_placeholder

def fill_feed_dict(data_set, images_pl, labels_pl):
    images_feed, labels_feed = data_set.next_batch(FLAGS.batch_size, FLAGS.fake_data)
    feed_dict = {images_pl: images_feed, labels_pl: labels_feed}
    return feed_dict

#在給定的數(shù)據(jù)集上執(zhí)行一次評(píng)估操作
def do_eval(sess, eval_correct, images_placeholder, labels_placeholder, data_set):
    true_count = 0
    steps_per_epoch = data_set.num_examples // FLAGS.batch_size
    num_examples = steps_per_epoch * FLAGS.batch_size
    
    for step in xrange(steps_per_epoch):
        feed_dict = fill_feed_dict(data_set, images_placeholder, labels_placeholder)
        true_count += sess.run(eval_correct, feed_dict = feed_dict)
    
    precision = float(true_count) / num_examples
    print('  Num examples: %d  Num correct: %d  Precision @ 1: %0.04f' %
        (num_examples, true_count, precision))


# 啟動(dòng)訓(xùn)練過程
def run_training():
    data_sets = input_data.read_data_sets(FLAGS.input_data_dir, FLAGS.fake_data)
    
    # 告訴Tensorflow模型將會(huì)被構(gòu)建在默認(rèn)的Graph上
    with tf.Graph().as_default():
        images_placeholder, labels_placeholder = placeholder_inputs(FLAGS.batch_size)
        #從前向推斷模型中構(gòu)建用于預(yù)測的計(jì)算圖
        logits = mnist.inference(images_placeholder, FLAGS.hidden1, FLAGS.hidden2)
        #為計(jì)算圖添加計(jì)算損失的節(jié)點(diǎn)
        loss = mnist.loss(logits, labels_placeholder)
        #為計(jì)算圖添加計(jì)算和應(yīng)用梯度的節(jié)點(diǎn)
        train_op = mnist.training(loss, FLAGS.learning_rate)
        # 添加評(píng)估節(jié)點(diǎn)
        eval_correct = mnist.evaluation(logits, labels_placeholder)
        
        init = tf.global_variables_initializer()
        
        merged_summaries = tf.summary.merge_all()
        
        
        saver = tf.train.Saver()
        # 創(chuàng)建一個(gè)會(huì)話用來運(yùn)行計(jì)算圖中的節(jié)點(diǎn)
        sess = tf.Session()
        
         # 實(shí)例化一個(gè) SummaryWriter 輸出 summaries 和 Graph.
        summary_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)
        summary_writer.flush()
        
        sess.run(init)
        
        for step in xrange(FLAGS.max_steps):
            start_time = time.time()
            feed_dict = fill_feed_dict(data_sets.train, images_placeholder, labels_placeholder)
            _, loss_value = sess.run([train_op, loss],feed_dict = feed_dict)
            duration = time.time() - start_time
            
            if step % 100 == 0:
                print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration))
                summary_str = sess.run(merged_summaries,feed_dict=feed_dict)
                summary_writer.add_summary(summary_str, step)
                summary_writer.flush()
            
            
            if (step + 1) % 1000 == 0  or (step + 1) == FLAGS.max_steps:
                
                checkpoint_file = os.path.join(FLAGS.log_dir, 'model.ckpt')
                saver.save(sess, checkpoint_file, global_step=step)
                print('Training data eval')
                do_eval(sess, eval_correct, images_placeholder, labels_placeholder, data_sets.train)
                print('Validation data eval')
                do_eval(sess, eval_correct, images_placeholder, labels_placeholder, data_sets.validation)
                print('Test data eval')
                do_eval(sess, eval_correct, images_placeholder, labels_placeholder, data_sets.test)

#創(chuàng)建日志文件夾,啟動(dòng)訓(xùn)練過程
def main(_):
#     if tf.gfile.Exists(FLAGS.log_dir):
#         tf.gfile.DeleteRecursively(FLAGS.log_dir)
#     tf.gfile.MakeDirs(FLAGS.log_dir)
    #啟動(dòng)訓(xùn)練過程
    run_training()

#用ArgumentParser類把模型的(超)參數(shù)全部解析到全局變量FLAGS里面
if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
      '--learning_rate',
      type=float,
      default=0.01,
      help='Initial learning rate.'
      )
    parser.add_argument(
      '--max_steps',
      type=int,
      default=2000,
      help='Number of steps to run trainer.'
      )
    parser.add_argument(
      '--hidden1',
      type=int,
      default=128,
      help='Number of units in hidden layer 1.'
      )
    parser.add_argument(
      '--hidden2',
      type=int,
      default=32,
      help='Number of units in hidden layer 2.'
      )
    parser.add_argument(
      '--batch_size',
      type=int,
      default=100,
      help='Batch size.  Must divide evenly into the dataset sizes.'
      )
    parser.add_argument(
      '--input_data_dir',
      type=str,
      default='MNIST_data/',
      help='Directory to put the input data.'
      )
    parser.add_argument(
      '--log_dir',
      type=str,
      default='logs/Fully_Connected_Feed',
      help='Directory to put the log data.'
      )
    parser.add_argument(
      '--fake_data',
      default=False,
      help='If true, uses fake data for unit testing.',
      action='store_true'
      )
  #把模型的(超)參數(shù)全部解析到全局變量FLAGS里面
    FLAGS, unparsed = parser.parse_known_args()
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

程序運(yùn)行結(jié)果池颈，

Scalar標(biāo)量圖尾序，

image.png

hidden1/weights 圖，

image.png