採用全連線網路（Full-Connected Networks）完成MNIST資料的手寫體識別

Tensor即張量的理解

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fully_connected_feed.py 解析

# 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.
# ==============================================================================

"""Trains and Evaluates the MNIST network using a feed dictionary."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# pylint: disable=missing-docstring
import argparse
import os
import sys
import time

from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.examples.tutorials.mnist import mnist

# Basic model parameters as external flags.
FLAGS = None


def placeholder_inputs(batch_size):
  """Generate placeholder variables to represent the input tensors.

  These placeholders are used as inputs by the rest of the model building
  code and will be fed from the downloaded data in the .run() loop, below.

  Args:
    batch_size: The batch size will be baked into both placeholders.

  Returns:
    images_placeholder: Images placeholder.
    labels_placeholder: Labels placeholder.
  """
  # Note that the shapes of the placeholders match the shapes of the full
  # image and label tensors, except the first dimension is now batch_size
  # rather than the full size of the train or test data sets.
  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):
  """Fills the feed_dict for training the given step.

  A feed_dict takes the form of:
  feed_dict = {
      <placeholder>: <tensor of values to be passed for placeholder>,
      ....
  }

  Args:
    data_set: The set of images and labels, from input_data.read_data_sets()
    images_pl: The images placeholder, from placeholder_inputs().
    labels_pl: The labels placeholder, from placeholder_inputs().

  Returns:
    feed_dict: The feed dictionary mapping from placeholders to values.
  """
  # Create the feed_dict for the placeholders filled with the next
  # `batch size` examples.
  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


def do_eval(sess,
            eval_correct,
            images_placeholder,
            labels_placeholder,
            data_set):
  """Runs one evaluation against the full epoch of data.

  Args:
    sess: The session in which the model has been trained.
    eval_correct: The Tensor that returns the number of correct predictions.
    images_placeholder: The images placeholder.
    labels_placeholder: The labels placeholder.
    data_set: The set of images and labels to evaluate, from
      input_data.read_data_sets().
  """
  # And run one epoch of eval.
  true_count = 0  # Counts the number of correct predictions.
  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))


def run_training():
  """Train MNIST for a number of steps."""
  # Get the sets of images and labels for training, validation, and
  # test on MNIST.
  data_sets = input_data.read_data_sets(FLAGS.input_data_dir, FLAGS.fake_data)

  # Tell TensorFlow that the model will be built into the default Graph.
  with tf.Graph().as_default():
    # Generate placeholders for the images and labels.
    images_placeholder, labels_placeholder = placeholder_inputs(
        FLAGS.batch_size)

    # Build a Graph that computes predictions from the inference model.
    logits = mnist.inference(images_placeholder,
                             FLAGS.hidden1,
                             FLAGS.hidden2)

    # Add to the Graph the Ops for loss calculation.
    loss = mnist.loss(logits, labels_placeholder)

    # Add to the Graph the Ops that calculate and apply gradients.
    train_op = mnist.training(loss, FLAGS.learning_rate)

    # Add the Op to compare the logits to the labels during evaluation.
    eval_correct = mnist.evaluation(logits, labels_placeholder)

    # Build the summary Tensor based on the TF collection of Summaries.
    summary = tf.summary.merge_all()

    # Add the variable initializer Op.
    init = tf.global_variables_initializer()

    # Create a saver for writing training checkpoints.
    saver = tf.train.Saver()

    # Create a session for running Ops on the Graph.
    sess = tf.Session()

    # Instantiate a SummaryWriter to output summaries and the Graph.
    summary_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)

    # And then after everything is built:

    # Run the Op to initialize the variables.
    sess.run(init)

    # Start the training loop.
    for step in xrange(FLAGS.max_steps):
      start_time = time.time()

      # Fill a feed dictionary with the actual set of images and labels
      # for this particular training step.
      feed_dict = fill_feed_dict(data_sets.train,
                                 images_placeholder,
                                 labels_placeholder)

      # Run one step of the model.  The return values are the activations
      # from the `train_op` (which is discarded) and the `loss` Op.  To
      # inspect the values of your Ops or variables, you may include them
      # in the list passed to sess.run() and the value tensors will be
      # returned in the tuple from the call.
      _, loss_value = sess.run([train_op, loss],
                               feed_dict=feed_dict)

      duration = time.time() - start_time

      # Write the summaries and print an overview fairly often.
      if step % 100 == 0:
        # Print status to stdout.
        print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration))
        # Update the events file.
        summary_str = sess.run(summary, feed_dict=feed_dict)
        summary_writer.add_summary(summary_str, step)
        summary_writer.flush()

      # Save a checkpoint and evaluate the model periodically.
      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)
        # Evaluate against the training set.
        print('Training Data Eval:')
        do_eval(sess,
                eval_correct,
                images_placeholder,
                labels_placeholder,
                data_sets.train)
        # Evaluate against the validation set.
        print('Validation Data Eval:')
        do_eval(sess,
                eval_correct,
                images_placeholder,
                labels_placeholder,
                data_sets.validation)
        # Evaluate against the test set.
        print('Test Data Eval:')
        do_eval(sess,
                eval_correct,
                images_placeholder,
                labels_placeholder,
                data_sets.test)


def main(_):
  if tf.gfile.Exists(FLAGS.log_dir):
    tf.gfile.DeleteRecursively(FLAGS.log_dir)
  tf.gfile.MakeDirs(FLAGS.log_dir)
  run_training()


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=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
                           'tensorflow/mnist/input_data'),
      help='Directory to put the input data.'
  )
  parser.add_argument(
      '--log_dir',
      type=str,
      default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
                           'tensorflow/mnist/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'
  )

  FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

以下為部分程式碼解析

120 data_sets = input_data.read_data_sets(FLAGS.input_data_dir, FLAGS.fake_ data) 

120 行，準備訓練、驗證和測試資料集。這裡 TensorFlow 提供了內建模組可以直接操作下載 MNIST datasets 資料集。

 123 with tf.Graph().as_default(): 

123 行，使用預設圖（ graph), TensorFlow 裡使用圖來表示計算任務，圖中的節點被稱 為 Op (operation），一個 Op 獲取 0 個或多個 tensor 執行計算，併產生 0 個或多個 tensor。

225 if __name__ =='__main__ ':
226 parser = argparse.ArgumentParser() 
.......
276 FLAGS, unparsed = parser.parse_known_ args() 
277 tf.app.run(main=main, argv=[sys.argv[O]] +unparsed) 

225 ～ 277 行，解析命令列啟動 TensorFlow。

218 def main(_) : 
219 if tf.gfile.Exists(FLAGS.log_dir): 
220 tf.gfile.DeleteRecursively(FLAGS.log_dir) 
221 tf.gfile.MakeDirs (FLAGS.log_dir) 
222 run_training () 

218 ～ 222 行，啟動 TensorFlow 後首先呼叫 main 函式，判斷目錄是否存在，存在就刪 除不存在就建立。 最後開始訓練 MNIST 資料。

 125 images_placeholder, labels_placeholder = placeholder_inputs( 
 126 FLAGS.batch_size) 

125 ～ 126 行，建立圖片和其對應的標籤佔位符，後面真正使用時會進行資料填充，這裡預先告知資料的形狀和型別

129 logits = mnist.inference(images_placeholder, 
130 FLAGS.hidden1, 
131 FLAGS.hidden2) 
132
133 # Add to the Graph the Ops for loss calculation. 
134 loss = mnist.1oss(logits, labels_placeholder)  
135  
136 # Add to the Graph the Ops that calculate and apply gradients. 
137 train_op = mnist.training(loss, FLAGS.learning_rate) 
138 
139 # Add the Op to compare the logits to the labels during evaluation. 
140 eval_correct = mnist.evaluation(logits, labels_placeholder) 

129 ～ 140 行，建立網路 Op, loss Op, gradients Op, evaluation Op

142 # Build the summary Tensor based on the TF collection of Summaries. 
143 summary= tf.summary.merge_all() 

143 行，合併所有的 summary Op 為一個 Op
TensorFlow 裡所有出現 summary程式碼的地方都是在建立 summary Op，用來儲存訓練過程中你想要記錄的資料。 比如：

tf.summary.histogram('histogram’, var)
tf.summary.scalar(' loss', loss) 

如果你需要記錄的資料很多，就會建立很多 summary Op，這時候使用 tf.summary. merge_all 來合併所有的 summary Op，就會方便很多。 在訓練過程中使用 summary FileWriter 把這些資料寫入磁碟。 在訓練完畢後你就可以啟動 Tensorboard:

tensorboard---logdir=path/to/logs

然後在瀏覽器中開啟 Web 介面 http://localhost:6006 來檢視訓練中的各種指標資料。 在訓練過程中的變化情況。 這裡的 logdir 就是 summary File Writer 引數裡填寫的路徑

146 init= tf.global_variables_initializer() 

146 行，建立初始化變數 Op

148 # Create a saver for writing training checkpoints. 
149 saver = tf.train.Saver() 

149 行，建立 saver 來儲存模型。

152 sess = tf.Session () 

152 行，建立會話（session）上下文，圖需要在會話中執行。

 154 # Instantiate a SummaryWriter to output summaries and the Graph.
 155 summary_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)

155 行，建立 summaryFileWriter，把 summary Op 返回的資料寫到磁碟。

159 # Run the Op to initialize the variables. 
160 sess.run(init) 

160 行，執行初始化所有變數，之前建立的 Op 只是描述了資料是怎樣流動或者怎麼計 算，沒有真正開始執行運算，只有把 Op 放入 sess.run(Op）中才會開始執行

162 # Start the training loop. 
163 for step in xrange(FLAGS.max_steps): 

163 行，開始訓練迴圈總共執行 FLAGS.max_steps 個 step

164 start_time = time.time()  

164 行，記錄每個 step 的開始時間

166 # Fill a feed dictionary with the actual set of images and labels 
167 # for this particular training step. 168 feed_dict = fill_feed_dict(data_sets. train, 
169 images_placeholder, 
170 labels_placeholder) 

168 ～ 170 行，取一個 batch 訓練資料，使用真實資料填充圖片和標籤佔位符

177 _, loss_value = sess.run([train_op, loss], 
178 feed dict=feed dict)  

177 ～ 178 行，把一個 batch 資料放入模型進行訓練，得到 train_op（被忽略掉了）和 loass op 的返回值，如果你想觀察 Op 或者變數的值，需要把它們放到列表裡傳給 sess. run（），然後它們的值會以元組的形式返回

180 duration= time.time() - start_time  

180 行，計算執行一個 step 花費的時間。

183 if step % 100 == 0: 
184 # Print status to stdout. 
185 print(' Step%d:loss = %.2f (%.3f sec)'%(step,loss_ value,duration)) 
186 # Update the events file. 
187 summary_str = sess.run(summary, feed_dict=feed_dict) 
188 summary_writer.add_summary(summary_str, step) 
189 summary_writer.flush() 

183 ～ 189 行，每 100 個 step 把 summary資訊寫入磁碟一次

191   # Save a checkpoint and evaluate the model periodically.
192      if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
193        checkpoint_file = os.path.join(FLAGS.log_dir, 'model.ckpt')
194        saver.save(sess, checkpoint_file, global_step=step)
195        # Evaluate against the training set.
196        print('Training Data Eval:')
197        do_eval(sess,
198                eval_correct,
199                images_placeholder,
200                labels_placeholder,
201                data_sets.train)
202        # Evaluate against the validation set.
203        print('Validation Data Eval:')
204        do_eval(sess,
205                eval_correct,
206                images_placeholder,
207                labels_placeholder,
208                data_sets.validation)
209        # Evaluate against the test set.
210        print('Test Data Eval:')
211        do_eval(sess,
212                eval_correct,
213                images_placeholder,
214                labels_placeholder,
215                data_sets.test)

192 ～ 215 行，每 1000 個 step 或者是最後一個 step 儲存一下模型，並且列印訓練過程 中產生的模型在訓練、驗證、測試資料集上的準確率。

MNIST.py 解析

# 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.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

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.
  """
  # Add a scalar summary for the snapshot loss.
  tf.summary.scalar('loss', loss)
  # 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))

以下為部分程式碼解析

38 NUM_CLASSES = 10
39 
40 # The MNIST images are always 28x28 pixels.
41 IMAGE_SIZE = 28
42 IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE

38 行， MNIST dataset 總共 10 類， 0 ～ 9 手寫數字圖片。
41 ～ 42 行， MNIST dataset 每張圖片畫素 28 × 28

45 def inference(images, hidden1_units, hidden2_units):
46  """Build the MNIST model up to where it may be used for inference.
47
48  Args:
49    images: Images placeholder, from inputs().
50    hidden1_units: Size of the first hidden layer.
51    hidden2_units: Size of the second hidden layer.
52
53  Returns:
54    softmax_linear: Output tensor with the computed logits.
55   """
56  # Hidden 1
57  with tf.name_scope('hidden1'):
58    weights = tf.Variable(
59        tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
60                            stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),
61        name='weights')
62    biases = tf.Variable(tf.zeros([hidden1_units]),
63                         name='biases')
64    hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
65  # Hidden 2
66  with tf.name_scope('hidden2'):
67    weights = tf.Variable(
68        tf.truncated_normal([hidden1_units, hidden2_units],
69                            stddev=1.0 / math.sqrt(float(hidden1_units))),
70        name='weights')
71    biases = tf.Variable(tf.zeros([hidden2_units]),
72                         name='biases')
73    hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
74  # Linear
75  with tf.name_scope('softmax_linear'):
76    weights = tf.Variable(
77        tf.truncated_normal([hidden2_units, NUM_CLASSES],
78                          stddev=1.0/math.sqrt(float(hidden2_units))),
79        name='weights')
80    biases = tf.Variable(tf.zeros([NUM_CLASSES]),
81                         name='biases')
82    logits = tf.matmul(hidden2, weights) + biases
83  return logits

45 ～ 83 行，構建網路

56  # Hidden 1
57  with tf.name_scope('hidden1'):
58    weights = tf.Variable(
59        tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
60                            stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),
61        name='weights')
62    biases = tf.Variable(tf.zeros([hidden1_units]),
63                         name='biases')
64    hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)

57 ～ 64 行，第一層隱藏層
57 行，設定名稱空間，不同的名稱空間中變數名不衝
58 ～ 61 行，建立變數和權重，使用截斷正態分佈進行初始化。
62 ～ 63 行，建立變數和偏置，全部初始化為 0。 64 行 （ax+b） 過激勵函式 relu。 這裡 w 就是權重， b 就是偏向， a 在第一層就是輸入圖片， 不在第一層就是上一層的輸出。

65  # Hidden 2
66  with tf.name_scope('hidden2'):
67    weights = tf.Variable(
68        tf.truncated_normal([hidden1_units, hidden2_units],
69                            stddev=1.0 / math.sqrt(float(hidden1_units))),
70        name='weights')
71    biases = tf.Variable(tf.zeros([hidden2_units]),
72                         name='biases')
73    hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)

66 ～ 73 行，第二層隱藏層

74  # Linear
75  with tf.name_scope('softmax_linear'):
76    weights = tf.Variable(
77        tf.truncated_normal([hidden2_units, NUM_CLASSES],
78                          stddev=1.0/math.sqrt(float(hidden2_units))),
79        name='weights')
80    biases = tf.Variable(tf.zeros([NUM_CLASSES]),
81                         name='biases')
82    logits = tf.matmul(hidden2, weights) + biases
83  return logits

75 ～ 82 行，線性層。

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)

86 ～ 99 行，建立 loss Op。

labels = tf.to_int64(labels)

96 行，型別轉換。

 return tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
#以上為下載後的程式碼
#書中程式碼如下
97 cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( 
98 labels=labels, logits=logits, name=’xentropy’) 
99 return tf.reduce mean(cross_entropy, name=’xentropy_mean’)

97 ～ 98 行，這裡使用

sparse_softmax_cross_entropy_ with_logits loss

每張圖片只允許被標記為一個類別。
99 行，取均值。

tf.summary.scalar('loss', loss)

120 行，記錄 loss 變化數值。

optimizer = tf.train.GradientDescentOptimizer(learning_rate)

122 行，使用梯度下降優化器，傳入學習率。

global_step = tf.Variable(0, name='global_step', trainable=False)

124 行，建立變數來記錄全域性步數。

train_op = optimizer.minimize(loss, global_step=global_step)

127 行，使用優化器的目的是最小化 loss。

在最後執行檔案 fully_connected_ feed. py
這下就可以看到分類的結果了。 如果你願意，當然同樣可以用自己手寫的資料處理成 相應的大小，然後給程式去識別
若想了解更多內容，推介《白話深度學習與TensorFlow》該書，本部落格即基於該書部分內容編寫。

目錄