基於TensorFlow解決手寫數字識別的Softmax方法、多層卷積網絡方法和前饋神經網絡方法

時間 2019-11-11

標籤基於 tensorflow 解決手寫數字識別 softmax 方法多層網絡神經網絡欄目系統網絡简体版

原文原文鏈接

一.基於TensorFlow的softmax迴歸模型解決手寫字母識別問題python

詳細步驟以下：git

1.加載MNIST數據： input_data.read_data_sets('MNIST_data',one_hot=true)express

2.運行TensorFlow的InterractiveSession: sess = tf.InteractiveSession()apache

3.構建Softmax迴歸模型: 佔位符tf.placeholder 變量tf.Variable 類別預測與損失函數 tf.nn.softmax tf.refuce_sum 訓練模型 tf.train.GradientDescentOptimizer 評估模型網絡

結果：在測試集上有91%正確率session

二.構建多層卷積網絡app

詳細步驟以下：less

1.權重初始化函數ide

2.卷積和池化函數函數

3.第一層卷積

4.第二層卷積

5.密集鏈接層

6.輸出層

7.訓練和評估模型

代碼：（DeepMnist.py）

 1 from tensorflow.examples.tutorials.mnist import input_data
 2 mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
 3 
 4 import tensorflow as tf
 5 sess = tf.InteractiveSession()
 6 
 7 x = tf.placeholder(tf.float32, shape=[None, 784])
 8 y_ = tf.placeholder(tf.float32, shape=[None, 10])
 9 
10 w = tf.Variable(tf.zeros([784, 10]))
11 b = tf.Variable(tf.zeros([10]))
12 
13 sess.run(tf.global_variables_initializer())
14 
15 y = tf.matmul(x ,w) + b
16 
17 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_, logits=y))
18 
19 train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
20 
21 for _ in range(1000):
22     batch = mnist.train.next_batch(100)
23     train_step.run(feed_dict={x:batch[0],y_:batch[1]})
24 
25 correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
26 
27 accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
28 
29 print(accuracy.eval(feed_dict={x:mnist.test.images,y_:mnist.test.labels}))
30 
31 def weight_variable(shape):
32     initial = tf.truncated_normal(shape, stddev=0.1)
33     return tf.Variable(initial)
34 
35 def bias_variable(shape):
36     initial = tf.constant(0.1,shape=shape)
37     return tf.Variable(initial)
38 
39 def conv2d(x,w):
40     return tf.nn.conv2d(x,w,strides=[1,1,1,1],padding='SAME')
41 
42 def max_pool_2x2(x):
43     return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
44 
45 w_conv1 = weight_variable([5,5,1,32])
46 b_conv1 = bias_variable([32])
47 
48 x_image = tf.reshape(x, [-1,28,28,1])
49 
50 h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1)
51 h_pool1 = max_pool_2x2(h_conv1)
52 
53 w_conv2 = weight_variable([5,5,32,64])
54 b_conv2 = bias_variable([64])
55 
56 h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2)
57 h_pool2 = max_pool_2x2(h_conv2)
58 
59 w_fc1 = weight_variable([7*7*64,1024])
60 b_fc1 =bias_variable([1024])
61 
62 h_pool2_flat = tf.reshape(h_pool2, [-1,7*7*64])
63 h_fc1 =tf.nn.relu(tf.matmul(h_pool2_flat,w_fc1) + b_fc1)
64 
65 keep_prob = tf.placeholder(tf.float32)
66 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
67 
68 W_fc2 = weight_variable([1024, 10])
69 b_fc2 = bias_variable([10])
70 
71 y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
72 
73 cross_entropy = tf.reduce_mean(
74     tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
75 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
76 correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
77 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
78 sess.run(tf.global_variables_initializer())
79 for i in range(1000):
80   batch = mnist.train.next_batch(50)
81   if i%100 == 0:
82     train_accuracy = accuracy.eval(feed_dict={
83         x:batch[0], y_: batch[1], keep_prob: 1.0})
84     print("step %d, training accuracy %g"%(i, train_accuracy))
85   train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
86 
87 print("test accuracy %g"%accuracy.eval(feed_dict={
88     x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

輸出：

訓練1000次，測試準確率96.34%；20000次準確率達到99%以上；

三.簡易前饋神經網絡

代碼以下：

  1 # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  2 #
  3 # Licensed under the Apache License, Version 2.0 (the "License");
  4 # you may not use this file except in compliance with the License.
  5 # You may obtain a copy of the License at
  6 #
  7 #     http://www.apache.org/licenses/LICENSE-2.0
  8 #
  9 # Unless required by applicable law or agreed to in writing, software
 10 # distributed under the License is distributed on an "AS IS" BASIS,
 11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 12 # See the License for the specific language governing permissions and
 13 # limitations under the License.
 14 # ==============================================================================
 15 
 16 """Builds the MNIST network.
 17 Implements the inference/loss/training pattern for model building.
 18 1. inference() - Builds the model as far as is required for running the network
 19 forward to make predictions.
 20 2. loss() - Adds to the inference model the layers required to generate loss.
 21 3. training() - Adds to the loss model the Ops required to generate and
 22 apply gradients.
 23 This file is used by the various "fully_connected_*.py" files and not meant to
 24 be run.
 25 """
 26 from __future__ import absolute_import
 27 from __future__ import division
 28 from __future__ import print_function
 29 
 30 import math
 31 
 32 import tensorflow as tf
 33 
 34 # The MNIST dataset has 10 classes, representing the digits 0 through 9.
 35 NUM_CLASSES = 10
 36 
 37 # The MNIST images are always 28x28 pixels.
 38 IMAGE_SIZE = 28
 39 IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE
 40 
 41 
 42 def inference(images, hidden1_units, hidden2_units):
 43   """Build the MNIST model up to where it may be used for inference.
 44   Args:
 45     images: Images placeholder, from inputs().
 46     hidden1_units: Size of the first hidden layer.
 47     hidden2_units: Size of the second hidden layer.
 48   Returns:
 49     softmax_linear: Output tensor with the computed logits.
 50   """
 51   # Hidden 1
 52   with tf.name_scope('hidden1'):
 53     weights = tf.Variable(
 54         tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
 55                             stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),
 56         name='weights')
 57     biases = tf.Variable(tf.zeros([hidden1_units]),
 58                          name='biases')
 59     hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
 60   # Hidden 2
 61   with tf.name_scope('hidden2'):
 62     weights = tf.Variable(
 63         tf.truncated_normal([hidden1_units, hidden2_units],
 64                             stddev=1.0 / math.sqrt(float(hidden1_units))),
 65         name='weights')
 66     biases = tf.Variable(tf.zeros([hidden2_units]),
 67                          name='biases')
 68     hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
 69   # Linear
 70   with tf.name_scope('softmax_linear'):
 71     weights = tf.Variable(
 72         tf.truncated_normal([hidden2_units, NUM_CLASSES],
 73                             stddev=1.0 / math.sqrt(float(hidden2_units))),
 74         name='weights')
 75     biases = tf.Variable(tf.zeros([NUM_CLASSES]),
 76                          name='biases')
 77     logits = tf.matmul(hidden2, weights) + biases
 78   return logits
 79 
 80 
 81 def loss(logits, labels):
 82   """Calculates the loss from the logits and the labels.
 83   Args:
 84     logits: Logits tensor, float - [batch_size, NUM_CLASSES].
 85     labels: Labels tensor, int32 - [batch_size].
 86   Returns:
 87     loss: Loss tensor of type float.
 88   """
 89   labels = tf.to_int64(labels)
 90   cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
 91       labels=labels, logits=logits, name='xentropy')
 92   return tf.reduce_mean(cross_entropy, name='xentropy_mean')
 93 
 94 
 95 def training(loss, learning_rate):
 96   """Sets up the training Ops.
 97   Creates a summarizer to track the loss over time in TensorBoard.
 98   Creates an optimizer and applies the gradients to all trainable variables.
 99   The Op returned by this function is what must be passed to the
100   `sess.run()` call to cause the model to train.
101   Args:
102     loss: Loss tensor, from loss().
103     learning_rate: The learning rate to use for gradient descent.
104   Returns:
105     train_op: The Op for training.
106   """
107   # Add a scalar summary for the snapshot loss.
108   tf.summary.scalar('loss', loss)
109   # Create the gradient descent optimizer with the given learning rate.
110   optimizer = tf.train.GradientDescentOptimizer(learning_rate)
111   # Create a variable to track the global step.
112   global_step = tf.Variable(0, name='global_step', trainable=False)
113   # Use the optimizer to apply the gradients that minimize the loss
114   # (and also increment the global step counter) as a single training step.
115   train_op = optimizer.minimize(loss, global_step=global_step)
116   return train_op
117 
118 
119 def evaluation(logits, labels):
120   """Evaluate the quality of the logits at predicting the label.
121   Args:
122     logits: Logits tensor, float - [batch_size, NUM_CLASSES].
123     labels: Labels tensor, int32 - [batch_size], with values in the
124       range [0, NUM_CLASSES).
125   Returns:
126     A scalar int32 tensor with the number of examples (out of batch_size)
127     that were predicted correctly.
128   """
129   # For a classifier model, we can use the in_top_k Op.
130   # It returns a bool tensor with shape [batch_size] that is true for
131   # the examples where the label is in the top k (here k=1)
132   # of all logits for that example.
133   correct = tf.nn.in_top_k(logits, labels, 1)
134   # Return the number of true entries.
135   return tf.reduce_sum(tf.cast(correct, tf.int32))

mnist.py

  1 # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  2 #
  3 # Licensed under the Apache License, Version 2.0 (the "License");
  4 # you may not use this file except in compliance with the License.
  5 # You may obtain a copy of the License at
  6 #
  7 #     http://www.apache.org/licenses/LICENSE-2.0
  8 #
  9 # Unless required by applicable law or agreed to in writing, software
 10 # distributed under the License is distributed on an "AS IS" BASIS,
 11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 12 # See the License for the specific language governing permissions and
 13 # limitations under the License.
 14 # ==============================================================================
 15 
 16 """Trains and Evaluates the MNIST network using a feed dictionary."""
 17 from __future__ import absolute_import
 18 from __future__ import division
 19 from __future__ import print_function
 20 
 21 # pylint: disable=missing-docstring
 22 import argparse
 23 import os.path
 24 import sys
 25 import time
 26 
 27 from six.moves import xrange  # pylint: disable=redefined-builtin
 28 import tensorflow as tf
 29 
 30 from tensorflow.examples.tutorials.mnist import input_data
 31 from tensorflow.examples.tutorials.mnist import mnist
 32 
 33 # Basic model parameters as external flags.
 34 FLAGS = None
 35 
 36 
 37 def placeholder_inputs(batch_size):
 38   """Generate placeholder variables to represent the input tensors.
 39   These placeholders are used as inputs by the rest of the model building
 40   code and will be fed from the downloaded data in the .run() loop, below.
 41   Args:
 42     batch_size: The batch size will be baked into both placeholders.
 43   Returns:
 44     images_placeholder: Images placeholder.
 45     labels_placeholder: Labels placeholder.
 46   """
 47   # Note that the shapes of the placeholders match the shapes of the full
 48   # image and label tensors, except the first dimension is now batch_size
 49   # rather than the full size of the train or test data sets.
 50   images_placeholder = tf.placeholder(tf.float32, shape=(batch_size,
 51                                                          mnist.IMAGE_PIXELS))
 52   labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))
 53   return images_placeholder, labels_placeholder
 54 
 55 
 56 def fill_feed_dict(data_set, images_pl, labels_pl):
 57   """Fills the feed_dict for training the given step.
 58   A feed_dict takes the form of:
 59   feed_dict = {
 60       <placeholder>: <tensor of values to be passed for placeholder>,
 61       ....
 62   }
 63   Args:
 64     data_set: The set of images and labels, from input_data.read_data_sets()
 65     images_pl: The images placeholder, from placeholder_inputs().
 66     labels_pl: The labels placeholder, from placeholder_inputs().
 67   Returns:
 68     feed_dict: The feed dictionary mapping from placeholders to values.
 69   """
 70   # Create the feed_dict for the placeholders filled with the next
 71   # `batch size` examples.
 72   images_feed, labels_feed = data_set.next_batch(FLAGS.batch_size,
 73                                                  FLAGS.fake_data)
 74   feed_dict = {
 75       images_pl: images_feed,
 76       labels_pl: labels_feed,
 77   }
 78   return feed_dict
 79 
 80 
 81 def do_eval(sess,
 82             eval_correct,
 83             images_placeholder,
 84             labels_placeholder,
 85             data_set):
 86   """Runs one evaluation against the full epoch of data.
 87   Args:
 88     sess: The session in which the model has been trained.
 89     eval_correct: The Tensor that returns the number of correct predictions.
 90     images_placeholder: The images placeholder.
 91     labels_placeholder: The labels placeholder.
 92     data_set: The set of images and labels to evaluate, from
 93       input_data.read_data_sets().
 94   """
 95   # And run one epoch of eval.
 96   true_count = 0  # Counts the number of correct predictions.
 97   steps_per_epoch = data_set.num_examples // FLAGS.batch_size
 98   num_examples = steps_per_epoch * FLAGS.batch_size
 99   for step in xrange(steps_per_epoch):
100     feed_dict = fill_feed_dict(data_set,
101                                images_placeholder,
102                                labels_placeholder)
103     true_count += sess.run(eval_correct, feed_dict=feed_dict)
104   precision = float(true_count) / num_examples
105   print('  Num examples: %d  Num correct: %d  Precision @ 1: %0.04f' %
106         (num_examples, true_count, precision))
107 
108 
109 def run_training():
110   """Train MNIST for a number of steps."""
111   # Get the sets of images and labels for training, validation, and
112   # test on MNIST.
113   data_sets = input_data.read_data_sets(FLAGS.input_data_dir, FLAGS.fake_data)
114 
115   # Tell TensorFlow that the model will be built into the default Graph.
116   with tf.Graph().as_default():
117     # Generate placeholders for the images and labels.
118     images_placeholder, labels_placeholder = placeholder_inputs(
119         FLAGS.batch_size)
120 
121     # Build a Graph that computes predictions from the inference model.
122     logits = mnist.inference(images_placeholder,
123                              FLAGS.hidden1,
124                              FLAGS.hidden2)
125 
126     # Add to the Graph the Ops for loss calculation.
127     loss = mnist.loss(logits, labels_placeholder)
128 
129     # Add to the Graph the Ops that calculate and apply gradients.
130     train_op = mnist.training(loss, FLAGS.learning_rate)
131 
132     # Add the Op to compare the logits to the labels during evaluation.
133     eval_correct = mnist.evaluation(logits, labels_placeholder)
134 
135     # Build the summary Tensor based on the TF collection of Summaries.
136     summary = tf.summary.merge_all()
137 
138     # Add the variable initializer Op.
139     init = tf.global_variables_initializer()
140 
141     # Create a saver for writing training checkpoints.
142     saver = tf.train.Saver()
143 
144     # Create a session for running Ops on the Graph.
145     sess = tf.Session()
146 
147     # Instantiate a SummaryWriter to output summaries and the Graph.
148     summary_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)
149 
150     # And then after everything is built:
151 
152     # Run the Op to initialize the variables.
153     sess.run(init)
154 
155     # Start the training loop.
156     for step in xrange(FLAGS.max_steps):
157       start_time = time.time()
158 
159       # Fill a feed dictionary with the actual set of images and labels
160       # for this particular training step.
161       feed_dict = fill_feed_dict(data_sets.train,
162                                  images_placeholder,
163                                  labels_placeholder)
164 
165       # Run one step of the model.  The return values are the activations
166       # from the `train_op` (which is discarded) and the `loss` Op.  To
167       # inspect the values of your Ops or variables, you may include them
168       # in the list passed to sess.run() and the value tensors will be
169       # returned in the tuple from the call.
170       _, loss_value = sess.run([train_op, loss],
171                                feed_dict=feed_dict)
172 
173       duration = time.time() - start_time
174 
175       # Write the summaries and print an overview fairly often.
176       if step % 100 == 0:
177         # Print status to stdout.
178         print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration))
179         # Update the events file.
180         summary_str = sess.run(summary, feed_dict=feed_dict)
181         summary_writer.add_summary(summary_str, step)
182         summary_writer.flush()
183 
184       # Save a checkpoint and evaluate the model periodically.
185       if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
186         checkpoint_file = os.path.join(FLAGS.log_dir, 'model.ckpt')
187         saver.save(sess, checkpoint_file, global_step=step)
188         # Evaluate against the training set.
189         print('Training Data Eval:')
190         do_eval(sess,
191                 eval_correct,
192                 images_placeholder,
193                 labels_placeholder,
194                 data_sets.train)
195         # Evaluate against the validation set.
196         print('Validation Data Eval:')
197         do_eval(sess,
198                 eval_correct,
199                 images_placeholder,
200                 labels_placeholder,
201                 data_sets.validation)
202         # Evaluate against the test set.
203         print('Test Data Eval:')
204         do_eval(sess,
205                 eval_correct,
206                 images_placeholder,
207                 labels_placeholder,
208                 data_sets.test)
209 
210 
211 def main(_):
212   if tf.gfile.Exists(FLAGS.log_dir):
213     tf.gfile.DeleteRecursively(FLAGS.log_dir)
214   tf.gfile.MakeDirs(FLAGS.log_dir)
215   run_training()
216 
217 
218 if __name__ == '__main__':
219   parser = argparse.ArgumentParser()
220   parser.add_argument(
221       '--learning_rate',
222       type=float,
223       default=0.01,
224       help='Initial learning rate.'
225   )
226   parser.add_argument(
227       '--max_steps',
228       type=int,
229       default=2000,
230       help='Number of steps to run trainer.'
231   )
232   parser.add_argument(
233       '--hidden1',
234       type=int,
235       default=128,
236       help='Number of units in hidden layer 1.'
237   )
238   parser.add_argument(
239       '--hidden2',
240       type=int,
241       default=32,
242       help='Number of units in hidden layer 2.'
243   )
244   parser.add_argument(
245       '--batch_size',
246       type=int,
247       default=100,
248       help='Batch size.  Must divide evenly into the dataset sizes.'
249   )
250   parser.add_argument(
251       '--input_data_dir',
252       type=str,
253       default='/tmp/tensorflow/mnist/input_data',
254       help='Directory to put the input data.'
255   )
256   parser.add_argument(
257       '--log_dir',
258       type=str,
259       default='/tmp/tensorflow/mnist/logs/fully_connected_feed',
260       help='Directory to put the log data.'
261   )
262   parser.add_argument(
263       '--fake_data',
264       default=False,
265       help='If true, uses fake data for unit testing.',
266       action='store_true'
267   )
268 
269   FLAGS, unparsed = parser.parse_known_args()
270   tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)