tensorflow--mnist注解
我自己对mnist官方例程进行了部分注解，希望分享出来有助于入门选手更好理解tensorflow的运行机制，可以拷贝到IDE再调试看看，看看具体数据流向还有一部分tensorflow里面用到的库。
我用的是pip安装的tensorflow-GPU-1.13,这段源码原始位置在https://github.com/tensorflow/models/blob/master/official/mnist/mnist.py
代码：
1 from __future__ import absolute_import
2 from __future__ import division
3 from __future__ import print_function
4
5 #absl是python标准库内的
6 from absl import app as absl_app
7 from absl import flags
8
9 import tensorflow as tf # pylint: disable=g-bad-import-order
10
11 from official.mnist import dataset
12 from official.utils.flags import core as flags_core
13 from official.utils.logs import hooks_helper
14 from official.utils.misc import distribution_utils
15 from official.utils.misc import model_helpers
16
17
18 LEARNING_RATE = 1e-4
19
20 #参数默认data_format = 'channels_first'
21 def create_model(data_format):
22 """Model to recognize digits in the MNIST dataset.
23
24 Network structure is equivalent to:
25 https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/examples/tutorials/mnist/mnist_deep.py
26 and
27 https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py
28
29 But uses the tf.keras API.
30
31 Args:
32 data_format: Either 'channels_first' or 'channels_last'. 'channels_first' is
33 typically faster on GPUs while 'channels_last' is typically faster on
34 CPUs. See
35 https://www.tensorflow.org/performance/performance_guide#data_formats
36
37 Returns:
38 A tf.keras.Model.
39 """
40
41 #data_format：一个字符串,可以是channels_last(默认)或channels_first,\
42 # 表示输入中维度的顺序,channels_last对应于具有形状(batch, height, width, channels)\
43 # 的输入,而channels_first对应于具有形状(batch, channels, height, width)的输入.
44 #这里感觉输入只有三个维度，默认是单通道图？
45 if data_format == 'channels_first':
46 input_shape = [1, 28, 28]
47 else:
48 assert data_format == 'channels_last'
49 input_shape = [28, 28, 1]
50
51 #将tf.keras.layers.MaxPooling2D传递给max_pool
52 l = tf.keras.layers
53 max_pool = l.MaxPooling2D(
54 (2, 2), (2, 2), padding='same', data_format=data_format)
55 # The model consists of a sequential chain of layers, so tf.keras.Sequential
56 # (a subclass of tf.keras.Model) makes for a compact description.
57 return tf.keras.Sequential(
58 [
59 #输入层确保输入的大小符合网络需要[28, 28]->[1, 28, 28]
60 l.Reshape(
61 target_shape=input_shape,
62 input_shape=(28 * 28,)),
63 #卷积
64 l.Conv2D(
65 32,#filters：整数, 输出空间的维数(即卷积中的滤波器数),就是卷积核个数
66 5,#卷积核大小，这里是5x5
67 padding='same',
68 data_format=data_format,
69 activation=tf.nn.relu),
70 #最大pooling
71 max_pool,
72 #卷积
73 l.Conv2D(
74 64,
75 5,
76 padding='same',
77 data_format=data_format,
78 activation=tf.nn.relu),
79 # 最大pooling
80 max_pool,
81 #在保留第0轴的情况下对输入的张量进行Flatten(扁平化),拉直？
82 l.Flatten(),
83 #fc 1024 -> units: 该层的神经单元结点数。
84 l.Dense(1024, activation=tf.nn.relu),
85 l.Dropout(0.4),
86 #fc输出
87 l.Dense(10)
88 ])
89
90 #添加了很多参数，指定了一部分的值，数据url，模型url，batch_size等等
91 def define_mnist_flags():
92 flags_core.define_base()
93 flags_core.define_performance(num_parallel_calls=False)
94 flags_core.define_image()
95 flags.adopt_module_key_flags(flags_core)
96 #自定义项参数都在这里设置了
97 flags_core.set_defaults(data_dir='./tmp/mnist_data',
98 model_dir='./tmp/mnist_model',
99 batch_size=100,
100 train_epochs=40,
101 stop_threshold=0.998)
102
103
104 def model_fn(features, labels, mode, params):
105 """The model_fn argument for creating an Estimator."""
106 # 翻译成中文，注释的意思就是添加一个data_format的参数,下面的Estimator类需要用到
107 model = create_model(params['data_format'])
108 image = features
109 # 来判断一个对象是否是一个已知的类型。
110 if isinstance(image, dict):
111 image = features['image']
112
113 #测试模式
114 if mode == tf.estimator.ModeKeys.PREDICT:
115 logits = model(image, training=False)
116 predictions = {
117 'classes': tf.argmax(logits, axis=1),
118 'probabilities': tf.nn.softmax(logits),
119 }
120 #如果只是测试到这里就返回了
121 return tf.estimator.EstimatorSpec(
122 mode=tf.estimator.ModeKeys.PREDICT,
123 predictions=predictions,
124 export_outputs={
125 'classify': tf.estimator.export.PredictOutput(predictions)
126 })
127
128 #训练模式
129 if mode == tf.estimator.ModeKeys.TRAIN:
130 #设置LEARNING_RATE
131 optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE)
132
133 logits = model(image, training=True)
134 loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
135 accuracy = tf.metrics.accuracy(
136 labels=labels, predictions=tf.argmax(logits, axis=1))
137
138 # Name tensors to be logged with LoggingTensorHook.
139 tf.identity(LEARNING_RATE, 'learning_rate')
140 tf.identity(loss, 'cross_entropy')
141 tf.identity(accuracy[1], name='train_accuracy')
142
143 # Save accuracy scalar to Tensorboard output.
144 tf.summary.scalar('train_accuracy', accuracy[1])
145
146 return tf.estimator.EstimatorSpec(
147 mode=tf.estimator.ModeKeys.TRAIN,
148 loss=loss,
149 train_op=optimizer.minimize(loss, tf.train.get_or_create_global_step()))
150 if mode == tf.estimator.ModeKeys.EVAL:
151 logits = model(image, training=False)
152 loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
153 return tf.estimator.EstimatorSpec(
154 mode=tf.estimator.ModeKeys.EVAL,
155 loss=loss,
156 eval_metric_ops={
157 'accuracy':
158 tf.metrics.accuracy(
159 labels=labels, predictions=tf.argmax(logits, axis=1)),
160 })
161
162
163 def run_mnist(flags_obj):
164 """Run MNIST training and eval loop.
165
166 Args:
167 flags_obj: An object containing parsed flag values.
168 """
169
170 #apply_clean是官方例程里面提供的用来清理现存model的方法，\
171 # 取决于flags_obj.clean(True则清理flags_obj.model_dir内的文件)
172 model_helpers.apply_clean(flags_obj)
173
174 #把自定义的实现传给tf.estimator.Estimator
175 model_function = model_fn
176
177 #tf.ConfigProto()主要的作用是配置tf.Session的运算方式，比如gpu运算或者cpu运算
178 session_config = tf.ConfigProto(
179 #设置线程一个操作内部并行运算的线程数，比如矩阵乘法，如果设置为０，则表示以最优的线程数处理
180 inter_op_parallelism_threads=flags_obj.inter_op_parallelism_threads,
181 #设置多个操作并行运算的线程数，比如 c = a + b，d = e + f . 可以并行运算
182 intra_op_parallelism_threads=flags_obj.intra_op_parallelism_threads,
183 #有时候，不同的设备，它的cpu和gpu是不同的，如果将这个选项设置成True，\
184 # 那么当运行设备不满足要求时，会自动分配GPU或者CPU
185 allow_soft_placement=True)
186
187 #获取gpu数目，优化算法等,用于优化
188 distribution_strategy = distribution_utils.get_distribution_strategy(
189 flags_core.get_num_gpus(flags_obj), flags_obj.all_reduce_alg)
190
191 #所有输出(检查点,事件文件等)都被写入model_dir或其子目录.如果model_dir未设置,则使用临时目录.
192 #可以通过RunConfig对象(包含了有关执行环境的信息)传递config参数.它被传递给model_fn,\
193 # 如果model_fn有一个名为“config”的参数(和输入函数以相同的方式).如果该config参数未被传递,\
194 # 则由Estimator进行实例化.不传递配置意味着使用对本地执行有用的默认值.Estimator使配置对模型\
195 # 可用(例如,允许根据可用的工作人员数量进行专业化),并且还使用其一些字段来控制内部,特别是关于检查点
196 run_config = tf.estimator.RunConfig(
197 train_distribute=distribution_strategy, session_config=session_config)
198
199 data_format = flags_obj.data_format
200 #channels_first,即（3,128,128,128）通道数在最前面
201 #channels_last,即（128,128,128,3）通道数在最后面
202 if data_format is None:
203 data_format = ('channels_first'
204 if tf.test.is_built_with_cuda() else 'channels_last')#判断安装的TF是否支持GPU
205
206 #estimator类对TensorFlow模型进行训练和计算.
207 #Estimator对象包装由model_fn指定的模型,其中,给定输入和其他一些参数,返回需要进行训练、计算,或预测的操作.
208 mnist_classifier = tf.estimator.Estimator(
209 #这个model_fn是参数名而已
210 model_fn=model_function,#模型对象
211 model_dir=flags_obj.model_dir,#模型目录,如果为空会创建一个临时目录
212 #猜测会去model_dir中寻找数据
213 config=run_config,#运行的一些参数
214 params={
215 'data_format': data_format,#数据类型
216 })
217
218 #这里定义了两个内部函数，只能被这个语句块的内部调用
219 # Set up training and evaluation input functions.
220 def train_input_fn():
221 """Prepare data for training."""
222
223 # When choosing shuffle buffer sizes, larger sizes result in better
224 # randomness, while smaller sizes use less memory. MNIST is a small
225 # enough dataset that we can easily shuffle the full epoch.
226 ds = dataset.train(flags_obj.data_dir)
227 ds = ds.cache().shuffle(buffer_size=50000).batch(flags_obj.batch_size)
228
229 # Iterate through the dataset a set number (`epochs_between_evals`) of times
230 # during each training session.
231 ds = ds.repeat(flags_obj.epochs_between_evals)
232 return ds
233
234 def eval_input_fn():
235 return dataset.test(flags_obj.data_dir).batch(
236 flags_obj.batch_size).make_one_shot_iterator().get_next()
237
238 # Set up hook that outputs training logs every 100 steps.
239 train_hooks = hooks_helper.get_train_hooks(
240 flags_obj.hooks, model_dir=flags_obj.model_dir,
241 batch_size=flags_obj.batch_size)
242
243 # Train and evaluate model.
244 for _ in range(flags_obj.train_epochs // flags_obj.epochs_between_evals):
245 #训练一次，验证一次
246 mnist_classifier.train(input_fn=train_input_fn, hooks=train_hooks)
247 eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
248 print('\nEvaluation results:\n\t%s\n' % eval_results)
249
250 #如果eval_results['accuracy'] >= flags_obj.stop_threshold 说明模型训练好了
251 if model_helpers.past_stop_threshold(flags_obj.stop_threshold,
252 eval_results['accuracy']):
253 break
254
255 # Export the model
256 if flags_obj.export_dir is not None:
257 #预分配内存，等待数据进入
258 image = tf.placeholder(tf.float32, [None, 28, 28])
259 input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({
260 'image': image,
261 })
262 #输出模型
263 mnist_classifier.export_savedmodel(flags_obj.export_dir, input_fn)
264
265
266 def main(_):
267 run_mnist(flags.FLAGS)
268
269
270 if __name__ == '__main__':
271 #日志
272 tf.logging.set_verbosity(tf.logging.INFO)
273 #给flags.FLAGS添加了很多参数项目
274 define_mnist_flags()
275 #带参数的启动
276 absl_app.run(main)