tensorflow--mnist注解

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function #absl是python标准库内的
from absl import app as absl_app
from absl import flags import tensorflow as tf # pylint: disable=g-bad-import-order from official.mnist import dataset
from official.utils.flags import core as flags_core
from official.utils.logs import hooks_helper
from official.utils.misc import distribution_utils
from official.utils.misc import model_helpers LEARNING_RATE = 1e-4 #参数默认data_format = 'channels_first'
def create_model(data_format):
"""Model to recognize digits in the MNIST dataset. Network structure is equivalent to:
https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/examples/tutorials/mnist/mnist_deep.py
and
https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py But uses the tf.keras API. Args:
data_format: Either 'channels_first' or 'channels_last'. 'channels_first' is
typically faster on GPUs while 'channels_last' is typically faster on
CPUs. See
https://www.tensorflow.org/performance/performance_guide#data_formats Returns:
A tf.keras.Model.
""" #data_format：一个字符串,可以是channels_last(默认)或channels_first,\
# 表示输入中维度的顺序,channels_last对应于具有形状(batch, height, width, channels)\
# 的输入,而channels_first对应于具有形状(batch, channels, height, width)的输入.
#这里感觉输入只有三个维度，默认是单通道图？
if data_format == 'channels_first':
input_shape = [1, 28, 28]
else:
assert data_format == 'channels_last'
input_shape = [28, 28, 1] #将tf.keras.layers.MaxPooling2D传递给max_pool
l = tf.keras.layers
max_pool = l.MaxPooling2D(
(2, 2), (2, 2), padding='same', data_format=data_format)
# The model consists of a sequential chain of layers, so tf.keras.Sequential
# (a subclass of tf.keras.Model) makes for a compact description.
return tf.keras.Sequential(
[
#输入层确保输入的大小符合网络需要[28, 28]->[1, 28, 28]
l.Reshape(
target_shape=input_shape,
input_shape=(28 * 28,)),
#卷积
l.Conv2D(
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
5,#卷积核大小，这里是5x5
padding='same',
data_format=data_format,
activation=tf.nn.relu),
#最大pooling
max_pool,
#卷积
l.Conv2D(
64,
5,
padding='same',
data_format=data_format,
activation=tf.nn.relu),
# 最大pooling
max_pool,
#在保留第0轴的情况下对输入的张量进行Flatten(扁平化),拉直？
l.Flatten(),
#fc 1024 -> units: 该层的神经单元结点数。
l.Dense(1024, activation=tf.nn.relu),
l.Dropout(0.4),
#fc输出
l.Dense(10)
]) #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
def define_mnist_flags():
flags_core.define_base()
flags_core.define_performance(num_parallel_calls=False)
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
#自定义项参数都在这里设置了
flags_core.set_defaults(data_dir='./tmp/mnist_data',
model_dir='./tmp/mnist_model',
batch_size=100,
train_epochs=40,
stop_threshold=0.998) def model_fn(features, labels, mode, params):
"""The model_fn argument for creating an Estimator."""
# 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
model = create_model(params['data_format'])
image = features
# 来判断一个对象是否是一个已知的类型。
if isinstance(image, dict):
image = features['image'] #测试模式
if mode == tf.estimator.ModeKeys.PREDICT:
logits = model(image, training=False)
predictions = {
'classes': tf.argmax(logits, axis=1),
'probabilities': tf.nn.softmax(logits),
}
#如果只是测试到这里就返回了
return tf.estimator.EstimatorSpec(
mode=tf.estimator.ModeKeys.PREDICT,
predictions=predictions,
export_outputs={
'classify': tf.estimator.export.PredictOutput(predictions)
}) #训练模式
if mode == tf.estimator.ModeKeys.TRAIN:
#设置LEARNING_RATE
optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE) logits = model(image, training=True)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
accuracy = tf.metrics.accuracy(
labels=labels, predictions=tf.argmax(logits, axis=1)) # Name tensors to be logged with LoggingTensorHook.
tf.identity(LEARNING_RATE, 'learning_rate')
tf.identity(loss, 'cross_entropy')
tf.identity(accuracy[1], name='train_accuracy') # Save accuracy scalar to Tensorboard output.
tf.summary.scalar('train_accuracy', accuracy[1]) return tf.estimator.EstimatorSpec(
mode=tf.estimator.ModeKeys.TRAIN,
loss=loss,
train_op=optimizer.minimize(loss, tf.train.get_or_create_global_step()))
if mode == tf.estimator.ModeKeys.EVAL:
logits = model(image, training=False)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
return tf.estimator.EstimatorSpec(
mode=tf.estimator.ModeKeys.EVAL,
loss=loss,
eval_metric_ops={
'accuracy':
tf.metrics.accuracy(
labels=labels, predictions=tf.argmax(logits, axis=1)),
}) def run_mnist(flags_obj):
"""Run MNIST training and eval loop. Args:
flags_obj: An object containing parsed flag values.
""" #apply_clean是官方例程里面提供的用来清理现存model的方法，\
# 取决于flags_obj.clean(True则清理flags_obj.model_dir内的文件)
model_helpers.apply_clean(flags_obj) #把自定义的实现传给tf.estimator.Estimator
model_function = model_fn #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
session_config = tf.ConfigProto(
#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
inter_op_parallelism_threads=flags_obj.inter_op_parallelism_threads,
#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
intra_op_parallelism_threads=flags_obj.intra_op_parallelism_threads,
#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，\
# 那么当运行设备不满足要求时，会自动分配GPU或者CPU
allow_soft_placement=True) #获取gpu数目，优化算法等,用于优化
distribution_strategy = distribution_utils.get_distribution_strategy(
flags_core.get_num_gpus(flags_obj), flags_obj.all_reduce_alg) #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.
#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,\
# 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,\
# 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\
# 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
run_config = tf.estimator.RunConfig(
train_distribute=distribution_strategy, session_config=session_config) data_format = flags_obj.data_format
#channels_first,即（3,128,128,128）通道数在最前面
#channels_last,即（128,128,128,3）通道数在最后面
if data_format is None:
data_format = ('channels_first'
if tf.test.is_built_with_cuda() else 'channels_last')#判断安装的TF是否支持GPU #estimator类对TensorFlow模型进行训练和计算.
#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.
mnist_classifier = tf.estimator.Estimator(
#这个model_fn是参数名而已
model_fn=model_function,#模型对象
model_dir=flags_obj.model_dir,#模型目录,如果为空会创建一个临时目录
#猜测会去model_dir中寻找数据
config=run_config,#运行的一些参数
params={
'data_format': data_format,#数据类型
}) #这里定义了两个内部函数，只能被这个语句块的内部调用
# Set up training and evaluation input functions.
def train_input_fn():
"""Prepare data for training.""" # When choosing shuffle buffer sizes, larger sizes result in better
# randomness, while smaller sizes use less memory. MNIST is a small
# enough dataset that we can easily shuffle the full epoch.
ds = dataset.train(flags_obj.data_dir)
ds = ds.cache().shuffle(buffer_size=50000).batch(flags_obj.batch_size) # Iterate through the dataset a set number (`epochs_between_evals`) of times
# during each training session.
ds = ds.repeat(flags_obj.epochs_between_evals)
return ds def eval_input_fn():
return dataset.test(flags_obj.data_dir).batch(
flags_obj.batch_size).make_one_shot_iterator().get_next() # Set up hook that outputs training logs every 100 steps.
train_hooks = hooks_helper.get_train_hooks(
flags_obj.hooks, model_dir=flags_obj.model_dir,
batch_size=flags_obj.batch_size) # Train and evaluate model.
for _ in range(flags_obj.train_epochs // flags_obj.epochs_between_evals):
#训练一次，验证一次
mnist_classifier.train(input_fn=train_input_fn, hooks=train_hooks)
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print('\nEvaluation results:\n\t%s\n' % eval_results) #如果eval_results['accuracy'] >= flags_obj.stop_threshold 说明模型训练好了
if model_helpers.past_stop_threshold(flags_obj.stop_threshold,
eval_results['accuracy']):
break # Export the model
if flags_obj.export_dir is not None:
#预分配内存，等待数据进入
image = tf.placeholder(tf.float32, [None, 28, 28])
input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({
'image': image,
})
#输出模型
mnist_classifier.export_savedmodel(flags_obj.export_dir, input_fn) def main(_):
run_mnist(flags.FLAGS) if __name__ == '__main__':
#日志
tf.logging.set_verbosity(tf.logging.INFO)
#给flags.FLAGS添加了很多参数项目
define_mnist_flags()
#带参数的启动
absl_app.run(main)