转载请注明出处：

https://www.cnblogs.com/darkknightzh/p/9989586.html

代码网址：

https://github.com/darkknightzh/trainEagerMnist

参考网址：

https://github.com/tensorflow/models/blob/master/official/mnist/mnist_eager.py

https://github.com/madalinabuzau/tensorflow-eager-tutorials/blob/master/07_convolutional_neural_networks_for_emotion_recognition.ipynb

一总体流程

tensorflow使用eager时，需要下面几句话（如果不使用第三句话，则依旧可以使用静态图）：

import tensorflow as tf

import tensorflow.contrib.eager as tfe

tfe.enable_eager_execution()

tensorflow使用eager模式后，感觉和pytorch一样方便。使用eager后，不需要tf.placeholder，用起来更加方便。

目前貌似tf.keras.layers和tf.layers支持eager，slim不支持。

总体流程如下：

initial optimizer

for I in range(epochs):

    for imgs, targets in training_data:

        with tf.GradientTape() as tape:

            logits = model(imgs, training=True)

            loss_value = calc_loss(logits, targets)

        grads = tape.gradient(loss_value, model.variables)

        optimizer.apply_gradients(zip(grads, model.variables), global_step=step_counter)

        update training_accurate, total_loss

    test model

    save model

二创建模型

可以使用下面三种方式创建模型

1. 类似pytorch的方式

先在__init__中定义用到的层，然后重载call函数，构建网络。模型前向计算时，会调用call函数。如下面代码所示：

 class simpleModel(tf.keras.Model):

     def __init__(self, num_classes):

         super(simpleModel, self).__init__()

         input_shape = [28, 28, 1]

         data_format = 'channels_last'

         self.reshape = tf.keras.layers.Reshape(target_shape=input_shape, input_shape=(input_shape[0] * input_shape[1],))

         self.conv1 = tf.keras.layers.Conv2D(16, 5, padding="same", activation='relu')

         self.batch1 = tf.keras.layers.BatchNormalization()

         self.pool1 = tf.keras.layers.MaxPooling2D((2, 2), (2, 2), padding='same', data_format=data_format)

         self.conv2 = tf.keras.layers.Conv2D(32, 5, padding="same", activation='relu')

         self.batch2 = tf.keras.layers.BatchNormalization()

         self.pool2 = tf.keras.layers.MaxPooling2D((2, 2), (2, 2), padding='same', data_format=data_format)

         self.conv3 = tf.keras.layers.Conv2D(64, 5, padding="same", activation='relu')

         self.batch3 = tf.keras.layers.BatchNormalization()

         self.pool3 = tf.keras.layers.MaxPooling2D((2, 2), (2, 2), padding='same', data_format=data_format)

         self.conv4 = tf.keras.layers.Conv2D(64, 5, padding="same", activation='relu')

         self.batch4 = tf.keras.layers.BatchNormalization()

         self.pool4 = tf.keras.layers.MaxPooling2D((2, 2), (2, 2), padding='same', data_format=data_format)

         self.flat = tf.keras.layers.Flatten()

         self.fc5 = tf.keras.layers.Dense(1024, activation='relu')

         self.batch5 = tf.keras.layers.BatchNormalization()

         self.fc6 = tf.keras.layers.Dense(num_classes)

         self.batch6 = tf.keras.layers.BatchNormalization()

     def call(self, inputs, training=None):

         x = self.reshape(inputs)

         x = self.conv1(x)

         x = self.batch1(x, training=training)

         x = self.pool1(x)

         x = self.conv2(x)

         x = self.batch2(x, training=training)

         x = self.pool2(x)

         x = self.conv3(x)

         x = self.batch3(x, training=training)

         x = self.pool3(x)

         x = self.conv4(x)

         x = self.batch4(x, training=training)

         x = self.pool4(x)

         x = self.flat(x)

         x = self.fc5(x)

         x = self.batch5(x, training=training)

         x = self.fc6(x)

         x = self.batch6(x, training=training)

         # x = tf.layers.dropout(x, rate=0.3, training=training)

         return x

     def get_acc(self, target):

         correct_prediction = tf.equal(tf.argmax(self.logits, 1), tf.argmax(target, 1))

         acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

         return acc

     def get_loss(self):

         return self.loss

     def loss_fn(self, images, target, training):

         self.logits = self(images, training)  # call call(self, inputs, training=None) function

         self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.logits, labels=target))

         return self.loss

     def grads_fn(self, images, target, training):  # do not return loss and acc if unnecessary

         with tfe.GradientTape() as tape:

             loss = self.loss_fn(images, target, training)

         return tape.gradient(loss, self.variables)

2. 直接使用tf.keras.Sequential

如下面代码所示：

 def create_model1():

     data_format = 'channels_last'

     input_shape = [28, 28, 1]

     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(

         [

             l.Reshape(target_shape=input_shape, input_shape=(28 * 28,)),

             l.Conv2D(16, 5, padding='same', data_format=data_format, activation=tf.nn.relu),

             l.BatchNormalization(),

             max_pool,

             l.Conv2D(32, 5, padding='same', data_format=data_format, activation=tf.nn.relu),

             l.BatchNormalization(),

             max_pool,

             l.Conv2D(64, 5, padding='same', data_format=data_format, activation=tf.nn.relu),

             l.BatchNormalization(),

             max_pool,

             l.Conv2D(64, 5, padding='same', data_format=data_format, activation=tf.nn.relu),

             l.BatchNormalization(),

             max_pool,

             l.Flatten(),

             l.Dense(1024, activation=tf.nn.relu),

             l.BatchNormalization(),

             # # l.Dropout(0.4),

             l.Dense(10),

             l.BatchNormalization()

         ])

3. 使用tf.keras.Sequential()及add函数

如下面代码所示：

 def create_model2():

     data_format = 'channels_last'

     input_shape = [28, 28, 1]

     model = tf.keras.Sequential()

     model.add(tf.keras.layers.Reshape(target_shape=input_shape, input_shape=(input_shape[0] * input_shape[1],)))

     model.add(tf.keras.layers.Conv2D(16, 5, padding="same", activation='relu'))

     model.add(tf.keras.layers.BatchNormalization())

     model.add(tf.keras.layers.MaxPooling2D((2, 2), (2, 2), padding='same', data_format=data_format))

     model.add(tf.keras.layers.Conv2D(32, 5, padding="same", activation='relu'))

     model.add(tf.keras.layers.BatchNormalization())

     model.add(tf.keras.layers.MaxPooling2D((2, 2), (2, 2), padding='same', data_format=data_format))

     model.add(tf.keras.layers.Conv2D(64, 5, padding="same", activation='relu'))

     model.add(tf.keras.layers.BatchNormalization())

     model.add(tf.keras.layers.MaxPooling2D((2, 2), (2, 2), padding='same', data_format=data_format))

     model.add(tf.keras.layers.Conv2D(64, 5, padding="same", activation='relu'))

     model.add(tf.keras.layers.BatchNormalization())

     model.add(tf.keras.layers.MaxPooling2D((2, 2), (2, 2), padding='same', data_format=data_format))

     model.add(tf.keras.layers.Flatten())

     model.add(tf.keras.layers.Dense(1024, activation='relu'))

     model.add(tf.keras.layers.BatchNormalization())

     model.add(tf.keras.layers.Dense(10))

     model.add(tf.keras.layers.BatchNormalization())

 return model

三使用动态图更新梯度

在更新梯度时，需要加上下面的几句话

 with tf.GradientTape() as tape:

     logits = model(imgs, training=True)

     loss_value = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labs))

 grads = tape.gradient(loss_value, model.variables)

 optimizer.apply_gradients(zip(grads, model.variables), global_step=step_counter)

第二行得到特征，第三行得到损失，第四行得到梯度，第五行将梯度应用到模型，更新模型参数。

四保存及载入模型

1. 使用tfe.Saver

代码如下

 def saveModelV1(model_dir, model, global_step, modelname='model1'):

     tfe.Saver(model.variables).save(os.path.join(model_dir, modelname), global_step=global_step)

 def restoreModelV1(model_dir, model):

     dummy_input = tf.constant(tf.zeros((1, 28, 28, 1)))  # Run the model once to initialize variables

     dummy_pred = model(dummy_input, training=False)

     saver = tfe.Saver(model.variables)  # Restore the variables of the model

     saver.restore(tf.train.latest_checkpoint(model_dir))

2. 使用tf.train.Checkpoint

代码如下

 step_counter = tf.train.get_or_create_global_step()

 checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer, step_counter=step_counter)

 def saveModelV2(model_dir, checkpoint, modelname='model2'):

     checkpoint_prefix = os.path.join(model_dir, modelname)

     checkpoint.save(checkpoint_prefix)

 def restoreModelV2(model_dir, checkpoint):

     checkpoint.restore(tf.train.latest_checkpoint(model_dir))

五具体代码

代码未严格按照总体流程的步骤，仅供参考，见https://github.com/darkknightzh/trainEagerMnist

其中eagerFlag为使用eager的方式，0为不使用eager（使用静态图），1为使用V1的方式，2为使用V2的方式。当使用静态图时，不要加tfe.enable_eager_execution()，否则会报错。具体可参考代码。

秒客网

（原）tensorflow使用eager在mnist上训练的简单例子

一总体流程

二创建模型

1. 类似pytorch的方式

2. 直接使用tf.keras.Sequential

3. 使用tf.keras.Sequential()及add函数

三使用动态图更新梯度

四保存及载入模型

1. 使用tfe.Saver

2. 使用tf.train.Checkpoint

五具体代码

相关文章

（原）tensorflow使用eager在mnist上训练的简单例子

一 总体流程

二 创建模型

1. 类似pytorch的方式

2. 直接使用tf.keras.Sequential

3. 使用tf.keras.Sequential()及add函数

三 使用动态图更新梯度

四 保存及载入模型

1. 使用tfe.Saver

2. 使用tf.train.Checkpoint

五 具体代码

相关文章

一总体流程

二创建模型

三使用动态图更新梯度

四保存及载入模型

五具体代码