AttributeError：当模型为 时，'Model' 对象没有属性 'trainable_variables'

我刚刚开始学习 Tensorflow (2.1.0)、Keras (2.3.1) 和 Python 3.7.7。

顺便说一句，我在 Windows 7 64 位的 Anaconda 环境中运行所有代码。我还在 Linux 上尝试过 Anaconda 环境，但遇到了同样的错误。

我正在关注这个 Tensorflow 教程：“定制培训：演练 https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough".

一切都很好，但是当我输入这段代码时：

def grad(model, inputs, targets):
  with tf.GradientTape() as tape:
    loss_value = loss(model, inputs, targets, training=True)
  return loss_value, tape.gradient(loss_value, model.trainable_variables)

我收到错误：

“模型”的实例没有“trainable_variables”成员

这是我的模型，包含所有导入内容：

import keras
from keras.models import Input, Model
from keras.layers import Dense, Conv2D, Conv2DTranspose, UpSampling2D, MaxPooling2D, Flatten, ZeroPadding2D
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam
import numpy as np
import tensorflow as tf

def vgg16_encoder_decoder(input_size = (200,200,1)):
    #################################
    # Encoder
    #################################
    inputs = Input(input_size, name = 'input')

    conv1 = Conv2D(64, (3, 3), activation = 'relu', padding = 'same', name ='conv1_1')(inputs)
    conv1 = Conv2D(64, (3, 3), activation = 'relu', padding = 'same', name ='conv1_2')(conv1)
    pool1 = MaxPooling2D(pool_size = (2,2), strides = (2,2), name = 'pool_1')(conv1)

    conv2 = Conv2D(128, (3, 3), activation = 'relu', padding = 'same', name ='conv2_1')(pool1)
    conv2 = Conv2D(128, (3, 3), activation = 'relu', padding = 'same', name ='conv2_2')(conv2)
    pool2 = MaxPooling2D(pool_size = (2,2), strides = (2,2), name = 'pool_2')(conv2)

    conv3 = Conv2D(256, (3, 3), activation = 'relu', padding = 'same', name ='conv3_1')(pool2)
    conv3 = Conv2D(256, (3, 3), activation = 'relu', padding = 'same', name ='conv3_2')(conv3)
    conv3 = Conv2D(256, (3, 3), activation = 'relu', padding = 'same', name ='conv3_3')(conv3)
    pool3 = MaxPooling2D(pool_size = (2,2), strides = (2,2), name = 'pool_3')(conv3)

    conv4 = Conv2D(512, (3, 3), activation = 'relu', padding = 'same', name ='conv4_1')(pool3)
    conv4 = Conv2D(512, (3, 3), activation = 'relu', padding = 'same', name ='conv4_2')(conv4)
    conv4 = Conv2D(512, (3, 3), activation = 'relu', padding = 'same', name ='conv4_3')(conv4)
    pool4 = MaxPooling2D(pool_size = (2,2), strides = (2,2), name = 'pool_4')(conv4)

    conv5 = Conv2D(512, (3, 3), activation = 'relu', padding = 'same', name ='conv5_1')(pool4)
    conv5 = Conv2D(512, (3, 3), activation = 'relu', padding = 'same', name ='conv5_2')(conv5)
    conv5 = Conv2D(512, (3, 3), activation = 'relu', padding = 'same', name ='conv5_3')(conv5)
    pool5 = MaxPooling2D(pool_size = (2,2), strides = (2,2), name = 'pool_5')(conv5)

    #################################
    # Decoder
    #################################
    #conv1 = Conv2DTranspose(512, (2, 2), strides = 2, name = 'conv1')(pool5)

    upsp1 = UpSampling2D(size = (2,2), name = 'upsp1')(pool5)
    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_1')(upsp1)
    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_2')(conv6)
    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_3')(conv6)

    upsp2 = UpSampling2D(size = (2,2), name = 'upsp2')(conv6)
    conv7 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv7_1')(upsp2)
    conv7 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv7_2')(conv7)
    conv7 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv7_3')(conv7)
    zero1 = ZeroPadding2D(padding =  ((1, 0), (1, 0)), data_format = 'channels_last', name='zero1')(conv7)

    upsp3 = UpSampling2D(size = (2,2), name = 'upsp3')(zero1)
    conv8 = Conv2D(256, 3, activation = 'relu', padding = 'same', name = 'conv8_1')(upsp3)
    conv8 = Conv2D(256, 3, activation = 'relu', padding = 'same', name = 'conv8_2')(conv8)
    conv8 = Conv2D(256, 3, activation = 'relu', padding = 'same', name = 'conv8_3')(conv8)

    upsp4 = UpSampling2D(size = (2,2), name = 'upsp4')(conv8)
    conv9 = Conv2D(128, 3, activation = 'relu', padding = 'same', name = 'conv9_1')(upsp4)
    conv9 = Conv2D(128, 3, activation = 'relu', padding = 'same', name = 'conv9_2')(conv9)

    upsp5 = UpSampling2D(size = (2,2), name = 'upsp5')(conv9)
    conv10 = Conv2D(64, 3, activation = 'relu', padding = 'same', name = 'conv10_1')(upsp5)
    conv10 = Conv2D(64, 3, activation = 'relu', padding = 'same', name = 'conv10_2')(conv10)

    conv11 = Conv2D(1, 3, activation = 'relu', padding = 'same', name = 'conv11')(conv10)

    model = Model(inputs = inputs, outputs = conv11, name = 'vgg-16_encoder_decoder')

    return model

我在中找到了该属性的任何参考张量流 Keras 模型 https://www.tensorflow.org/api_docs/python/tf/keras/Model文档。

On "将 TensorFlow 1 代码迁移到 TensorFlow 2 - 2. 使代码成为 2.0 原生 https://www.tensorflow.org/guide/migrate#2_use_python_objects_to_track_variables_and_losses", say:

如果您需要聚合变量列表（例如 tf.Graph.get_collection(tf.GraphKeys.VARIABLES))，使用 .variables Layer 和 Model 对象的 .trainable_variables 属性。

Tensorflow教程中的网络》定制培训：演练 https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough" is:

model = tf.keras.Sequential([
  tf.keras.layers.Dense(10, activation=tf.nn.relu, input_shape=(4,)),  # input shape required
  tf.keras.layers.Dense(10, activation=tf.nn.relu),
  tf.keras.layers.Dense(3)
])

当我做：

print(type(model))

I get:

<class 'tensorflow.python.keras.engine.sequential.Sequential'>

但如果我打印我的网络类型，vgg16_encoder_decoder, I get:

<class 'keras.engine.training.Model'>

So, 问题是网络的类型。上面的类我还没说呢，'keras.engine.training.Model'， 前。

我怎样才能解决这个问题让我使用该属性trainable_variables?

问题是你正在使用keras图书馆而不是tensorflow.keras。 使用 TensorFlow 时，强烈建议使用它自己的 keras 实现。

这段代码应该可以工作

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Conv2D, Conv2DTranspose, UpSampling2D, MaxPooling2D, Flatten, ZeroPadding2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam
import numpy as np


def vgg16_encoder_decoder(input_size = (200,200,1)):
    # Your code here (no change needed)


model = vgg16_encoder_decoder()
model.trainable_variables