TF2.6:ValueError:无法保存模型，因为尚未设置输入形状

Question

我想在Google Colab中使用迁移学习创建一个自定义模型

import tensorflow as tf
from tensorflow.keras.layers import Conv2D
from tensorflow.python.keras.applications.xception import Xception

class MyModel(tf.keras.Model):

  def __init__(self, input_shape, num_classes=5, dropout_rate=0.5):
    super(MyModel, self).__init__()
    self.weight_dict = {}
    self.weight_dict['backbone'] = Xception(input_shape=input_shape, weights='imagenet', include_top=False)
    self.weight_dict['outputs'] = Conv2D(num_classes, (1, 1), padding="same", activation="softmax")
    self.build((None,) + input_shape)

  def call(self, inputs, training=False):
    self.weight_dict['backbone'].trainable = False
    x = self.weight_dict['backbone'](inputs)
    x = self.weight_dict['outputs'](x)
    return x

model = MyModel(input_shape=(256, 256, 3))
model.save('./saved')

但是，我遇到了以下错误：

ValueError: Model `<__main__.MyModel object at 0x7fc66134bdd0>` cannot be saved because the input shapes have not been set. Usually, input shapes are automatically determined from calling `.fit()` or `.predict()`. To manually set the shapes, call `model.build(input_shape)`.

是的，没有对.fit()或.predict()的调用。但是在类的__init__()方法中有一个对.build的调用。我该怎么办

Answer 1

如果尚未构建该层，compute_output_shape将在该层上调用build。这假设该层稍后将与与提供的输入形状匹配的输入一起使用

工作代码如下所示

import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.applications.xception import Xception

class MyModel(tf.keras.Model):

  def __init__(self, input_shape, num_classes=5, dropout_rate=0.5):
    super(MyModel, self).__init__()
    self.weight_dict = {}
    self.weight_dict['backbone'] = Xception(input_shape=input_shape, weights='imagenet', include_top=False)
    self.weight_dict['outputs'] = Conv2D(num_classes, (1, 1), padding="same", activation="softmax")
    self.build((None,) + input_shape)

  def call(self, inputs, training=False):
    self.weight_dict['backbone'].trainable = False
    x = self.weight_dict['backbone'](inputs)
    x = self.weight_dict['outputs'](x)
    return x

input_shape=(256, 256, 3)
model=MyModel(input_shape)

model.compute_output_shape(input_shape=(None, 256, 256, 3))
model.save('./saved')

输出：

2.6.0

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5
83689472/83683744 [==============================] - 1s 0us/step
INFO:tensorflow:Assets written to: ./saved/assets

有关更多信息，请参阅here