这是创建keras模型的两种方法，但是两种方法的摘要结果中的output shapes不同。显然，前者可以打印更多信息，并且可以更轻松地检查网络的正确性。
import tensorflow as tf
from tensorflow.keras import Input, layers, Model
class subclass(Model):
def __init__(self):
super(subclass, self).__init__()
self.conv = layers.Conv2D(28, 3, strides=1)
def call(self, x):
return self.conv(x)
def func_api():
x = Input(shape=(24, 24, 3))
y = layers.Conv2D(28, 3, strides=1)(x)
return Model(inputs=[x], outputs=[y])
if __name__ == '__main__':
func = func_api()
func.summary()
sub = subclass()
sub.build(input_shape=(None, 24, 24, 3))
sub.summary()
输出：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 24, 24, 3) 0
_________________________________________________________________
conv2d (Conv2D) (None, 22, 22, 28) 784
=================================================================
Total params: 784
Trainable params: 784
Non-trainable params: 0
_________________________________________________________________
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) multiple 784
=================================================================
Total params: 784
Trainable params: 784
Non-trainable params: 0
_________________________________________________________________
那么，我应该如何使用子类方法在summary（）处获取output shape？
答案 0 :(得分：4)
我猜关键点是类Network中的_init_graph_network方法，它是Model的父类。如果在调用_init_graph_network方法时指定了inputs和outputs参数，则会调用__init__。
因此，有两种可能的方法：
_init_graph_network方法来构建模型图。 和这两种方法都需要输入层和输出（从self.call中需要）。
现在调用summary将给出确切的输出形状。但是它将显示Input层，这不是子类化Model的一部分。
from tensorflow import keras
from tensorflow.keras import layers as klayers
class MLP(keras.Model):
def __init__(self, input_shape=(32), **kwargs):
super(MLP, self).__init__(**kwargs)
# Add input layer
self.input_layer = klayers.Input(input_shape)
self.dense_1 = klayers.Dense(64, activation='relu')
self.dense_2 = klayers.Dense(10)
# Get output layer with `call` method
self.out = self.call(self.input_layer)
# Reinitial
super(MLP, self).__init__(
inputs=self.input_layer,
outputs=self.out,
**kwargs)
def build(self):
# Initialize the graph
self._is_graph_network = True
self._init_graph_network(
inputs=self.input_layer,
outputs=self.out
)
def call(self, inputs):
x = self.dense_1(inputs)
return self.dense_2(x)
if __name__ == '__main__':
mlp = MLP(16)
mlp.summary()
输出将是：
Model: "mlp_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 16)] 0
_________________________________________________________________
dense (Dense) (None, 64) 1088
_________________________________________________________________
dense_1 (Dense) (None, 10) 650
=================================================================
Total params: 1,738
Trainable params: 1,738
Non-trainable params: 0
_________________________________________________________________
答案 1 :(得分：1)
我已经使用这种方法解决了这个问题，我不知道是否有更简单的方法。
class subclass(Model):
def __init__(self):
...
def call(self, x):
...
def model():
x = Input(shape=(24, 24, 3))
return Model(inputs=[x], outputs=self.call(x))
if __name__ == '__main__':
sub = subclass()
sub.model().summary()
答案 2 :(得分：1)
我解决问题的方式与Elazar提到的方式非常相似。覆盖类subclass中的函数summary（）。然后，您可以在使用模型子类化时直接调用summary（）：
class subclass(Model):
def __init__(self):
...
def call(self, x):
...
def summary(self):
x = Input(shape=(24, 24, 3))
model = Model(inputs=[x], outputs=self.call(x))
return model.summary()
if __name__ == '__main__':
sub = subclass()
sub.summary()
答案 3 :(得分：1)
我分析了 Adi Shumely 的回答：
所以我提出并提出了这个解决方案，它不需要对模型进行任何修改，只需要改进模型，因为它是在调用 summary() 方法之前通过添加对调用的调用来构建的() 模型的方法与输入张量。 我尝试了我自己的模型以及此提要中提供的三个模型，并且到目前为止都有效。
来自此提要的第一篇文章：
import tensorflow as tf
from tensorflow.keras import Input, layers, Model
class subclass(Model):
def __init__(self):
super(subclass, self).__init__()
self.conv = layers.Conv2D(28, 3, strides=1)
def call(self, x):
return self.conv(x)
if __name__ == '__main__':
sub = subclass()
sub.build(input_shape=(None, 24, 24, 3))
# Adding this call to the call() method solves it all
sub.call(Input(shape=(24, 24, 3)))
# And the summary() outputs all the information
sub.summary()
来自提要的第二个帖子
from tensorflow import keras
from tensorflow.keras import layers as klayers
class MLP(keras.Model):
def __init__(self, **kwargs):
super(MLP, self).__init__(**kwargs)
self.dense_1 = klayers.Dense(64, activation='relu')
self.dense_2 = klayers.Dense(10)
def call(self, inputs):
x = self.dense_1(inputs)
return self.dense_2(x)
if __name__ == '__main__':
mlp = MLP()
mlp.build(input_shape=(None, 16))
mlp.call(klayers.Input(shape=(16)))
mlp.summary()
从提要的最后一个帖子开始
import tensorflow as tf
class MyModel(tf.keras.Model):
def __init__(self, **kwargs):
super(MyModel, self).__init__(**kwargs)
self.dense10 = tf.keras.layers.Dense(10, activation=tf.keras.activations.softmax)
self.dense20 = tf.keras.layers.Dense(20, activation=tf.keras.activations.softmax)
def call(self, inputs):
x = self.dense10(inputs)
y_pred = self.dense20(x)
return y_pred
model = MyModel()
model.build(input_shape = (None, 32, 32, 1))
model.call(tf.keras.layers.Input(shape = (32, 32, 1)))
model.summary()
答案 4 :(得分：1)
我已经使用这种方法解决了在 tensorflow 2.1 和 tensorflow 2.4.1 上测试过的这个问题。使用 model.inputs_layer
class Logistic(tf.keras.models.Model):
def __init__(self, hidden_size = 5, output_size=1, dynamic=False, **kwargs):
'''
name: String name of the model.
dynamic: (Subclassed models only) Set this to `True` if your model should
only be run eagerly, and should not be used to generate a static
computation graph. This attribute is automatically set for Functional API
models.
trainable: Boolean, whether the model's variables should be trainable.
dtype: (Subclassed models only) Default dtype of the model's weights (
default of `None` means use the type of the first input). This attribute
has no effect on Functional API models, which do not have weights of their
own.
'''
super().__init__(dynamic=dynamic, **kwargs)
self.inputs_ = tf.keras.Input(shape=(2,), name="hello")
self._set_input_layer(self.inputs_)
self.hidden_size = hidden_size
self.dense = layers.Dense(hidden_size, name = "linear")
self.outlayer = layers.Dense(output_size,
activation = 'sigmoid', name = "out_layer")
self.build()
def _set_input_layer(self, inputs):
"""add inputLayer to model and display InputLayers in model.summary()
Args:
inputs ([dict]): the result from `tf.keras.Input`
"""
if isinstance(inputs, dict):
self.inputs_layer = {n: tf.keras.layers.InputLayer(input_tensor=i, name=n)
for n, i in inputs.items()}
elif isinstance(inputs, (list, tuple)):
self.inputs_layer = [tf.keras.layers.InputLayer(input_tensor=i, name=i.name)
for i in inputs]
elif tf.is_tensor(inputs):
self.inputs_layer = tf.keras.layers.InputLayer(input_tensor=inputs, name=inputs.name)
def build(self):
super(Logistic, self).build(self.inputs_.shape if tf.is_tensor(self.inputs_) else self.inputs_)
_ = self.call(self.inputs_)
def call(self, X):
X = self.dense(X)
Y = self.outlayer(X)
return Y
model = Logistic()
model.summary()
Model: "logistic"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
hello:0 (InputLayer) [(None, 2)] 0
_________________________________________________________________
linear (Dense) (None, 5) 15
_________________________________________________________________
out_layer (Dense) (None, 1) 6
=================================================================
Total params: 21
Trainable params: 21
Non-trainable params: 0
_________________________________________________________________
答案 5 :(得分：0)
遇到相同的问题-只需3个步骤即可解决该问题：
class MyModel(tf.keras.Model):
def __init__(self,input_shape=(32,32,1), **kwargs):
super(MyModel, self).__init__(**kwargs)
self.input_layer = tf.keras.layers.Input(input_shape)
self.dense10 = tf.keras.layers.Dense(10, activation=tf.keras.activations.softmax)
self.dense20 = tf.keras.layers.Dense(20, activation=tf.keras.activations.softmax)
self.out = self.call(self.input_layer)
def call(self, inputs):
x = self.dense10(inputs)
y_pred = self.dense20(x)
return y_pred
model = MyModel()
model(x_test[:99])
print('x_test[:99].shape:',x_test[:10].shape)
model.summary()
输出：
x_test[:99].shape: (99, 32, 32, 1)
Model: "my_model_32"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_79 (Dense) (None, 32, 32, 10) 20
_________________________________________________________________
dense_80 (Dense) (None, 32, 32, 20) 220
=================================================================
Total params: 240
Trainable params: 240
Non-trainable params: 0