keras模型输出形状中的方括号
我最近在查看模型摘要时遇到了这种情况 我想知道,keras模型输出形状中的方括号,keras,Keras,我最近在查看模型摘要时遇到了这种情况 我想知道,[(无,16)]和(无,16)之间有什么区别?为什么输入层有这样的输入形状 来源:问题在于如何定义输入形状。python中的单个元素元组实际上是一个标量值,如下所示- input_shape0 = 32 input_shape1 = (32) input_shape2 = (32,) print(input_shape0, input_shape1, input_shape2) 由于Keras函数APIInput需要一个作为元组的输入形状,因此
[(无,16)]
和(无,16)
之间有什么区别?为什么输入层有这样的输入形状
来源:问题在于如何定义输入形状。python中的单个元素元组实际上是一个标量值,如下所示-
input_shape0 = 32
input_shape1 = (32)
input_shape2 = (32,)
print(input_shape0, input_shape1, input_shape2)
由于Keras函数APIInput
需要一个作为元组的输入形状,因此必须以(n,)
的形式传递它,而不是n
奇怪的是,你会得到一个方括号,因为当我运行完全相同的代码时,我会得到一个错误
TypeError Traceback (most recent call last)
<ipython-input-828-b564be68c80d> in <module>
33
34 if __name__ == '__main__':
---> 35 mlp = MLP((16))
36 mlp.summary()
<ipython-input-828-b564be68c80d> in __init__(self, input_shape, **kwargs)
6 super(MLP, self).__init__(**kwargs)
7 # Add input layer
----> 8 self.input_layer = klayers.Input(input_shape)
9
10 self.dense_1 = klayers.Dense(64, activation='relu')
~/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/input_layer.py in Input(shape, batch_size, name, dtype, sparse, tensor, **kwargs)
229 dtype=dtype,
230 sparse=sparse,
--> 231 input_tensor=tensor)
232 # Return tensor including `_keras_history`.
233 # Note that in this case train_output and test_output are the same pointer.
~/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/input_layer.py in __init__(self, input_shape, batch_size, dtype, input_tensor, sparse, name, **kwargs)
89 if input_tensor is None:
90 if input_shape is not None:
---> 91 batch_input_shape = (batch_size,) + tuple(input_shape)
92 else:
93 batch_input_shape = None
TypeError: 'int' object is not iterable
你能不能也为你使用的模型定义添加代码?这是这篇文章的最后一条评论。对于TF2.3和2.4,我仍然得到上面阿克什·塞格尔例子的方括号:“[(无,16)]”:(
TypeError Traceback (most recent call last)
<ipython-input-828-b564be68c80d> in <module>
33
34 if __name__ == '__main__':
---> 35 mlp = MLP((16))
36 mlp.summary()
<ipython-input-828-b564be68c80d> in __init__(self, input_shape, **kwargs)
6 super(MLP, self).__init__(**kwargs)
7 # Add input layer
----> 8 self.input_layer = klayers.Input(input_shape)
9
10 self.dense_1 = klayers.Dense(64, activation='relu')
~/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/input_layer.py in Input(shape, batch_size, name, dtype, sparse, tensor, **kwargs)
229 dtype=dtype,
230 sparse=sparse,
--> 231 input_tensor=tensor)
232 # Return tensor including `_keras_history`.
233 # Note that in this case train_output and test_output are the same pointer.
~/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/input_layer.py in __init__(self, input_shape, batch_size, dtype, input_tensor, sparse, name, **kwargs)
89 if input_tensor is None:
90 if input_shape is not None:
---> 91 batch_input_shape = (batch_size,) + tuple(input_shape)
92 else:
93 batch_input_shape = None
TypeError: 'int' object is not iterable
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()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_19 (InputLayer) (None, 16) 0
_________________________________________________________________
dense_8 (Dense) (None, 64) 1088
_________________________________________________________________
dense_9 (Dense) (None, 10) 650
=================================================================
Total params: 1,738
Trainable params: 1,738
Non-trainable params: 0
_________________________________________________________________