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keras 获取某层的输入/输出 tensor 尺寸操作

时间:2020-10-03 10:16:54 | 栏目:Python代码 | 点击:

获取单输入尺寸,该层只被使用了一次。

import keras
from keras.layers import Input, LSTM, Dense, Conv2D
from keras.models import Model
a = Input(shape=(32, 32, 3))
b = Input(shape=(64, 64, 3))
 
conv = Conv2D(16, (3, 3), padding='same')
conved_a = conv(a)
 
# 到目前为止只有一个输入,以下可行:
assert conv.input_shape == (None, 32, 32, 3)

如果该层被使用了两次

import keras
from keras.layers import Input, LSTM, Dense, Conv2D
from keras.models import Model
a = Input(shape=(32, 32, 3))
b = Input(shape=(64, 64, 3))
 
conv = Conv2D(16, (3, 3), padding='same')
conved_a = conv(a)
 
# 到目前为止只有一个输入,以下可行:
assert conv.input_shape == (None, 32, 32, 3)
 
conved_b = conv(b)
# 现在 `.input_shape` 属性不可行,但是这样可以:
assert conv.get_input_shape_at(0) == (None, 32, 32, 3)
assert conv.get_input_shape_at(1) == (None, 64, 64, 3)

如果是输出,只需要改成output就好:

import keras
from keras.layers import Input, LSTM, Dense, Conv2D
from keras.models import Model
a = Input(shape=(32, 32, 3))
b = Input(shape=(64, 64, 3))
 
conv = Conv2D(16, (3, 3), padding='same')
conved_a = conv(a)
 
# 到目前为止只有一个输入,以下可行:
assert conv.input_shape == (None, 32, 32, 3)
 
conved_b = conv(b)
# 就改了output,当然尺寸我也改了
assert conv.get_output_shape_at(0) == (None, 32, 32, 16)
assert conv.get_output_shape_at(1) == (None, 64, 64, 16)

补充知识:keras中获取shape的正确方法

在keras的网络中,如果用layer_name.shape的方式获取shape信息将会返还tensorflow.python.framework.tensor_shape.TensorShape其中包含的是tensorflow.python.framework.tensor_shape.Dimension

正确的方式是使用

import keras.backend as K
K.int_shape(laye_name)

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