获得某层tensor的输出维度

代码如下所示：

from keras import backend as K@wraps(Conv2D)def my_conv(*args,**kwargs): new_kwargs={'kernel_regularizer':l2(5e-6)} new_kwargs['padding']='valid' #'same' new_kwargs['strides']=(2,2) if kwargs.get('strides')==(2,2) else (1,1) # new_kwargs['kernel_initializer']=keras.initializers.glorot_uniform(seed=0) new_kwargs.update(kwargs) return Conv2D(*args,**new_kwargs)def conv(x,**kwargs): x=my_conv(**kwargs)(x) x=BatchNormalization(axis=-1)(x) x=LeakyReLU(alpha=0.05)(x) return xdef inception_resnet_a(x_input): x_short=x_input s1=conv(x_input,filters=32,kernel_size=(1,1)) s2=conv(x_input,filters=32,kernel_size=(1,1)) s2=conv(s2,filters=32,kernel_size=(3,3),padding='same') s3=conv(x_input,filters=32,kernel_size=(1,1)) s3=conv(s3,filters=48,kernel_size=(3,3),padding='same') s3=conv(s3,filters=64,kernel_size=(3,3),padding='same') x=keras.layers.concatenate([s1,s2,s3]) x=conv(x,filters=384,kernel_size=(1,1)) x=layers.Add()([x_short,x]) x=LeakyReLU(alpha=0.05)(x) print(K.int_shape(x))

使用K.int_shape(tensor_name)即可得到对应tensor的维度

以上这篇keras获得model中某一层的某一个Tensor的输出维度教程就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。