获得某层tensor的输出维度

代码如下：

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 x

def 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