# Performing the average pooling on CPU output_cpu = torch.nn.AvgPool2d(3, stride=2)(input_tensor) # Performing the average pooling on GPU output_gpu = torch.nn.AvgPool2d(3, stride=2)(input_tensor.cuda()) output_cpu, output_gpu...
具体如下: AdaptiveAvgPool2d CLASStorch.nn.AdaptiveAvgPool2d(output_size)[SOURCE] Applies a 2D adaptive average pooling over an input signal composed of several input planes. The output is of size H x W, for any input size. The number of output features is equal to the number of input p...
layers.Conv2D(16, (3, 3), activation='relu', input_shape=(img_height, img_width, 3)), # 卷积层1,卷积核3*3 layers.AveragePooling2D((2, 2)), # 池化层1,2*2采样 layers.Conv2D(32, (3, 3), activation='relu'), # 卷积层2,卷积核3*3 layers.AveragePooling2D((2, 2)), # 池...
classtorch.nn.AdaptiveAvgPool2d(output_size)[source] Applies a 2D adaptive average pooling over an input signal composed of several input planes.The output is of size H x W, for any input size. The number of output features is equal to the number of input planes. Parameters output_size–...
CLASStorch.nn.AdaptiveAvgPool2d(output_size)[SOURCE] Applies a 2D adaptive average pooling over an input signal composed of several input planes. The output is of size H x W, for any input size. The number of output features is equal to the number of input planes. ...
具体如下: AdaptiveAvgPool2d CLASStorch.nn.AdaptiveAvgPool2d(output_size)[SOURCE] Applies a 2D adaptive average pooling over an input signal composed of several input planes. The output is of size H x W, for any input size. The number of output features is equal to the nu...
Concatenatefromtensorflow.kerasimport*fromtensorflow.kerasimportregularizers#正则化fromtensorflow.keras.optimizersimportRMSprop#优化选择器fromtensorflow.keras.layersimportAveragePooling2Dfromtensorflow.keras.datasetsimportmnistimportmatplotlib.pyplot as pltimportnumpy as npfromtensorflow.python.keras.utilsimportnp_utils...
Applies a 2D adaptive average pooling over an input signal composed of several input planes.The output is of size H x W, for any input size. The number of output features is equal to the number of input planes. Parameters output_size– the target output size of the image of the form ...
我们将使用 Pytorch 预定义好的 Conv2d 类作为我们的卷积层。我们定义一个三层的卷积神经网络。每一层进行一次 ReLU操作。最后我们做一个平均汇聚层(average pooling)。 class Mnist_CNN(nn.Module): def __init__(self): super().__init__()
class torch.nn.AvgPool2d(kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True)对信号的输入通道,提供2维的平均池化(average pooling ) 输入信号的大小(N,C,H,W),输出大小(N,C,H_out,W_out)和池化窗口大小(kH,kW)的关系是: $$ out(N_i,Cj,h,w)=1/(kHkW)\sum^...