max_pool_tensor = torch.nn.MaxPool2d((2, 2), 2) max_pool_numpy = MaxPooling2D((2, 2), stride=2) out_numpy = max_pool_numpy(x_numpy[0, 0]) out_tensor = max_pool_tensor(x_tensor) d_loss_numpy = np.random.random(out_tensor.shape) d_loss_tensor = torch.tensor(d_loss_num...
池化操作尺寸nn.MaxPool2d()函数详解 MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False) nn.MaxPool2d(2, stride=2) 作用:对邻域内特征点取最大 减小卷积层参数误差造成估计均值的偏移的误差,更多的保留纹理信息。 参数解释 kernel_size(int or tuple) -...
import torch from torch import nn def pool2d(X, pool_size, mode='max'): # 默认使用最大池化,平均池化将Mode设置为avg X = X.float() p_h, p_w = pool_size Y = torch.zeros(X.shape[0] - p_h + 1, X.shape[1] - p_w + 1) for i in range(Y.shape[0]): for j in range(...
参考Python和PyTorch对比实现池化层MaxPool函数及反向传播 maxpooling importnumpyasnpimporttorchclassMaxPooling2D:def__init__(self,kernel_size=(2,2),stride=2):self.kernel_size=kernel_size self.w_height=kernel_size[0]self.w_width=kernel_size[1]self.stride=stride self.x=Noneself.in_height=Nonese...
self.c2 = Conv2D(filters=16, kernel_size=(5, 5), activation='sigmoid') self.p2 = MaxPool2D(pool_size=(2, 2), strides=2) self.flatten = Flatten() self.f1 = Dense(120, activation='sigmoid') self.f2 = Dense(84, activation='sigmoid') ...
# Max Pooling (down-sampling)（池化层）conv1 = maxpool2d(conv1, k=2)print(conv1.shape)# Convolution Layer（卷积层）conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])print(conv2.shape)# Max Pooling (down-sampling) （池化层）conv2 = maxpool2d(conv2, k=2...
model.add(MaxPool2D(pool_size=2,strides=2))#继续构建卷积层和池化层,区别是卷积核数量为64。 model.add(Conv2D(filters=64,kernel_size=3,padding='same',activation='relu'))model.add(Conv2D(filters=64,kernel_size=3,padding='same',activation='relu'))model.add(MaxPool2D(pool_size=2,strides=...
x = self.pool(F.relu(self.conv1(x))) #第一个卷积层首先要经过relu做激活,然后使用前面定义好的nn.MaxPool2d(2, 2)方法做池化 x = self.pool(F.relu(self.conv2(x))) #第二个卷积层也要经过relu做激活,然后使用前面定义好的nn.MaxPool2d(2, 2)方法做池化 ...
model.add(Conv2D(32, kernel_size=(3, 3), activation=’relu’, input_shape=(28, 28, 1)))model.add(Conv2D(64, (3, 3), activation=’relu’))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Flatten())model.add(Dense(128, activation=’relu’))model....