self.conv1 = nn.Conv2d(1, 3, 5) self.conv2 = nn.Conv2d(3, 16, 5) self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) x = F.max_p...
(x): # E: Function is missing a type annotation [no-untyped-def] loss = torch.nn.functional.max_pool2d(x, kernel_size=3, stride=2, padding=1).sum() return torch.autograd.grad(loss, x) y = x.clone() result, = compute_grad(y) compile32, = torch.compile(compute_grad)(x) ...
3 2 pnnx.Input input 0 1 input F.max_pool2d op_0 1 1 input out kernel_size=%kernel_size stride=%stride padding=%padding ceil_mode=%ceil_mode return_indices=False pnnx.Output output 1 0 out )PNNXIR"; } }; REGISTER_GLOBAL_PNNX_NCNN_GRAPH_REWRITER_PASS(F_max_pool2d_1,20) ...
3.1. 一维卷积层:torch.nn.Conv1d() 3.2. 二维卷积层:torch.nn.Conv2d() 3.3. 三维卷积层:torch.nn.Conv3d() 四、池化层 4.1. 1维最大池化:torch.nn.MaxPool1d() 4.2. 2维最大池化:torch.nn.MaxPool2d() 4.3. 3维最大池化:torch.nn.MaxPool3d() 五、非线性激活层 5.1. torch.nn.ReLU() 5....
MaxPool2d(9, 1, padding=4) self.maxpool3 = nn.MaxPool2d(13, 1, padding=6) def forward(self, x): o1 = self.maxpool1(x) o2 = self.maxpool2(x) o3 = self.maxpool3(x) return torch.cat([x, o1, o2, o3], dim=1) class SPPF(nn.Module): def __init__(self): super()...
conv2d_relu1 = tf.nn.relu(h_conv1 + b_conv1) pooling1 = max_pool_2x2(conv2d_relu1) #第二层卷积,提取64种特征 W_conv2 = tf.Variable(tf.random_uniform([5, 5, 32, 64])) b_conv2 = tf.constant(0.1, shape=[64]) h_conv2 = conv2d(pooling1, W_conv2) ...
Add to Plan Share via Facebookx.comLinkedInEmail Error: HTTPSConnectionPool(host='login.microsoftonline.com', port=443): Max retries exceeded with url:Max retries exceeded with url: /b458eb0a-178f-4197-8da4-514c0fe6f17b/v2.0/.well-known/openid-configuration ...
maxStackSize Int32 The maximum stack size, in bytes, to be used by the thread, or 0 to use the default maximum stack size specified in the header for the executable. Important For partially trusted code,maxStackSizeis ignored if it is greater than the default stack size. No exception is...
单项选择题程序语句 max_pool(conv2, [1, 3, 3, 1], strides=[1, 1, 1, 1], padding="SAME")那么这个语句中,体现的池化窗口尺寸为多少() A.1*1B.3*3C.1*3D.3*1 点击查看答案 您可能感兴趣的试卷 你可能感兴趣的试题 1.单项选择题函数tf.nn.relu的作用是() A.被卷积数据B.卷积核C....
maxpool = nn.MaxPool2d(kernel_size=2, stride=2) #对输入张量进行池化操作 output_tensor = maxpool(input_tensor) ``` 在上面的代码中,首先定义了一个3通道、高32、宽32的输入张量`input_tensor`,然后使用`nn.MaxPool2d`函数定义了一个2x2的池化核和2x2的步长。最后,使用`maxpool`对`input_tensor`...