将以下代码转化为Paddle框架写法class BasicLayer(nn.Module): def __init__(self, net_depth, dim, depth, kernel_size=3, conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=nn.Sigmoid): super().__init__() self.dim = dim self.depth = depth # build blocks self.blocks = nn.ModuleList([ BasicBlock(net_depth, dim, kernel_size, conv_layer, norm_layer, gate_act) for i in range(depth)]) def forward(self, x): for blk in self.blocks: x = blk(x) return x
时间: 2023-03-10 20:43:58 浏览: 89
class BasicLayer(paddle.nn.Layer):
def __init__(self, net_depth, dim, depth, kernel_size=3, conv_layer=paddle.nn.Conv2D, norm_layer=paddle.nn.BatchNorm, gate_act=paddle.nn.Sigmoid):
super(BasicLayer, self).__init__()
self.dim = dim
self.depth = depth
相关问题
class BasicBlock(nn.Module): def __init__(self, net_depth, dim, kernel_size=3, conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=nn.Sigmoid): super().__init__() self.norm = norm_layer(dim) self.conv = conv_layer(net_depth, dim, kernel_size, gate_act) def forward(self, x): identity = x x = self.norm(x) x = self.conv(x) x = identity + x return x转化为Paddle框架写法
class BasicBlock(fluid.dygraph.Layer):
def __init__(self, net_depth, dim, kernel_size=3, conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=fluid.dygraph.nn.functional.sigmoid):
super(BasicBlock, self).__init__()
self.norm = norm_layer(dim)
self.conv = conv_layer(net_depth, dim, kernel_size, gate_act)
def forward(self, x):
identity = x
x = self.norm(x)
x = self.conv(x)
x = identity + x
return x
import paddle.nn as nn import paddle # 定义RNN网络 class MyRNN(paddle.nn.Layer): def __init__(self): #定义RNN网络 def forward(self, x): #设置网络架构补充代码
以下是一个简单的RNN网络的完整代码示例:
```
import paddle.nn as nn
import paddle
class MyRNN(paddle.nn.Layer):
def __init__(self, input_size, hidden_size, num_layers, output_size):
super(MyRNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
# 定义RNN网络
self.rnn = nn.RNN(input_size=input_size, hidden_size=hidden_size,
num_layers=num_layers, nonlinearity='tanh', time_major=True)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
# 设置网络架构
h0 = paddle.zeros([self.num_layers, x.shape[1], self.hidden_size])
out, hn = self.rnn(x, h0)
out = self.fc(out[-1])
return out
```
在这个例子中,我们定义了一个RNN网络,包含一个RNN层和一个全连接层。`__init__`函数中,我们传入了输入大小`input_size`、隐藏层大小`hidden_size`、层数`num_layers`和输出大小`output_size`,并用这些参数初始化了RNN和全连接层。在`forward`函数中,我们首先定义了初始的隐藏状态`h0`,然后将输入`x`和`h0`传入RNN层,得到输出`out`和最终的隐藏状态`hn`。接着,我们取`out`中的最后一个时间步的输出,并将其传入全连接层得到输出结果。