1.CNN
建立了3层(3层=2层+1层全连接层)。分别是conv1、conv2和分类问题中的全连接层线性层out
c
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Sequential( # input shape (1, 28, 28)
nn.Conv2d(
in_channels=1, # input height
out_channels=16, # n_filters
kernel_size=5, # filter size
stride=1, # filter movement/step
padding=2, # if want same width and length of this image after Conv2d, padding=(kernel_size-1)/2 if stride=1
), # output shape (16, 28, 28)
nn.ReLU(), # activation
nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14)
)
self.conv2 = nn.Sequential( # input shape (16, 14, 14)
nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)
nn.ReLU(), # activation
nn.MaxPool2d(2), # output shape (32, 7, 7)
)
self.out = nn.Linear(32 * 7 * 7, 10) # fully connected layer, output 10 classes
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
output = self.out(x)
return output, x # return x for visualization
2.RNN
2.1RNN分类问题代码
设计了rnn层【输入(INPUT_SIZE),隐藏层1层(hidden_size)】和分类问题的全连接线性层out
c
class RNN(nn.Module):
def __init__(self):
super(RNN, self).__init__()
self.rnn = nn.LSTM( # LSTM 效果要比 nn.RNN() 好多了
input_size=28, # 图片每行的数据像素点
hidden_size=64, # rnn hidden unit
num_layers=1, # 有几层 RNN layers
batch_first=True, # input & output 会是以 batch size 为第一维度的特征集 e.g. (batch, time_step, input_size)
)
self.out = nn.Linear(64, 10) # 输出层
def forward(self, x):
# x shape (batch, time_step, input_size)
# r_out shape (batch, time_step, output_size)
# h_n shape (n_layers, batch, hidden_size) LSTM 有两个 hidden states, h_n 是分线, h_c 是主线
# h_c shape (n_layers, batch, hidden_size)
r_out, (h_n, h_c) = self.rnn(x, None) # None 表示 hidden state 会用全0的 state
# 选取最后一个时间点的 r_out 输出
# 这里 r_out[:, -1, :] 的值也是 h_n 的值
out = self.out(r_out[:, -1, :])
return out
rnn = RNN()
print(rnn)
"""
RNN (
(rnn): LSTM(28, 64, batch_first=True)
(out): Linear (64 -> 10)
)
"""
2.2RNN回归问题代码
具体参考:https://mofanpy.com/tutorials/machine-learning/torch/RNN-regression
c
class RNN(nn.Module):
def __init__(self):
super(RNN, self).__init__()
self.rnn = nn.RNN( # 这回一个普通的 RNN 就能胜任
input_size=1,
hidden_size=32, # rnn hidden unit
num_layers=1, # 有几层 RNN layers
batch_first=True, # input & output 会是以 batch size 为第一维度的特征集 e.g. (batch, time_step, input_size)
)
self.out = nn.Linear(32, 1)
def forward(self, x, h_state): # 因为 hidden state 是连续的, 所以我们要一直传递这一个 state
# x (batch, time_step, input_size)
# h_state (n_layers, batch, hidden_size)
# r_out (batch, time_step, output_size)
r_out, h_state = self.rnn(x, h_state) # h_state 也要作为 RNN 的一个输入
outs = [] # 保存所有时间点的预测值
for time_step in range(r_out.size(1)): # 对每一个时间点计算 output
outs.append(self.out(r_out[:, time_step, :]))
return torch.stack(outs, dim=1), h_state
rnn = RNN()
print(rnn)
"""
RNN (
(rnn): RNN(1, 32, batch_first=True)
(out): Linear (32 -> 1)
)
"""