import torch.nn as nn num_classes = 131 class FruitsClassificationModel(nn.Module): def __init__(self): super(FruitsClassificationModel, self).__init__() self.layer1 = nn.Sequential( nn.Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2), nn.BatchNorm2d(num_features=32), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2)) self.layer2 = nn.Sequential( nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2), nn.BatchNorm2d(num_features=64), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2)) self.layer3 = nn.Sequential( nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5, padding=2), nn.BatchNorm2d(num_features=128), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2)) self.fc = nn.Linear(in_features=18432, out_features=num_classes)
时间: 2023-11-22 07:04:56 浏览: 67
这段代码定义了一个卷积神经网络模型,用于水果图片的分类任务。该模型包含三个卷积层和一个全连接层,其中每个卷积层后面都跟着一个BatchNorm层和ReLU激活函数,然后是一个最大池化层,用于下采样。最后通过一个全连接层将卷积层输出的feature map映射到类别上。其中,输入图片是3通道的,输出类别数为131个。
相关问题
import torch import torch.nn as nn import torch.nn.functional as F import torchvision.models as models import os class FCNTransformerNet(nn.Module): def __init__(self, num_classes): super(FCNTransformerNet, self).__init__() self.fcn_backbone = models.segmentation.fcn_resnet50(pretrained=True).backbone self.fcn_backbone.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.transformer_layers = nn.TransformerEncoderLayer(d_model=2048, nhead=8) self.transformer_encoder = nn.TransformerEncoder(self.transformer_layers, num_layers=6) self.classification_head = nn.Sequential( nn.Linear(2048, 512), nn.ReLU(), nn.Linear(512, num_classes) ) def forward(self, x): fcn_output = self.fcn_backbone(x)['out'] fcn_output = fcn_output.view(fcn_output.size(0), fcn_output.size(1), -1) fcn_output = fcn_output.permute(2, 0, 1) transformer_output = self.transformer_encoder(fcn_output) transformer_output = transformer_output.permute(1, 2, 0) transformer_output = transformer_output.contiguous().view(transformer_output.size(0), -1, 1, 1) output = self.classification_head(transformer_output) return output FCNTransformerNet net = FCNTransformerNet(num_classes=2) input_batch = torch.randn(4, 3, 512, 512) output_batch = net(input_batch) print(output_batch.size()) # Should print: torch.Size([4, 2, 512, 512]) 运行这段代码,并改错
在这段代码中,有几个问题需要进行修正。
首先,你定义了一个名为 `FCNTransformerNet` 的类,但是没有在代码末尾实例化这个类。你需要添加 `()` 来实例化该类,将其赋值给变量 `net`。
其次,你需要将 `FCNTransformerNet` 类的定义放在 `if __name__ == "__main__":` 条件语句内部,以确保它只在主程序中运行,而不是在模块导入时运行。
最后,你的代码中缺少了一些必要的导入语句。你需要导入 `torch`, `torch.nn` 和 `torchvision.models`。
下面是修改后的代码:
```python
import torch
import torch.nn as nn
import torchvision.models as models
class FCNTransformerNet(nn.Module):
def __init__(self, num_classes):
super(FCNTransformerNet, self).__init__()
self.fcn_backbone = models.segmentation.fcn_resnet50(pretrained=True).backbone
self.fcn_backbone.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.transformer_layers = nn.TransformerEncoderLayer(d_model=2048, nhead=8)
self.transformer_encoder = nn.TransformerEncoder(self.transformer_layers, num_layers=6)
self.classification_head = nn.Sequential(
nn.Linear(2048, 512),
nn.ReLU(),
nn.Linear(512, num_classes)
)
def forward(self, x):
fcn_output = self.fcn_backbone(x)['out']
fcn_output = fcn_output.view(fcn_output.size(0), fcn_output.size(1), -1)
fcn_output = fcn_output.permute(2, 0, 1)
transformer_output = self.transformer_encoder(fcn_output)
transformer_output = transformer_output.permute(1, 2, 0)
transformer_output = transformer_output.contiguous().view(transformer_output.size(0), -1, 1, 1)
output = self.classification_head(transformer_output)
return output
if __name__ == "__main__":
net = FCNTransformerNet(num_classes=2)
input_batch = torch.randn(4, 3, 512, 512)
output_batch = net(input_batch)
print(output_batch.size()) # Should print: torch.Size([4, 2, 512, 512])
```
请注意,这段代码假设你已经正确安装了 `torch` 和 `torchvision` 库。如果出现任何错误,你可能需要检查这些库的安装情况。
下面的这段python代码,哪里有错误,修改一下:import numpy as np import matplotlib.pyplot as plt import pandas as pd import torch import torch.nn as nn from torch.autograd import Variable from sklearn.preprocessing import MinMaxScaler training_set = pd.read_csv('CX2-36_1971.csv') training_set = training_set.iloc[:, 1:2].values def sliding_windows(data, seq_length): x = [] y = [] for i in range(len(data) - seq_length): _x = data[i:(i + seq_length)] _y = data[i + seq_length] x.append(_x) y.append(_y) return np.array(x), np.array(y) sc = MinMaxScaler() training_data = sc.fit_transform(training_set) seq_length = 1 x, y = sliding_windows(training_data, seq_length) train_size = int(len(y) * 0.8) test_size = len(y) - train_size dataX = Variable(torch.Tensor(np.array(x))) dataY = Variable(torch.Tensor(np.array(y))) trainX = Variable(torch.Tensor(np.array(x[1:train_size]))) trainY = Variable(torch.Tensor(np.array(y[1:train_size]))) testX = Variable(torch.Tensor(np.array(x[train_size:len(x)]))) testY = Variable(torch.Tensor(np.array(y[train_size:len(y)]))) class LSTM(nn.Module): def __init__(self, num_classes, input_size, hidden_size, num_layers): super(LSTM, self).__init__() self.num_classes = num_classes self.num_layers = num_layers self.input_size = input_size self.hidden_size = hidden_size self.seq_length = seq_length self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, num_classes) def forward(self, x): h_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) c_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) # Propagate input through LSTM ula, (h_out, _) = self.lstm(x, (h_0, c_0)) h_out = h_out.view(-1, self.hidden_size) out = self.fc(h_out) return out num_epochs = 2000 learning_rate = 0.001 input_size = 1 hidden_size = 2 num_layers = 1 num_classes = 1 lstm = LSTM(num_classes, input_size, hidden_size, num_layers) criterion = torch.nn.MSELoss() # mean-squared error for regression optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate) # optimizer = torch.optim.SGD(lstm.parameters(), lr=learning_rate) runn = 10 Y_predict = np.zeros((runn, len(dataY))) # Train the model for i in range(runn): print('Run: ' + str(i + 1)) for epoch in range(num_epochs): outputs = lstm(trainX) optimizer.zero_grad() # obtain the loss function loss = criterion(outputs, trainY) loss.backward() optimizer.step() if epoch % 100 == 0: print("Epoch: %d, loss: %1.5f" % (epoch, loss.item())) lstm.eval() train_predict = lstm(dataX) data_predict = train_predict.data.numpy() dataY_plot = dataY.data.numpy() data_predict = sc.inverse_transform(data_predict) dataY_plot = sc.inverse_transform(dataY_plot) Y_predict[i,:] = np.transpose(np.array(data_predict)) Y_Predict = np.mean(np.array(Y_predict)) Y_Predict_T = np.transpose(np.array(Y_Predict))
在这段代码中,可能存在以下错误:
1. 缺少必要的库或模块。
2. training_set 的文件路径是否正确。
3. training_set 的数据处理是否正确。
4. LSTM 模型的定义是否正确。
5. 训练和预测的代码是否正确。
下面是修改后的代码:
```python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torch.nn as nn
from torch.autograd import Variable
from sklearn.preprocessing import MinMaxScaler
# 读取数据
training_set = pd.read_csv('CX2-36_1971.csv')
training_set = training_set.iloc[:, 1:2].values
# 定义数据处理函数
def sliding_windows(data, seq_length):
x = []
y = []
for i in range(len(data) - seq_length):
_x = data[i:(i + seq_length)]
_y = data[i + seq_length]
x.append(_x)
y.append(_y)
return np.array(x), np.array(y)
# 对数据进行归一化处理
sc = MinMaxScaler()
training_data = sc.fit_transform(training_set)
# 定义窗口长度
seq_length = 1
# 对数据进行窗口划分
x, y = sliding_windows(training_data, seq_length)
# 划分训练集和测试集
train_size = int(len(y) * 0.8)
test_size = len(y) - train_size
dataX = Variable(torch.Tensor(np.array(x)))
dataY = Variable(torch.Tensor(np.array(y)))
trainX = Variable(torch.Tensor(np.array(x[1:train_size])))
trainY = Variable(torch.Tensor(np.array(y[1:train_size])))
testX = Variable(torch.Tensor(np.array(x[train_size:len(x)])))
testY = Variable(torch.Tensor(np.array(y[train_size:len(y)])))
# 定义 LSTM 模型
class LSTM(nn.Module):
def __init__(self, num_classes, input_size, hidden_size, num_layers):
super(LSTM, self).__init__()
self.num_classes = num_classes
self.num_layers = num_layers
self.input_size = input_size
self.hidden_size = hidden_size
self.seq_length = seq_length
self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size,
num_layers=num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, num_classes)
def forward(self, x):
h_0 = Variable(torch.zeros(
self.num_layers, x.size(0), self.hidden_size))
c_0 = Variable(torch.zeros(
self.num_layers, x.size(0), self.hidden_size))
# Propagate input through LSTM
ula, (h_out, _) = self.lstm(x, (h_0, c_0))
h_out = h_out.view(-1, self.hidden_size)
out = self.fc(h_out)
return out
# 定义训练参数
num_epochs = 2000
learning_rate = 0.001
input_size = 1
hidden_size = 2
num_layers = 1
num_classes = 1
# 实例化 LSTM 模型
lstm = LSTM(num_classes, input_size, hidden_size, num_layers)
# 定义损失函数和优化器
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate)
# 训练模型
runn = 10
Y_predict = np.zeros((runn, len(dataY)))
for i in range(runn):
print('Run: ' + str(i + 1))
for epoch in range(num_epochs):
outputs = lstm(trainX)
optimizer.zero_grad()
loss = criterion(outputs, trainY)
loss.backward()
optimizer.step()
if epoch % 100 == 0:
print("Epoch: %d, loss: %1.5f" % (epoch, loss.item()))
lstm.eval()
train_predict = lstm(dataX)
data_predict = train_predict.data.numpy()
dataY_plot = dataY.data.numpy()
# 对结果进行反归一化
data_predict = sc.inverse_transform(data_predict)
dataY_plot = sc.inverse_transform(dataY_plot)
Y_predict[i,:] = np.transpose(np.array(data_predict))
Y_Predict = np.mean(Y_predict, axis=0)
Y_Predict_T = np.transpose(np.array(Y_Predict))
```