[人工智能]PyTorch学习记录（五）处理多维特征的输入

第一次接触pytorch，本贴仅记录学习过程，侵删

在B站看完了视频的P7 07.处理多维特征的输入。
附上视频地址：《PyTorch深度学习实践》完结合集_07. 处理多维特征的输入

先记录一些笔记。

Multiple Dimension Logistic Regression Model：

Mini-Batch (N samples)：

import torch


class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear = torch.nn.Linear(8, 1)
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self, x):
        x = self.sigmoid(self.linear(x))
        return x


model = Model()

矩阵是一种空间变换的函数，是从N维空间映射到M维空间的线性变换。

Example:Diabetes Prediction

import numpy as np
import torch

#  1. Prepare Dataset
xy = np.loadtxt('diabetes.csv.gz', delimiter=',', dtype=np.float32)
x_data = torch.from_numpy((xy[:, :-1]))
y_data = torch.from_numpy(xy[:, [-1]])


# 2. Define Model
class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(8, 6)
        self.linear2 = torch.nn.Linear(6, 4)
        self.linear3 = torch.nn.Linear(4, 1)
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self, x):
        x = self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        return x


model = Model()

# 3. Construct Loss and Optimizer
criterion = torch.nn.BCELoss(size_average=True)
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

# 4. Training Cycle
for epoch in range(1000):
    # Forward
    y_pred = model(x_data)  # This program has not use Mini-Batch for training.
    loss = criterion(y_pred, y_data)
    print(epoch, loss.item())

    # Backward
    optimizer.zero_grad()
    loss.backward()

    # Update
    optimizer.step()

使用不同的激活函数：

其中，ReLU并不是连续的。

可以在此链接出看到相应激活函数的具体内容：Visualising Activation Functions in Neural Networks

Pytorch激活函数文档：Non-linear Activations (weighted sum, nonlinearity)

当我们尝试不同的激活函数时，我们只需修改一小部分内容：
特别的，如果我们设置的激活函数是ReLU，由于它的取值范围是在（0，1），最后一层输出的值如果是小于0的话，那么在ReLU作用后，输出会为0，如果后面我们需要算ln0的话，就会出现问题。这个时候我们就可以把最后一层的激活函数改成sigmoid，这样结果就会得到0-1之间比较光滑的概率输出。

import numpy as np
import torch

#  1. Prepare Dataset
xy = np.loadtxt('diabetes.csv.gz', delimiter=',', dtype=np.float32)
x_data = torch.from_numpy((xy[:, :-1]))
y_data = torch.from_numpy(xy[:, [-1]])


# 2. Define Model
class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(8, 6)
        self.linear2 = torch.nn.Linear(6, 4)
        self.linear3 = torch.nn.Linear(4, 1)
        # self.sigmoid = torch.nn.Sigmoid()
        self.activate = torch.nn.ReLU()

    def forward(self, x):
        x = self.activate(self.linear1(x))
        x = self.activate(self.linear2(x))
        x = torch.sigmoid(self.linear3(x))
        return x


model = Model()

# 3. Construct Loss and Optimizer
criterion = torch.nn.BCELoss(size_average=True)
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

# 4. Training Cycle
for epoch in range(1000):
    # Forward
    y_pred = model(x_data)  # This program has not use Mini-Batch for training.
    loss = criterion(y_pred, y_data)
    print(epoch, loss.item())

    # Backward
    optimizer.zero_grad()
    loss.backward()

    # Update
    optimizer.step()