PyTorch - 实现第一个神经网络

PyTorch 具有创建和实现神经网络的特别功能。在本章中，我们将创建一个具有一个隐藏层并开发了一个输出单元的简单神经网络。

我们将使用以下步骤来实现使用 PyTorch 的第一个神经网络 −

步骤 1

首先，我们需要使用以下命令导入 PyTorch 库 −

import torch 
import torch.nn as nn

步骤 2

定义所有层和批处理大小以开始执行神经网络，如下所示 −

# Defining input size, hidden layer size, output size and batch size respectively
n_in, n_h, n_out, batch_size = 10, 5, 1, 10

步骤 3

由于神经网络包括输入数据的组合以获得各自的输出数据，因此我们将按照以下给出的相同程序进行 −

# Create dummy input and target tensors (data)
x = torch.randn(batch_size, n_in)
y = torch.tensor([[1.0], [0.0], [0.0], 
[1.0], [1.0], [1.0], [0.0], [0.0], [1.0], [1.0]])

步骤 4

使用内置函数创建一个顺序模型。使用以下代码行创建一个顺序模型 −

# Create a model
model = nn.Sequential(nn.Linear(n_in, n_h),
   nn.ReLU(),
   nn.Linear(n_h, n_out),
   nn.Sigmoid())

步骤 5

在梯度下降优化器的帮助下构造损失函数，如下所示 −

Construct the loss function
criterion = torch.nn.MSELoss()
# Construct the optimizer (Stochastic Gradient Descent in this case)
optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)

步骤 6

使用具有给定代码行的循环来实现梯度下降模型 −

# Gradient Descent
for epoch in range(50):
   # Forward pass: Compute predicted y by passing x to the model
   y_pred = model(x)

   # Compute and print loss
   loss = criterion(y_pred, y)
   print('epoch: ', epoch,' loss: ', loss.item())

   # Zero gradients, perform a backward pass, and update the weights.
   optimizer.zero_grad()

   # perform a backward pass (backpropagation)
   loss.backward()

   # Update the parameters
   optimizer.step()

步骤 7

生成的结果如下 −

epoch: 0 loss: 0.2545787990093231
epoch: 1 loss: 0.2545052170753479
epoch: 2 loss: 0.254431813955307
epoch: 3 loss: 0.25435858964920044
epoch: 4 loss: 0.2542854845523834
epoch: 5 loss: 0.25421255826950073
epoch: 6 loss: 0.25413978099823
epoch: 7 loss: 0.25406715273857117
epoch: 8 loss: 0.2539947032928467
epoch: 9 loss: 0.25392240285873413
epoch: 10 loss: 0.25385022163391113
epoch: 11 loss: 0.25377824902534485
epoch: 12 loss: 0.2537063956260681
epoch: 13 loss: 0.2536346912384033
epoch: 14 loss: 0.25356316566467285
epoch: 15 loss: 0.25349172949790955
epoch: 16 loss: 0.25342053174972534
epoch: 17 loss: 0.2533493936061859
epoch: 18 loss: 0.2532784342765808
epoch: 19 loss: 0.25320762395858765
epoch: 20 loss: 0.2531369626522064
epoch: 21 loss: 0.25306645035743713
epoch: 22 loss: 0.252996027469635
epoch: 23 loss: 0.2529257833957672
epoch: 24 loss: 0.25285571813583374
epoch: 25 loss: 0.25278574228286743
epoch: 26 loss: 0.25271597504615784
epoch: 27 loss: 0.25264623761177063
epoch: 28 loss: 0.25257670879364014
epoch: 29 loss: 0.2525072991847992
epoch: 30 loss: 0.2524380087852478
epoch: 31 loss: 0.2523689270019531
epoch: 32 loss: 0.25229987502098083
epoch: 33 loss: 0.25223103165626526
epoch: 34 loss: 0.25216227769851685
epoch: 35 loss: 0.252093642950058
epoch: 36 loss: 0.25202515721321106
epoch: 37 loss: 0.2519568204879761
epoch: 38 loss: 0.251888632774353
epoch: 39 loss: 0.25182053446769714
epoch: 40 loss: 0.2517525553703308
epoch: 41 loss: 0.2516847252845764
epoch: 42 loss: 0.2516169846057892
epoch: 43 loss: 0.2515493929386139
epoch: 44 loss: 0.25148195028305054
epoch: 45 loss: 0.25141456723213196
epoch: 46 loss: 0.2513473629951477
epoch: 47 loss: 0.2512802183628082
epoch: 48 loss: 0.2512132525444031
epoch: 49 loss: 0.2511464059352875