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PyTorch从Scratch训练Convent

在本章中,我们将重点学习如何从Scratch创建一个Convent。这推断了使用 torch 创建相应的修道院或样本神经网络。

第1步

使用各自的参数创建必要的类,参数包括具有随机值的权重。

class Neural_Network(nn.Module):
def __init__(self, ):
super(Neural_Network, self).__init__()
self.inputSize = 2
self.outputSize = 1
self.hiddenSize = 3
# weights
self.W1 = torch.randn(self.inputSize,
self.hiddenSize) # 3 X 2 tensor
self.W2 = torch.randn(self.hiddenSize, self.outputSize) # 3 X 1 tensor

第2步

使用sigmoid函数创建函数的向前模式。

def forward(self, X):
self.z = torch.matmul(X, self.W1) # 3 X 3 ".dot"
does not broadcast in PyTorch
self.z2 = self.sigmoid(self.z) # activation function
self.z3 = torch.matmul(self.z2, self.W2)
o = self.sigmoid(self.z3) # final activation
function
return o
def sigmoid(self, s):
return 1 / (1 + torch.exp(-s))
def sigmoidPrime(self, s):
# derivative of sigmoid
return s * (1 - s)
def backward(self, X, y, o):
self.o_error = y - o # error in output
self.o_delta = self.o_error * self.sigmoidPrime(o) # derivative of sig to error
self.z2_error = torch.matmul(self.o_delta, torch.t(self.W2))
self.z2_delta = self.z2_error * self.sigmoidPrime(self.z2)
self.W1 + = torch.matmul(torch.t(X), self.z2_delta)
self.W2 + = torch.matmul(torch.t(self.z2), self.o_delta)

第3步
创建如下所述的培训和预测模型 –

def train(self, X, y):
# forward + backward pass for training
o = self.forward(X)
self.backward(X, y, o)
def saveWeights(self, model):
# Implement PyTorch internal storage functions
torch.save(model, "NN")
# you can reload model with all the weights and so forth with:
# torch.load("NN")
def predict(self):
print ("Predicted data based on trained weights: ")
print ("Input (scaled): " + str(xPredicted))
print ("Output: " + str(self.forward(xPredicted)))

炫意HTML5 » PyTorch从Scratch训练Convent

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