pytorch梯度下降函数_pytorch入门2：梯度下降算法

原理：

通过梯度下降算法，求解loss的最小值，设置好初始w权重值，不断进行迭代，最后得到最小的loss值

代码：

# Training Data

x_data = [1.0, 2.0, 3.0]

y_data = [2.0, 4.0, 6.0]

w = 1.0 # a random guess: random value

# our model forward pass

def forward(x):

return x * w

# Loss function

def loss(x, y):

y_pred = forward(x)

return (y_pred - y) * (y_pred - y)

# compute gradient

def gradient(x, y): # d_loss/d_w

return 2 * x * (x * w - y)

# Before training

print("Prediction (before training)", 4, forward(4))

# Training loop

for epoch in range(10):

for x_val, y_val in zip(x_data, y_data):

# Compute derivative w.r.t to the learned weights

# Update the weights

# Compute the loss and print progress

grad = gradient(x_val, y_val)

w = w - 0.01 * grad

print("\tgrad: ", x_val, y_val, round(grad, 2))

l = loss(x_val, y_val)

print("progress:", epoch, "w=", round(w, 2), "loss=", round(l, 2))

# After training

print("Predicted score (after training)", "4 hours of studying: ", forward(4))

知识点：

a为学习率，设置为0.01

迭代过程:

运行结果：

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