Some matrix operation statements of numpy and torch

Just for the record, please skip

Matrix multiplication (x*w1)

# numpy
h = x.dot(w1)

# torch
h = x.mm(w1)

The ones greater than 0 are reserved, the ones less than 0 are set to 0-the effect of the activation function ReLU

# numpy
h_relu = np.maximum(h, 0)

# torch
h_relu = h.clamp(min=0)

Subtract two arrays, the sum of the squares of each element after subtraction

# numpy
loss = np.square(y_pred - y).sum()

# torch
loss = (y_pred - y).pow(2).sum().item()

Note that you also need to take.item()

Take the transpose of the matrix

# numpy
grad_w2 = h_relu.T.dot(grad_y_pred)

# torch
grad_w2 = h_relu.t().mm(grad_y_pred)

torch is used.t()

Copy array

# numpy
grad_h = grad_h_relu.copy()

# torch
grad_h = grad_h_relu.clone()

Portal