PyTorch 实战-用 Numpy 热身

Numpy provides an n-dimensional array object, and many functions for manipulating these arrays. Numpy is a generic framework for scientific computing; it does not know anything about computation graphs, or deep learning, or gradients. However we can easily use numpy to fit a two-layer network to random data by manually implementing the forward and backward passes through the network using numpy operations:

# -*- coding: utf-8 -*-import numpy as np# N is batch size; D_in is input dimension;# H is hidden dimension; D_out is output dimension.N, D_in, H, D_out = 64, 1000, 100, 10# Create random input and output datax = np.random.randn(N, D_in)y = np.random.randn(N, D_out)# Randomly initialize weightsw1 = np.random.randn(D_in, H)w2 = np.random.randn(H, D_out)learning_rate = 1e-6for t in range(500):    # Forward pass: compute predicted y    h = x.dot(w1)    h_relu = np.maximum(h, 0)    y_pred = h_relu.dot(w2)    # Compute and print loss    loss = np.square(y_pred - y).sum()    print(t, loss)    # Backprop to compute gradients of w1 and w2 with respect to loss    grad_y_pred = 2.0 * (y_pred - y)    grad_w2 = h_relu.T.dot(grad_y_pred)    grad_h_relu = grad_y_pred.dot(w2.T)    grad_h = grad_h_relu.copy()    grad_h[h < 0] = 0    grad_w1 = x.T.dot(grad_h)    # Update weights    w1 -= learning_rate * grad_w1    w2 -= learning_rate * grad_w2

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