【数据平台】Pytorch库初识

PyTorch是使用GPU和CPU优化的深度学习张量库。

1、安装，参考官网：http://pytorch.org/

    

conda install pytorch torchvision -c pytorch

2、认识，参考：

https://github.com/yunjey/pytorch-tutorial

https://github.com/jcjohnson/pytorch-examples

http://pytorch-cn.readthedocs.io/zh/latest/

3、demo：

# Code in file tensor/two_layer_net_tensor.pyimport torchdtype = torch.FloatTensor# dtype = torch.cuda.FloatTensor # Uncomment this to run on GPU# 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 = torch.randn(N, D_in).type(dtype)y = torch.randn(N, D_out).type(dtype)# Randomly initialize weightsw1 = torch.randn(D_in, H).type(dtype)w2 = torch.randn(H, D_out).type(dtype)learning_rate = 1e-6for t in range(500):    # Forward pass: compute predicted y    h = x.mm(w1)    h_relu = h.clamp(min=0)    y_pred = h_relu.mm(w2)    # Compute and print loss    loss = (y_pred - y).pow(2).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().mm(grad_y_pred)    grad_h_relu = grad_y_pred.mm(w2.t())    grad_h = grad_h_relu.clone()    grad_h[h < 0] = 0    grad_w1 = x.t().mm(grad_h)    # Update weights using gradient descent    w1 -= learning_rate * grad_w1    w2 -= learning_rate * grad_w2

结果如下：

而同样np跑出来的结果是：

代码如下：

# Code in file tensor/two_layer_net_numpy.pyimport 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

根据实际应用场景，后续可深入学习，重点是gpu了。