PyTorch入门学习-1.深度学习回顾和PyTorch简介

01 神经网络快速入门

Modeling, Inference, Learning

什么是深度学习？

什么是神经网络？

激活函数

前向神经网络

卷积神经网络

Recurrent Neural Networks（RNN循环神经网络)

Seq2Seq with Attention

PyTorch可以做什么？

02 用PyTorch构建深度学习模型

• 深度学习模型框架
  TensorFlow ay/net
  PyTorch Caffe
  mxnet Keras
• PyTorch与其他框架的对比

（1）PyTorch：动态计算图 Dynamic Computation Graph
Tensorflow：静态计算图 Static Computation Graph
（2）PyTorch代码通俗易懂，非常接近Python原生代码，不会让人感觉是 完全在学习一门新的语言。
（3）拥有Facebook支持，社区活跃。

- PyTorch

PyTorch是一个基于Python的科学计算库，特点：
①类似于NumPy，但是它可以使用GPU；
②可以用它定义深度学习模型，可以灵活地进行深度学习模型的训练和使用。

常用的操作
（1）构造一个未初始化的5x3矩阵：

 x = torch.empty(5，3)
 print(x)

结果：

tensor([[0.0000e+00, 0.0000e+00, 0.0000e+00],
        [0.0000e+00, 4.7339e+30, 1.4347e-19],
        [2.7909e+23, 1.8037e+28, 1.7237e+25],
        [9.1041e-12, 6.2609e+22, 4.7428e+30],
        [3.8001e-39, 0.0000e+00, 0.0000e+00]])

（2）构建一个随机初始化的矩阵：

x = torch.rand(5, 3)
print(x)

结果：

tensor([[0.4821, 0.3854, 0.8517],
        [0.7962, 0.0632, 0.5409],
        [0.8891, 0.6112, 0.7829],
        [0.0715, 0.8069, 0.2608],
        [0.3292, 0.0119, 0.2759]])

（3）构建一个全部为0，类型为long的矩阵：

x = torch.zeros(5, 3, dtype=torch.long)
print(x)

结果：

tensor([[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]])

（4）从数据直接直接构建tensor：

x = torch.tensor([5.5, 3])
print(x)

结果：

tensor([5.5000, 3.0000])

（5）从一个已有的tensor构建一个tensor。这些方法会重用原来tensor的特征，例如，数据类型，除非提供新的数据。

x = x.new_ones(5, 3, dtype=torch.double)    # new_* methods take in sizes
print(x)
x = torch.randn_like(x, dtype=torch.float)  # override dtype!
print(x)                                    # result has the same size

结果：

tensor([[1., 1., 1.],
        [1., 1., 1.],
        [1., 1., 1.],
        [1., 1., 1.],
        [1., 1., 1.]], dtype=torch.float64)
tensor([[ 1.4793, -2.4772,  0.9738],
        [ 2.0328,  1.3981,  1.7509],
        [-0.7931, -0.0291, -0.6803],
        [-1.2944, -0.7352, -0.9346],
        [ 0.5917, -0.5149, -1.8149]])

（6）加法运算

y = torch.rand(5, 3)
print(x + y)

结果：

tensor([[ 1.7113, -1.5490,  1.4009],
        [ 2.4590,  1.6504,  2.6889],
        [-0.3609,  0.4950, -0.3357],
        [-0.5029, -0.3086, -0.1498],
        [ 1.2850, -0.3189, -0.8868]])

（7）另一种加法的操作

`print(torch.add(x, y)`)

结果：

tensor([[ 1.7113, -1.5490,  1.4009],
        [ 2.4590,  1.6504,  2.6889],
        [-0.3609,  0.4950, -0.3357],
        [-0.5029, -0.3086, -0.1498],
        [ 1.2850, -0.3189, -0.8868]])

（8）in-place加法

adds x to y

y.add_(x)
print(y)

结果：

tensor([[ 1.7113, -1.5490,  1.4009],
        [ 2.4590,  1.6504,  2.6889],
        [-0.3609,  0.4950, -0.3357],
        [-0.5029, -0.3086, -0.1498],
        [ 1.2850, -0.3189, -0.8868]])

（9）如果只有一个元素的tensor，使用.item()方法可以把里面的value变成Python数值。

x = torch.randn(1)
print(x)
print(x.item())

结果：

tensor([0.4726])
0.4726296067237854

（10）Numpy和Tensor之间的转化
在Torch Tensor和NumPy array之间相互转化非常容易。Torch Tensor和NumPy array会共享内存，所以改变其中一项也会改变另一项。把Torch Tensor转变成NumPy Array

a = torch.ones(5)
print(a)
b = a.numpy()
print(b)
a.add_(1)
print(a)
print(b)
import numpy as np
a = np.ones(5)
b = torch.from_numpy(a)
np.add(a, 1, out=a)
print(a)
print(b)

结果：

tensor([1., 1., 1., 1., 1.])
[1. 1. 1. 1. 1.]
tensor([2., 2., 2., 2., 2.])
[2. 2. 2. 2. 2.]
[2. 2. 2. 2. 2.]
tensor([2., 2., 2., 2., 2.], dtype=torch.float64)

第1课课后复习导图

用numpy实现两层神经网络
一个全连接ReLU神经网络，一个隐藏层，没有bias。用来从x预测y，使用L2 Loss。
这一实现完全使用numpy来计算前向神经网络，loss，和反向传播。
numpy ndarray是一个普通的n维array。它不知道任何关于深度学习或者梯度(gradient)的知识，也不知道计算图(computation graph)，只是一种用来计算数学运算的数据结构。
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 data
x = np.random.randn(N, D_in)
y = np.random.randn(N, D_out)

# Randomly initialize weights
w1 = np.random.randn(D_in, H)
w2 = np.random.randn(H, D_out)

learning_rate = 1e-6
for 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
    
    # loss = (y_pred - y) ** 2
    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

结果：

0 34399246.46047344
1 29023199.257758312
2 25155679.85447208
3 20344203.603057466
4 14771404.625789404
5 9796072.99431371
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