CMU Deep Learning 2018 by Bhiksha Raj 学习记录(1)

• Recitation 2

• numpy operations
  • array index
  • x = np.arange(10) ** 2

    # x:[ 0 1 4 9 16 25 36 49 64 81]

    print(x[::-1]) # all reversed

    print(x[8:1:-1]) # reversed slice

    print(x[::2]) # every other

    print(x[:]) # no-op (but useful syntax when dealing with n-d arrays)

    ---

    output:

    [81 64 49 36 25 16 9 4 1 0][64 49 36 25 16 9 4]

    [ 0 4 16 36 64][ 0 1 4 9 16 25 36 49 64 81]

  • simple syntax
  • # Simple syntax

    np.random.seed(123)

    x=np.random.random((10,))

    print(x)

    print(x>0.5)

    print(x[x>0.5])

    ---

    [ 0.69646919 0.28613933 0.22685145 0.55131477 0.71946897 0.42310646

    0.9807642 0.68482974 0.4809319 0.39211752][ True False False True True False True True False False]

    [ 0.69646919 0.55131477 0.71946897 0.9807642 0.68482974]

  • get diagonal elements
  • # Create a random matrix

    x = np.random.random((5,5))

    print(x)

    # Get diagonal elements

    print(np.diag(x))

  • save a single array
  • x = np.random.random((5,))

    np.save(‘temp.npy‘, x)

    y = np.load(‘temp.npy‘)

    print(y)

  • save dict of arrays
  • x1 = x = np.random.random((2,))

    y1 = x = np.random.random((2,))

    np.savez(‘temp.npy‘, x = x1, y = y1)

    data.np.load(‘temp.npy‘)

    print(data[‘x‘])

    print(data[‘y‘])

  • transpose
  • x=np.random.random((2,3))

    print(x)

    print(x.T) # simple transpose

    print(np.transpose(x, (1,0))) # syntax for multiple dimensions

    ---

    [[ 0.6919703 0.55438325 0.38895057][ 0.92513249 0.84167 0.35739757]][[ 0.6919703 0.92513249][ 0.55438325 0.84167 ]

    [ 0.38895057 0.35739757]]

    [[ 0.6919703 0.92513249][ 0.55438325 0.84167 ]

    [ 0.38895057 0.35739757]]

  • Add/remove a dim
  • # Special functions for adding and removing dims

    x=np.random.random((2,3,1))

    print(np.expand_dims(x, 1).shape) # add a new dimension

    print(np.squeeze(x,2).shape) # remove a dimension (must be size of 1)

    ---

    (2, 1, 3, 1)

    (2, 3)

• Pytorch operation
  • ‘‘‘

    https://github.com/cmudeeplearning11785/deep-learning-tutorials/blob/master/recitation-2/Tutorial-pytorch.ipynb

    ‘‘‘

import torch

import numpy as np

from torch.autograd import Variable

x = torch.FloatTensor(2,3)

print(x)

x.zero_()

print(x)

np.random.seed(123)

np_array = np.random.random((2,3))

print(torch.FloatTensor(np_array))

print(torch.from_numpy(np_array))

torch.manual_seed(123)

print(torch.randn(2,3))

print(torch.eye(3))

print(torch.ones(2,3))

print(torch.zeros(2,3))

print(torch.arange(0,3))

x = torch.FloatTensor(3,4)

print(x.size())

print(x.type())

x = torch.rand(3,2)

print(x)

y = x.cuda()

print(y)

z = y.cpu()

print(z)

print(z.numpy())

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

y = torch.rand(5,4).cuda()

print(torch.mm(x,y))

print(x.new(1,2).zero_())

from timeit import timeit

x = torch.rand(1000,64)

y = torch.rand(64,32)

number = 10000

def square():

z = torch.mm(x,y)

print(‘CPU: {}ms‘.format(timeit(square,number = number)1000))

x,y = x.cuda(),y.cuda()

print(‘GPU: {}ms‘.format(timeit(square,number = number)1000))

x = torch.arange(0,5)

print(torch.sum(x))

print(torch.sum(torch.exp(x)))

print(torch.mean(x))

x = torch.rand(3,2)

print(x)

print(x[1,:])

x = Variable(torch.arange(0,4),requires_grad = True)

y = torch.sum(x**2)

y.backward()

print(x)

print(y)

print(x.grad)

x = torch.rand(3,5)

y = torch.rand(5,4)

xv = Variable(x)

yv = Variable(y)

print(torch.mm(x,y))

print(torch.mm(xv,yv))

x = Variable(torch.arange(0,4),requires_grad = True)

torch.sum(x ** 2).backward()

print(x.grad)

torch.sum(x ** 2).backward()

print(x.grad)

x.grad.data.zero_()

torch.sum(x ** 2).backward()

print(x.grad)

net = torch.nn.Sequential(

torch.nn.Linear(28*28,256),

torch.nn.Sigmoid(),

torch.nn.Linear(256,10)

)

print(net.state_dict().keys())

print(net.state_dict())

torch.save(net.state_dict(),‘test.t7‘)

net.load_state_dict(torch.load(‘test.t7‘))

class MyNetwork(torch.nn.Module):

def init(self):

super().init()

self.layer1 = torch.nn.Linear(28*28,256),

self.layer2 = torch.nn.Sigmoid(),

self.layer3 = torch.nn.Linear(256,10)

def forward(self,input_val):
    h = input_val
    h = self.layer1(h)
    h = self.layer2(h)
    h = self.layer3(h)
    return h
    

net = MyNetwork()

原文地址：https://www.cnblogs.com/ecoflex/p/8870121.html