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()