<reponame>JunoCheon/D2L<filename>PYwithD2L/CH3/3.2-3. Linear REG.py
#%%
%matplotlib inline
import matplotlib1
import matplotlib.pyplot as plt
import random
from d2l import torch as d2l
import torch
# from d2l import torch as d2l

#%%

def synthetic_data(w, b, num_examples):  #@save
    """Generate y = Xw + b + noise."""
    X = torch.normal(0, 1, (num_examples, len(w)))
    y = torch.matmul(X, w) + b
    y += torch.normal(0, 0.01, y.shape)
    return X, y.reshape((-1, 1))

true_w = torch.tensor([2, -3.4])
true_b = 4.2
features, labels = synthetic_data(true_w, true_b, 1000)
#%%
print('features:', features[0], '\nlabel:', labels[0])
#%%

def data_iter(batch_size, features, labels):
    num_examples = len(features)
    indices = list(range(num_examples))
    # The examples are read at random, in no particular order
    random.shuffle(indices)
    for i in range(0, num_examples, batch_size):
        batch_indices = torch.tensor(indices[i:min(i + batch_size, num_examples)])
        yield features[batch_indices], labels[batch_indices]
#%%
batch_size = 10

for X, y in data_iter(batch_size, features, labels):
    print(X, '\n', y)
    break
#%%
w = torch.normal(0, 0.01, size=(2, 1), requires_grad=True)
b = torch.zeros(1, requires_grad=True)
#%%
def linreg(X, w, b):  #@save
    """The linear regression model."""
    return torch.matmul(X, w) + b
#%%
def squared_loss(y_hat, y):  #@save
    """Squared loss."""
    return (y_hat - y.reshape(y_hat.shape))**2 / 2
#%%
def sgd(params, lr, batch_size):  #@save
    """Minibatch stochastic gradient descent."""
    with torch.no_grad():
        for param in params:
            param -= lr * param.grad / batch_size
            param.grad.zero_()

#%%
lr = 0.03
num_epochs = 100
net = linreg
loss = squared_loss

for epoch in range(num_epochs):
    for X, y in data_iter(batch_size, features, labels):
        l = loss(net(X, w, b), y)  # Minibatch loss in `X` and `y`
        # Compute gradient on `l` with respect to [`w`, `b`]
        l.sum().backward()
        sgd([w, b], lr, batch_size)  # Update parameters using their gradient
    with torch.no_grad():
        train_l = loss(net(features, w, b), labels)
        print(f'epoch {epoch + 1}, loss {float(train_l.mean()):f}')
#%%
print(f'error in estimating w: {true_w - w.reshape(true_w.shape)}')
print(f'error in estimating b: {true_b - b}')

#########################################
########### 3.3LINEAR REG ###############
#########################################
#%%
import numpy as np
import torch
from torch.utils import data
# from d2l import torch as d2l

true_w = torch.tensor([2, -3.4])
true_b = 4.2
features, labels = synthetic_data(true_w, true_b, 1000)


#%%
def load_array(data_arrays, batch_size, is_train=True):  #@save
    """Construct a PyTorch data iterator."""
    dataset = data.TensorDataset(*data_arrays)
    return data.DataLoader(dataset, batch_size, shuffle=is_train)

batch_size = 10
data_iter = load_array((features, labels), batch_size)


#%%
next(iter(data_iter))
#%%
# `nn` is an abbreviation for neural networks
from torch import nn

net = nn.Sequential(nn.Linear(2, 1))


#%%

net[0].weight.data.normal_(0, 0.01)
net[0].bias.data.fill_(0)


#%%
loss = nn.MSELoss()


#%%
trainer = torch.optim.SGD(net.parameters(), lr=0.03)


#%%
num_epochs = 3
for epoch in range(num_epochs):
    for X, y in data_iter:
        l = loss(net(X), y)
        trainer.zero_grad()
        l.backward()
        trainer.step()
    l = loss(net(features), labels)
    print(f'epoch {epoch + 1}, loss {l:f}')
    
    

#%%
w = net[0].weight.data
print('error in estimating w:', true_w - w.reshape(true_w.shape))
b = net[0].bias.data
print('error in estimating b:', true_b - b)


#%%



w = torch.tensor(2.0, requires_grad=True)
print(w)
y=w**2+1
z = 2*y
z.backward()
print(z)
w.grad
#%%
print(torch.tensor([1,1]))
#%%


import numpy as np
import torch
from torch.utils import data
from d2l import torch as d2l

true_w = torch.tensor([2, -3.4])
true_b = 4.2
features, labels = d2l.synthetic_data(true_w, true_b, 1000)



#%%
def synthetic_data(w, b, num_examples):  #@save
    """Generate y = Xw + b + noise."""
    X = torch.normal(0, 1, (num_examples, len(w)))
    y = torch.matmul(X, w) + b
    y += torch.normal(0, 0.01, y.shape)
    return X, y.reshape((-1, 1))

true_w = torch.tensor([2, -3.4])
true_b = 4.2
features, labels = synthetic_data(true_w, true_b, 1000)

#%%
plt.scatter(features[:, (1)].detach().numpy(),
                labels.detach().numpy(), 1);

#%%
from torch import nn

net = nn.Sequential(nn.Linear(2,1))
# %%
net[0].weight.data.normal_(0,0.01)
net[0].bias.data.fill_(0)

# %%
loss = nn.MSELoss()

# %%
trainer = torch.optim.SGD(net.parameters(), lr=0.03)

#%%
def data_iter(batch_size, features, labels):
    num_examples = len(features)
    indices = list(range(num_examples))
    # The examples are read at random, in no particular order
    random.shuffle(indices)
    for i in range(0, num_examples, batch_size):
        batch_indices = torch.tensor(indices[i:min(i + batch_size, num_examples)])
        yield features[batch_indices], labels[batch_indices]

#%%
data_iter = load_array((features, labels), batch_size)
#%%
num_epochs = 3
for epoch in range(num_epochs):
    for X,y in data_iter:
        l = loss(net(X),y)
        trainer.zero_grad()
        l.backward()
        trainer.step()
    l= loss(net(features),labels)
    f'epoch {epoch +1} , loss {l:f}'
# %%

Linear REG.py
#%%
%matplotlib inline
import matplotlib1
import matplotlib.pyplot as plt
import random
from d2l import torch as d2l
import torch
# from d2l import torch as d2l

#%%

def synthetic_data(w, b, num_examples):  #@save
    """Generate y = Xw + b + noise."""
    X = torch.normal(0, 1, (num_examples, len(w)))
    y = torch.matmul(X, w) + b
    y += torch.normal(0, 0.01, y.shape)
    return X, y.reshape((-1, 1))

true_w = torch.tensor([2, -3.4])
true_b = 4.2
features, labels = synthetic_data(true_w, true_b, 1000)
#%%
print('features:', features[0], '\nlabel:', labels[0])
#%%

def data_iter(batch_size, features, labels):
    num_examples = len(features)
    indices = list(range(num_examples))
    # The examples are read at random, in no particular order
    random.shuffle(indices)
    for i in range(0, num_examples, batch_size):
        batch_indices = torch.tensor(indices[i:min(i + batch_size, num_examples)])
        yield features[batch_indices], labels[batch_indices]
#%%
batch_size = 10

for X, y in data_iter(batch_size, features, labels):
    print(X, '\n', y)
    break
#%%
w = torch.normal(0, 0.01, size=(2, 1), requires_grad=True)
b = torch.zeros(1, requires_grad=True)
#%%
def linreg(X, w, b):  #@save
    """The linear regression model."""
    return torch.matmul(X, w) + b
#%%
def squared_loss(y_hat, y):  #@save
    """Squared loss."""
    return (y_hat - y.reshape(y_hat.shape))**2 / 2
#%%
def sgd(params, lr, batch_size):  #@save
    """Minibatch stochastic gradient descent."""
    with torch.no_grad():
        for param in params:
            param -= lr * param.grad / batch_size
            param.grad.zero_()

#%%
lr = 0.03
num_epochs = 100
net = linreg
loss = squared_loss

for epoch in range(num_epochs):
    for X, y in data_iter(batch_size, features, labels):
        l = loss(net(X, w, b), y)  # Minibatch loss in `X` and `y`
        # Compute gradient on `l` with respect to [`w`, `b`]
        l.sum().backward()
        sgd([w, b], lr, batch_size)  # Update parameters using their gradient
    with torch.no_grad():
        train_l = loss(net(features, w, b), labels)
        print(f'epoch {epoch + 1}, loss {float(train_l.mean()):f}')
#%%
print(f'error in estimating w: {true_w - w.reshape(true_w.shape)}')
print(f'error in estimating b: {true_b - b}')

#########################################
########### 3.3LINEAR REG ###############
#########################################
#%%
import numpy as np
import torch
from torch.utils import data
# from d2l import torch as d2l

true_w = torch.tensor([2, -3.4])
true_b = 4.2
features, labels = synthetic_data(true_w, true_b, 1000)


#%%
def load_array(data_arrays, batch_size, is_train=True):  #@save
    """Construct a PyTorch data iterator."""
    dataset = data.TensorDataset(*data_arrays)
    return data.DataLoader(dataset, batch_size, shuffle=is_train)

batch_size = 10
data_iter = load_array((features, labels), batch_size)


#%%
next(iter(data_iter))
#%%
# `nn` is an abbreviation for neural networks
from torch import nn

net = nn.Sequential(nn.Linear(2, 1))


#%%

net[0].weight.data.normal_(0, 0.01)
net[0].bias.data.fill_(0)


#%%
loss = nn.MSELoss()


#%%
trainer = torch.optim.SGD(net.parameters(), lr=0.03)


#%%
num_epochs = 3
for epoch in range(num_epochs):
    for X, y in data_iter:
        l = loss(net(X), y)
        trainer.zero_grad()
        l.backward()
        trainer.step()
    l = loss(net(features), labels)
    print(f'epoch {epoch + 1}, loss {l:f}')
    
    

#%%
w = net[0].weight.data
print('error in estimating w:', true_w - w.reshape(true_w.shape))
b = net[0].bias.data
print('error in estimating b:', true_b - b)


#%%



w = torch.tensor(2.0, requires_grad=True)
print(w)
y=w**2+1
z = 2*y
z.backward()
print(z)
w.grad
#%%
print(torch.tensor([1,1]))
#%%


import numpy as np
import torch
from torch.utils import data
from d2l import torch as d2l

true_w = torch.tensor([2, -3.4])
true_b = 4.2
features, labels = d2l.synthetic_data(true_w, true_b, 1000)



#%%
def synthetic_data(w, b, num_examples):  #@save
    """Generate y = Xw + b + noise."""
    X = torch.normal(0, 1, (num_examples, len(w)))
    y = torch.matmul(X, w) + b
    y += torch.normal(0, 0.01, y.shape)
    return X, y.reshape((-1, 1))

true_w = torch.tensor([2, -3.4])
true_b = 4.2
features, labels = synthetic_data(true_w, true_b, 1000)

#%%
plt.scatter(features[:, (1)].detach().numpy(),
                labels.detach().numpy(), 1);

#%%
from torch import nn

net = nn.Sequential(nn.Linear(2,1))
# %%
net[0].weight.data.normal_(0,0.01)
net[0].bias.data.fill_(0)

# %%
loss = nn.MSELoss()

# %%
trainer = torch.optim.SGD(net.parameters(), lr=0.03)

#%%
def data_iter(batch_size, features, labels):
    num_examples = len(features)
    indices = list(range(num_examples))
    # The examples are read at random, in no particular order
    random.shuffle(indices)
    for i in range(0, num_examples, batch_size):
        batch_indices = torch.tensor(indices[i:min(i + batch_size, num_examples)])
        yield features[batch_indices], labels[batch_indices]

#%%
data_iter = load_array((features, labels), batch_size)
#%%
num_epochs = 3
for epoch in range(num_epochs):
    for X,y in data_iter:
        l = loss(net(X),y)
        trainer.zero_grad()
        l.backward()
        trainer.step()
    l= loss(net(features),labels)
    f'epoch {epoch +1} , loss {l:f}'
# %%
#%% # from d2l import torch as d2l #%% #@save Generate y = Xw + b + noise. #%% #%% # The examples are read at random, in no particular order #%% #%% #%% #@save The linear regression model. #%% #@save Squared loss. #%% #@save Minibatch stochastic gradient descent. #%% # Minibatch loss in `X` and `y` # Compute gradient on `l` with respect to [`w`, `b`] # Update parameters using their gradient #%% ######################################### ########### 3.3LINEAR REG ############### ######################################### #%% # from d2l import torch as d2l #%% #@save Construct a PyTorch data iterator. #%% #%% # `nn` is an abbreviation for neural networks #%% #%% #%% #%% #%% #%% #%% #%% #%% #@save Generate y = Xw + b + noise. #%% #%% # %% # %% # %% #%% # The examples are read at random, in no particular order #%% #%% # %%