def train(train_features,
test_features,
train_labels,
test_labels,
num_epochs=400):
loss = nn.MSELoss()
input_shape = train_features.shape[-1]
# Switch off the bias since we already catered for it in the polynomial
# features
net = nn.Sequential(nn.Linear(input_shape, 1, bias=False))
batch_size = min(10, train_labels.shape[0])
train_iter = d2l.load_array((train_features, train_labels.reshape(-1, 1)),
batch_size)
test_iter = d2l.load_array((test_features, test_labels.reshape(-1, 1)),
batch_size,
is_train=False)
trainer = torch.optim.SGD(net.parameters(), lr=0.01)
animator = d2l.Animator(xlabel='epoch',
ylabel='loss',
yscale='log',
xlim=[1, num_epochs],
ylim=[1e-3, 1e2],
legend=['train', 'test'])
for epoch in range(num_epochs):
d2l.train_epoch_ch3(net, train_iter, loss, trainer)
if epoch == 0 or (epoch + 1) % 20 == 0:
animator.add(epoch + 1, (evaluate_loss(
net, train_iter, loss), evaluate_loss(net, test_iter, loss)))
final_training_loss = evaluate_loss(net, train_iter, loss)
print(f'final training loss: {final_training_loss}')
print('weight:', net[0].weight.data.numpy())
return final_training_loss
def load_data_imdb(batch_size, num_steps=500):
data_dir = d2l.download_extract('aclImdb','aclImdb')
train_data = read_imdb(data_dir, True)
test_data = read_imdb(data_dir, False)
train_tokens = d2l.tokenize(train_data[0], token='word')
test_tokens = d2l.tokenize(test_data[0], token='word')
vocab = d2l.Vocab(train_tokens, min_freq=5)
train_features = torch.tensor([d2l.truncate_pad(
vocab[line], num_steps, vocab['<pad>']) for line in train_tokens])
test_features = torch.tensor([d2l.truncate_pad(
vocab[line], num_steps, vocab['<pad>']) for line in test_tokens])
train_iter = d2l.load_array((train_features, torch.tensor(train_data[1])),
batch_size)
test_iter = d2l.load_array((test_features, torch.tensor(test_data[1])),
batch_size,
is_train=False)
return train_iter, test_iter, vocab
def load_data_nmt(batch_size, num_steps, num_examples=600):
"""Return the iterator and the vocabularies of the translation dataset."""
text = preprocess_nmt(read_data_nmt())
source, target = tokenize_nmt(text, num_examples)
src_vocab = d2l.Vocab(source, min_freq=2,
reserved_tokens=['<pad>', '<bos>', '<eos>'])
tgt_vocab = d2l.Vocab(target, min_freq=2,
reserved_tokens=['<pad>', '<bos>', '<eos>'])
src_array, src_valid_len = build_array_nmt(source, src_vocab, num_steps)
tgt_array, tgt_valid_len = build_array_nmt(target, tgt_vocab, num_steps)
data_arrays = (src_array, src_valid_len, tgt_array, tgt_valid_len)
data_iter = d2l.load_array(data_arrays, batch_size)
return data_iter, src_vocab, tgt_vocab
def train(net, train_features, train_labels, test_features, test_labels,
num_epochs, learning_rate, weight_decay, batch_size):
train_ls, test_ls = [], []
train_iter = d2l.load_array((train_features, train_labels), batch_size)
# The Adam optimization algorithm is used here
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate,
weight_decay=weight_decay)
for epoch in range(num_epochs):
for X, y in train_iter:
optimizer.zero_grad()
l = loss(net(X), y)
l.backward()
optimizer.step()
train_ls.append(log_rmse(net, train_features, train_labels))
if test_labels is not None:
test_ls.append(log_rmse(net, test_features, test_labels))
return train_ls, test_ls
def train(net, train_features, train_labels, test_features, test_labels,
num_epochs, learning_rate, weight_decay, batch_size):
train_ls, test_ls = [], []
train_iter = d2l.load_array((train_features, train_labels), batch_size)
# 这里使用的是Adam优化算法
optimizer = torch.optim.Adam(net.parameters(),
lr=learning_rate,
weight_decay=weight_decay)
for epoch in range(num_epochs):
for X, y in train_iter:
optimizer.zero_grad()
l = loss(net(X), y)
#l = log_rmse(net(X), y)
#l = myloss(net(X), y)
l.backward()
optimizer.step()
#train_ls.append(log_rmse(net(train_features), train_labels).item())
train_ls.append(loss(net(train_features), train_labels).item())
if test_labels is not None:
#test_ls.append(log_rmse(net(test_features), test_labels).item())
test_ls.append(loss(net(test_features), test_labels).item())
return train_ls, test_ls
train(float(λ), animator)
for λ in torch.range(2, 20, 5):
train(float(λ), animator)
plt.show()
"""
# ----------------------------------------------------------------------------
# 1.1.2
# What do you observe? Use a validation set to find the optimal value of λ.
# Is it really the optimal value? Does this matter?
n_train, n_test, num_inputs, batch_size = 20, 100, 200, 5
n_eval = 20
true_w, true_b = torch.ones((num_inputs, 1)) * 0.01, 0.05
train_data = d2l.synthetic_data(true_w, true_b, n_train)
train_iter = d2l.load_array(train_data, batch_size)
test_data = d2l.synthetic_data(true_w, true_b, n_test)
test_iter = d2l.load_array(test_data, batch_size, is_train=False)
eval_data = d2l.synthetic_data(true_w, true_b, n_eval)
eval_iter = d2l.load_array(eval_data, batch_size, is_train=False)
eval_epochs, num_epochs = 5, 10
def init_params():
w = torch.normal(0, 1, size=(num_inputs, 1), requires_grad=True)
b = torch.zeros(1, requires_grad=True)
λ = torch.zeros(1, requires_grad=True)
return [w, b, λ]
def sgd(params, lr, batch_size):
with torch.no_grad():
for param in params:
import torch.nn as nn
T = 1000 # Generate a total of 1000 points
time = torch.arange(1, T + 1, dtype=torch.float32)
x = torch.sin(0.01 * time) + torch.normal(0, 0.2, (T,))
d2l.plot(time, [x], 'time', 'x', xlim=[1, 1000], figsize=(6, 3))
tau = 4
features = torch.zeros((T - tau, tau))
for i in range(tau):
features[:, i] = x[i: T - tau + i]
labels = d2l.reshape(x[tau:], (-1, 1))
batch_size, n_train = 16, 600
# Only the first `n_train` examples are used for training
train_iter = d2l.load_array((features[:n_train], labels[:n_train]),batch_size, is_train=True)
# Function for initializing the weights of the network
def init_weights(m):
if type(m) == nn.Linear:
torch.nn.init.xavier_uniform_(m.weight)
# A simple MLP
def get_net():
net = nn.Sequential(nn.Linear(4, 10),
nn.ReLU(),
nn.Linear(10, 1))
net.apply(init_weights)
return net
# Square loss
# %%
train_tokens = d2l.tokenize(train_data[0], token='word')
vocab = d2l.Vocab(train_tokens, min_freq=5, reserved_tokens=['<pad>'])
d2l.set_figsize()
d2l.plt.hist([len(line) for line in train_tokens], bins=range(0,1000,50))
#%%
num_steps = 500
train_features = torch.tensor([d2l.truncate_pad(
vocab[line], num_steps, vocab['<pad>']) for line in train_tokens])
print(train_features.shape)
#%%
train_iter = d2l.load_array((train_features, torch.tensor(train_data[1])), 64)
for X, y in train_iter:
print('X:', X.shape, ',y:', y.shape)
break
print('#batches:', len(train_iter))
#%%
def read_imdb(data_dir, is_train):
data, labels = [], []
for label in ('pos','neg'):
folder_name = os.path.join(data_dir, 'train' if is_train else 'test',
label)
for file in os.listdir(folder_name):
with open(os.path.join(folder_name, file), 'rb') as f:
review = f.read().decode('utf-8').replace('\n','')
data.append(review)
