def fit_and_plot_gluon(wd):
net = nn.Sequential()
net.add(nn.Dense(1))
net.initialize(init.Normal(sigma=1))
# 对权重参数衰减。权重名称一般是以weight结尾
trainer_w = gluon.Trainer(net.collect_params(".*weight"), "sgd", {
"learning_rate": lr,
"wd": wd
})
# 不对偏差参数衰减。偏差名称一般是以bias结尾
trainer_b = gluon.Trainer(net.collect_params(".*bias"), "sgd",
{"learning_rate": lr})
train_ls, test_ls = [], []
for _ in range(num_epochs):
for X, y in train_iter:
with autograd.record():
l = loss(net(X), y)
l.backward()
trainer_w.step(batch_size)
trainer_b.step(batch_size)
train_ls.append(
loss(net(train_features), train_labels).mean().asscalar())
test_ls.append(loss(net(test_features), test_labels).mean().asscalar())
d2l.semilogy(range(1, num_epochs + 1), train_ls, "epochs", "loss",
range(1, num_epochs + 1), test_ls, ["train", "test"])
print("L2 norm of w", net[0].weight.data().norm().asscalar())
def k_fold(k, X_train, y_train, num_epochs, learning_rate, weight_decay,
batch_size):
train_l_sum, valid_l_sum = 0, 0
for i in range(k):
data = get_k_fold_data(k, i, X_train, y_train)
net = get_net()
train_ls, valid_ls = train(
net,
data[0],
data[1],
data[2],
data[3],
num_epochs,
learning_rate,
# train_ls, valid_ls = train(net, train_features, train_labels, test_features, test_labels, num_epochs, learning_rate,
weight_decay,
batch_size)
train_l_sum += train_ls[-1]
valid_l_sum += valid_ls[-1]
if i == 0:
d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'rmse',
range(1, num_epochs + 1), valid_ls,
['train', 'valid'])
print('fold %d, train rmse %f, valid rmse %f' %
(i, train_ls[-1], valid_ls[-1]))
return train_l_sum / k, valid_l_sum / k
def fit_and_plot(train_features, test_features, train_labels, test_labels):
net = nn.Sequential()
net.add(nn.Dense(1))
net.initialize()
batch_size = min(10, train_labels.shape[0])
train_iter = gdata.DataLoader(gdata.ArrayDataset(train_features,
train_labels),
batch_size,
shuffle=True)
trainer = gluon.Trainer(net.collect_params(), 'sgd',
{'learning_rate': 0.01})
train_ls, test_ls = [], []
for _ in range(num_epochs):
for X, y in train_iter:
with autograd.record():
l = loss(net(X), y)
l.backward()
trainer.step(batch_size)
train_ls.append(
loss(net(train_features), train_labels).mean().asscalar())
test_ls.append(loss(net(test_features), test_labels).mean().asscalar())
print('final epoch: train loss', train_ls[-1], 'test loss', test_ls[-1])
d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'loss',
range(1, num_epochs + 1), test_ls, ['train', 'test'])
print('weight:', net[0].weight.data().asnumpy(), '\nbias:',
net[0].bias.data().asnumpy())
def fit_and_plot_gluon(wd):
net = nn.Sequential()
net.add(nn.Dense(1))
net.initialize(init.Normal(sigma=1))
# add attenuation to weight
trainer_w = gluon.Trainer(net.collect_params('.*weight'), 'sgd',
{'learning_rate': lr, 'wd': wd})
# don't add attenuation to bias
trainer_b = gluon.Trainer(net.collect_params('.*bias'), 'sgd',
{'learning_rate': lr})
train_ls, test_ls = [], []
for _ in range(num_epochs):
for x, y in train_iter:
with autograd.record():
l = loss(net(x), y)
l.backward()
# update wight and bias with function step
trainer_w.step(batch_size)
trainer_b.step(batch_size)
train_ls.append(loss(net(train_features),
train_labels).mean().asscalar())
test_ls.append(loss(net(test_features),
test_labels).mean().asscalar())
#print('true w: ', trainer_w.mean())
print('L2 norm of w:', net[0].weight.data().norm().asscalar())
print('final epoch: train loss ', train_ls[-1], 'test loss', test_ls[-1])
d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'loss',
range(1, num_epochs + 1), test_ls, ['train', 'test'])
def fit_and_plot_gluon(wd):
net = nn.Sequential()
net.add(nn.Dense(1))
net.initialize(init.Normal(sigma=1))
# 对权重参数衰减。权重名称一般是以weight结尾
trainer_w = gluon.Trainer(net.collect_params('.*weight'), 'sgd', {
'learning_rate': lr,
'wd': wd
})
# 不对偏差参数衰减。偏差名称一般是以bias结尾
trainer_b = gluon.Trainer(net.collect_params('.*bias'), 'sgd',
{'learning_rate': lr})
train_ls, test_ls = [], []
for _ in range(num_epochs):
for X, y in train_iter:
with autograd.record():
l = loss(net(X), y)
l.backward()
# 对两个Trainer实例分别调用step函数，从而分别更新权重和偏差
trainer_w.step(batch_size)
trainer_b.step(batch_size)
train_ls.append(
loss(net(train_features), train_labels).mean().asscalar())
test_ls.append(loss(net(test_features), test_labels).mean().asscalar())
d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'loss',
range(1, num_epochs + 1), test_ls, ['train', 'test'])
print('L2 norm of w:', net[0].weight.data().norm().asscalar())
def train_and_pred(train_features, test_features, train_lables, test_data,
num_epochs, lr, weight_decay, batch_size):
net = get_net()
train_ls, _ = train(net, train_features, train_lables, None, None,
num_epochs, lr, weight_decay, batch_size)
d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'rmse')
print('train rmse %f' % train_ls[-1])
preds = net(train_features).asnumpy()
test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])
def train_and_pred(train_features,test_features,train_labels,test_data,num_epochs,learning_rate,weight_decay,batch_size):
net = get_net()
train_ls, _ = train(net, train_features, train_labels, None,None,num_epochs,learning_rate,weight_decay,batch_size)
d2l.semilogy(range(1, num_epochs + 1),train_ls,'epochs','rmse')
print('train rmse %f' % train_ls[-1])
preds = net(test_features).asnumpy()
test_data['SalePrice'] = pd.Series(preds.reshape(1,-1)[0])
submission = pd.concat(test_data['Id',test_data['SalePrice']],axis = 1) #生成test_data的预测值文件
submission.to_csv('submission.csv',index=False)
def train_and_pred(train_features,test_features,train_labels,test_data,num_epochs,lr,weight_decay,batch_size):
net = get_net()
train_ls,_ =train(net,train_features,train_labels,None,None,num_epochs,lr,weight_decay,batch_size)
d2l.semilogy(range(1,num_epochs+1),train_ls,'epochs','rmse')
print("train rmse %f"%train_ls[-1])
preds = net(test_features).asnumpy()
test_data['SalePrice'] = pd.Series(preds.reshape(1,-1)[0])
submission = pd.concat([test_data["Id"],test_data["SalePrice"]],axis=1)
submission.to_csv("E:/Download/kaggle_house/submission.csv",index=False)
def train_and_pred(train_features,test_features,
train_labels,test_data,
num_epochs,lr,weight_decay,batch_size):
net = get_net()
train_ls,_ = train(net,train_features,train_labels,None,None,
num_epochs,lr,weight_decay,batch_size)
d2l.semilogy(range(1,num_epochs+1),train_ls,'epochs','rmse')
print('train rmse %f' %train_ls[-1])
# 直接将test_data的最后一列labels填上预测值，然后生成csv
preds = net(test_features).asnumpy()
test_data['SalePrice'] = pd.Series(preds.reshape(1,-1)[0])
submission = pd.concat([test_data['Id'],test_data['SalePrice']],axis=1)
submission.to_csv('submission.csv',index=False) # index的意思是，是否保留行索引
def pit_and_plot(lambd):
w,b = init_params() # 初始化模型参数
train_l, test_l = [], []
for _ in range(num_epochs):
for X,y in train_iter:
with autograd.record():
# 增加L2番薯惩罚
l = loss(net(X, w, b), y) + lambd*l2_penalty(w)
l.backward()
d2l.sgd([w,b], lr, batch_size)
train_l.append(loss(net(train_features, w, b), train_labels).mean().asscalar())
test_l.append(loss(net(test_features, w, b), test_labels).mean().asscalar())
d2l.semilogy(range(1, num_epochs+1), train_l, 'epochs', 'loss',
range(1, num_epochs+1), test_l, ['train', 'test'], figsize=(15, 5))
print('L2 norm of w:', w.norm().asscalar())
def fit_and_plot(lambd):
w, b = init_params()
train_ls, test_ls = [], []
for _ in range(num_epochs):
for X, y in train_iter:
with autograd.record():
l = loss(net(X, w, b), y) + lambd * l2_penalty(w)
l.backward()
d2l.sgd([w, b], lr, batch_size)
train_ls.append(
loss(net(train_features, w, b), train_labels).mean().asscalar())
test_ls.append(
loss(net(test_features, w, b), test_labels).mean().asscalar())
d2l.semilogy(range(1, num_epochs + 1), train_ls, "epochs", "loss",
range(1, num_epochs + 1), test_ls, ["train", "test"])
print("L2 norm of w", w.norm().asscalar())
def k_fold(k, x_train, y_train, num_epochs, batch_size, learning_rate,
weight_decay):
train_l_sum, valid_l_sum = 0, 0
for i in range(k):
data = get_k_fold_data(k, i, x_train, y_train)
net = get_net()
train_ls, valid_ls = train(net, *data, num_epochs, batch_size,
learning_rate, weight_decay)
train_l_sum += train_ls[-1]
valid_l_sum += train_ls[-1]
if (i == 0):
d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'rmse',
range(1, num_epochs + 1), valid_ls,
['train', 'valid'])
return train_l_sum / k, valid_l_sum / k
def fit_and_plot(lambd):
w, b = init_params()
train_ls, test_ls = [], []
for _ in range(num_epochs):
for X, y in train_iter:
with autograd.record():
# 添加了L2范数惩罚项，广播机制使其变成长度为batch_size的向量
l = loss(net(X, w, b), y) + lambd * l2_penalty(w)
l.backward()
d2l.sgd([w, b], lr, batch_size)
train_ls.append(
loss(net(train_features, w, b), train_labels).mean().asscalar())
test_ls.append(
loss(net(test_features, w, b), test_labels).mean().asscalar())
d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'loss',
range(1, num_epochs + 1), test_ls, ['train', 'test'])
print('L2 norm of w:', w.norm().asscalar())
def k_fold(k, x_train, y_train, num_epochs, learning_rate, weight_decay,
batch_size):
train_l_sum, valid_l_sum = 0, 0
for i in range(k): # k == 5
data = get_k_fold_data(k, i, x_train, y_train)
net = get_net()
train_ls, valid_ls = train(net, *data, num_epochs, learning_rate,
weight_decay, batch_size)
train_l_sum += train_ls[-1]
valid_l_sum += valid_ls[-1]
if i == 0:
d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'rmse',
range(1, num_epochs + 1), valid_ls,
['train', 'valid'])
print('fold %d, train rmse: %f, valid rmse: %f' %
(i, train_ls[-1], valid_ls[-1]))
return train_l_sum / k, valid_l_sum / k
def k_fold(k, X_train, y_train, num_epochs,
learning_rate, weight_decay, batch_size):
train_l_sum, valid_l_sum = 0, 0
for i in range(k):
data = get_k_fold_data(k, i, X_train, y_train)
net = get_net()
train_ls, valid_ls = train(net, *data, num_epochs, learning_rate, # *data data是一个列表，*data会分别开其中对应位置的元素赋给对应的函数参数
weight_decay, batch_size)
train_l_sum += train_ls[-1]
valid_l_sum += valid_ls[-1]
if i == 0:
d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'rmse', #semilogy绘图函数
range(1, num_epochs + 1), valid_ls,
['train', 'valid'])
print('fold %d, train rmse %f, valid rmse %f'
% (i, train_ls[-1], valid_ls[-1]))
return train_l_sum / k, valid_l_sum / k
def k_fold(k, X_train, y_train, num_epochs, learning_rate, weight_decay, batch_size):
train_l_sum, valid_l_sum = 0, 0
for i in range(k):
data = get_k_fold_data(k,i,X_train, y_train)
net = get_net()
train_ls, valid_ls = train(net, *data, num_epochs, learning_rate, weight_decay, batch_size)
train_l_sum += train_ls[-1]
valid_l_sum += valid_ls[-1]
if i == 0:
# """Plot x and log(y)."""
d2lzh.semilogy(
range(1, num_epochs+1), train_ls, "epoch", "rmse",
range(1, num_epochs+1), valid_ls, legend=["train", "valid"]
)
print("fold %d, train rmse %f, valid rmse %f" % (i, train_ls[-1], valid_ls[-1]))
return train_l_sum/k,valid_l_sum/k
def fit_and_plot(lambd):
w, b = init_params()
train_ls, test_ls = [], []
for _ in range(num_epochs):
for x, y in train_iter:
with autograd.record():
l = loss(net(x, w, b), y) + lambd * l2_penalty(w)
l.backward()
d2l.sgd([w, b], lr, batch_size)
train_ls.append(loss(net(train_features, w, b),
train_labels).mean().asscalar())
test_ls.append(loss(net(test_features, w, b),
test_labels).mean().asscalar())
print('true w: ', w.mean())
print('L2 norm of w:', w.norm().asscalar())
print('final epoch: train loss ', train_ls[-1], 'test loss', test_ls[-1])
d2l.semilogy(range(1, num_epochs+1), train_ls, 'epochs', 'loss',
range(1, num_epochs+1), test_ls, ['train', 'test'])
def k_fold(k,X_train,y_train,num_epochs, # 重点在于关乎data生成 --> 投入训练得到误差
learning_rate,weight_decay,batch_size): # 注意只投入训练数据！！
train_l_sum ,valid_l_sum = 0,0
# 共k次训练，每次训练又有epochs轮！！！
for i in range(k): # 准备训练，为了获得误差！！然后画图。
data = get_k_fold_data(k,i,X_train,y_train)
net = get_net()
# *data 前面有*是因为表示输入任意个！！
train_ls,valid_ls = train(net,*data,num_epochs,learning_rate,weight_decay,batch_size)
train_l_sum += train_ls[-1] # 取最后一个批次的损失,只是为了取损失！！
valid_l_sum += valid_ls[-1]
if i==0: # 如果是首个k-fold,就画图.别问为啥，再问自己改
print('i draw once!!!')
d2l.semilogy(range(1,num_epochs+1),train_ls,'epochs','rmse',
range(1,num_epochs+1),valid_ls,
['train','valid']) # 作图函数，p62
print('fold %d ,train rmse %f,valid rmse %f' %(i,train_ls[-1],valid_ls[-1]))
return train_l_sum / k ,valid_l_sum / k
def train_and_pred(train_features, test_features, train_labels, test_data,
num_epochs, lr, weight_decay, batch_size):
net = get_net()
###########################
# 用k_fold方法进行训练
###########################
# train_l, valid_l = k_fold(k, train_features, train_labels, num_epochs, lr, weight_decay, batch_size)
# print('%d-fold validation: avg train rmse %f, avg valid rmse %f' % (k, train_l, valid_l))
###########################
# 用普通方法进行训练
###########################
train_ls, _ = train(net, train_features, train_labels, None, None,
num_epochs, lr, weight_decay, batch_size)
d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'rmse')
print('train rmse %f' % train_ls[-1])
preds = net(test_features).asnumpy()
test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])
submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis=1)
submission.to_csv('submission.csv', index=False)
def fit_and_plot(lambd):
# w <NDArray 200x1 @cpu(0)>
# b <NDArray 1x1 @cpu(0)>
w, b = init_params()
train_ls, test_ls = [], []
for _ in range(num_epochs):
# 训练100次（梯度下降100次)
for X, y in train_iter:
# 训练批次为20，每一次取出一个训练，一共训练20次
# X <NDArray 1x200 @cpu(0)>
# y <NDArray 1x1 @cpu(0)>
with autograd.record():
# 添加了L2范数惩罚项，广播机制使其变成长度为batch_size的向量
# loss + λ / 2 * ||w||2
#
# L2范数的平方
#
# 带惩罚函数的损失
# 惩罚函数
# λ / 2 * (∥w∥ ** 2)
# l2_penalty = (w ** 2).sum() / 2
# lambd * l2_penalty(w) = lambd / 2 * ((w ** 2).sum())
# 损失计算
# nd.dot(X, w) + b
# X - <NDArray 1x200 @cpu(0)>
# w - <NDArray 200x1 @cpu(0)>
# b - <NDArray 1 @cpu(0)>
netResult = net(X, w, b)
lossO = loss(netResult, y)
l = lossO + lambd * l2_penalty(w)
# l = loss(net(X, w, b), y) + lambd * l2_penalty(w)
# 计算梯度
l.backward()
# batch_size 1
# lr 0.003
# [w, b] [200 x 1, 1 x 1]
# 梯度下降
d2l.sgd([w, b], lr, batch_size)
# train_features * w + b = y
# 20x200 200x1 1x1 20x1
train_ls.append(
loss(net(train_features, w, b), train_labels).mean().asscalar())
test_ls.append(
loss(net(test_features, w, b), test_labels).mean().asscalar())
# 100次的训练，每次训练数据的损失都在降低，但是测试数据的损失一直很高
d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'loss',
range(1, num_epochs + 1), test_ls, ['train', 'test'])
# --------------------normTest--------------------
# TTT1 = nd.array((1,2,3,4)).reshape((1, 4))
# 1 + 4 + 9 + 16 = 30
# TTT2 = TTT1.norm().asscalar()
# --------------------normTest--------------------
print('L2 norm of w:', w.norm().asscalar())