注：安裝pytorch時(shí)，首先選擇用anaconda的方法安裝，但是因?yàn)閜ycharm在工程設(shè)置的時(shí)候總會(huì)遇到一點(diǎn)問題，所以就沒有安裝anaconda，而是安裝python3.5.4+TensorFlow-gpu+cuda8.0之后再根據(jù)pytorch官網(wǎng)提供的方法安裝pytorch。安裝完pytorch之后，使用import torch 發(fā)現(xiàn)numpy的版本過低，這時(shí)候需要關(guān)閉python。使用pip install --upgrade numpy 進(jìn)行升級(jí)，如果遇到權(quán)限問題，需在管理員cmd下安裝。
import?numpy?as?np
import?torch
inputs?=?np.array([[73,?67,?43],
[91,?88,?64],
[87,?134,?58],
[102,?43,?37],
[69,?96,?70]],?dtype='float32')
targets?=?np.array([[56,?70],
[81,?101],
[119,?133],
[22,?37],
[103,?119]],?dtype='float32')
inputs?=?torch.from_numpy(inputs)
targets?=?torch.from_numpy(targets)
print(inputs)
print(targets)
w?=?torch.randn(2,?3,?requires_grad=True)
b?=?torch.randn(2,?requires_grad=True)
print(w)
print(b)
def?model(x):
return?x?@?w.t()?+?b
preds?=?model(inputs)
print(preds)
def?mse(t1,?t2):
diff?=?t1?-?t2
return?torch.sum(diff*diff)/diff.numel()
loss?=?mse(preds,?targets)
print(loss)
loss.backward()
print(w)
print(w.grad)
w.grad.zero_()
b.grad.zero_()
print(w.grad)
print(b.grad)
preds?=?model(inputs)
print(preds)
loss?=?mse(preds,?targets)
print(loss)
loss.backward()
print(w.grad)
print(b.grad)
with?torch.no_grad():
w?-=?w.grad*1e-5
b?-=?b.grad*1e-5
w.grad.zero_()
b.grad.zero_()
print(w)
print(b)
preds?=?model(inputs)
loss?=?mse(preds,?targets)
print(loss)
for?i?in?range(1000):
preds?=?model(inputs)
loss?=?mse(preds,?targets)
loss.backward()
with?torch.no_grad():
w?-=?w.grad?*?1e-5
b?-=?b.grad?*?1e-5
w.grad.zero_()
b.grad.zero_()
preds?=?model(inputs)
loss?=?mse(preds,?targets)
print(loss)
print(preds)
