import pytensor
from pytensor import tensor as pt
# 声明2个符号浮点标量
a = pt.dscalar("a")
b = pt.dscalar("b")
# 建立一个简单的表达式
c = a + b
# 将这个表达式转换成一个可调用对象,
# 它接收'(a, b)'值作为输入并计算出一个值给'c'
f_c = pytensor.function([a, b], c)
assert f_c(1.5, 2.5) == 4.0
# 计算样例表达式关于'a'的梯度
dc = pytensor.grad(c, a)
f_dc = pytensor.function([a, b], dc)
assert f_dc(1.5, 2.5) == 1.0
>>> import pytensor
>>> from pytensor import tensor as pt
>>>
>>> # 通过'pytensor.function'编译函数还能优化表达式图
>>> # 它会移除不必要的运算并将特定运算替代为更有效的运算
>>>
>>> v = pt.vector("v")
>>> M = pt.matrix("M")
>>>
>>> d = a/a + (M + a).dot(v)
>>>
>>> pytensor.dprint(d)
Add [id A]
├─ ExpandDims{axis=0} [id B]
│ └─ True_div [id C]
│ ├─ a [id D]
│ └─ a [id D]
└─ dot [id E]
├─ Add [id F]
│ ├─ M [id G]
│ └─ ExpandDims{axes=[0, 1]} [id H]
│ └─ a [id D]
└─ v [id I]
<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>
>>>
>>> f_d = pytensor.function([a, v, M], d)
>>>
>>> # 'a/a' -> '1'而点积被替代为BLAS函数(i.e. CGemv)
>>> pytensor.dprint(f_d)
Add [id A] 5
├─ [1.] [id B]
└─ CGemv{inplace} [id C] 4
├─ AllocEmpty{dtype='float64'} [id D] 3
│ └─ Shape_i{0} [id E] 2
│ └─ M [id F]
├─ 1.0 [id G]
├─ Add [id H] 1
│ ├─ M [id F]
│ └─ ExpandDims{axes=[0, 1]} [id I] 0
│ └─ a [id J]
├─ v [id K]
└─ 0.0 [id L]
<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>