wehub-resource-sync
58 lines
1.6 KiB
Python
Executable File
58 lines
1.6 KiB
Python
Executable File
import numpy as np
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from prml.nn.tensor.constant import Constant
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from prml.nn.tensor.tensor import Tensor
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from prml.nn.function import Function
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from prml.nn.array.broadcast import broadcast_to
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class Power(Function):
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"""
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First array elements raised to powers from second array
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"""
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def _check_input(self, x, y):
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x = self._convert2tensor(x)
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y = self._convert2tensor(y)
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if x.shape != y.shape:
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shape = np.broadcast(x.value, y.value).shape
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if x.shape != shape:
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x = broadcast_to(x, shape)
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if y.shape != shape:
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y = broadcast_to(y, shape)
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return x, y
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def forward(self, x, y):
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x, y = self._check_input(x, y)
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self.x = x
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self.y = y
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self.output = np.power(x.value, y.value)
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if isinstance(self.x, Constant) and isinstance(self.y, Constant):
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return Constant(self.output)
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return Tensor(self.output, function=self)
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def backward(self, delta):
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dx = self.y.value * np.power(self.x.value, self.y.value - 1) * delta
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if self.x.size == 1:
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if self.x.value > 0:
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dy = self.output * np.log(self.x.value) * delta
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else:
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dy = None
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else:
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if (self.x.value > 0).all():
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dy = self.output * np.log(self.x.value) * delta
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else:
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dy = None
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self.x.backward(dx)
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self.y.backward(dy)
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def power(x, y):
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"""
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First array elements raised to powers from second array
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"""
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return Power().forward(x, y)
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def rpower(x, y):
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return Power().forward(y, x)
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