示例#1
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 def forward(self):
     # A stable calculation, xent(sigmoid(x)) = (1 - t) + log(1 + exp(-x))
     self._input[1]._result = numeric.sigmoid(self._input[0]._value)
     labels = self._input[1]._value
     xent = (1 - labels.astype(DTYPE)) * self._input[0]._value + numeric.log1pexp(-self._input[0]._value)
     self._value = (np.sum(xent) / DTYPE(xent.size)).reshape(1)
     self._gradient = np.zeros(self._value.shape, dtype=DTYPE)
示例#2
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 def forward(self):
     # A stable calculation, xent(sigmoid(x)) = (1 - t) + log(1 + exp(-x))
     self._input[1]._result = numeric.sigmoid(self._input[0]._value)
     labels = self._input[1]._value
     xent = (1 - labels.astype(DTYPE)) * self._input[0]._value + numeric.log1pexp(-self._input[0]._value)
     self._value = (np.sum(xent) / DTYPE(xent.size)).reshape(1)
     self._gradient = np.zeros(self._value.shape, dtype=DTYPE)
示例#3
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 def forward(self):
     self._value = 2 * numeric.sigmoid(2 * self._input[0]._value) - 1 # tanh = 2 sig(2x) - 1
     self._gradient = np.zeros(self._value.shape, dtype=DTYPE)
示例#4
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 def forward(self):
     self._value = numeric.sigmoid(self._input[0]._value)
     self._gradient = np.zeros(self._value.shape, dtype=DTYPE)
示例#5
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 def forward(self):
     self._value = 2 * numeric.sigmoid(2 * self._input[0]._value) - 1 # tanh = 2 sig(2x) - 1
     self._gradient = np.zeros(self._value.shape, dtype=DTYPE)
示例#6
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 def forward(self):
     self._value = numeric.sigmoid(self._input[0]._value)
     self._gradient = np.zeros(self._value.shape, dtype=DTYPE)