Python Layer.convConvForward 예제들

예제 #1

파일: PoolingLayer.py 프로젝트: mil-tokyo/neural_network

	def forward(self, _units):
		self._units, self._pooled_inds = self.maxPool(_units)
		self._outputs = self.activate(self._units)

		if (not self._child == None):
			if self._child.type() == 'convolutional':
				next_units = Layer.convConvForward(self._weights, self._outputs, self._child.getUnits().shape)
				self._child.forward(next_units)
			elif self._child.type() == 'fullyconnected':
				self._child.forward(np.dot(self._outputs.flatten(), self._weights) + self._bias)

예제 #2

파일: ConvolutionalLayer.py 프로젝트: mil-tokyo/neural_network

	def forward(self, _units):
		self._units = _units
		self._outputs = self.activate(self._units)

		if (not self._child == None):
			if self._child.type() == 'convolutional':
				next_units = Layer.convConvForward(self._weights, self._units, self._child.getUnits().shape)
				self._child.forward(next_units)

			elif self._child.type() == 'pooling':
				self._child.forward(self._units)