def __call__(self, input, filter=(2, 2), strides=(2, 2)):
new_node = Op.__call__(self)
new_node.inputs = [input]
new_node.filter = filter
new_node.strides = strides
new_node.cache = {}
new_node.name = 'MaxPoolOp({})'.format(input.name)
return new_node
def __call__(self, input, filter, bias, strides=(1, 1), padding=(0, 0)):
new_node = Op.__call__(self)
# input: 4-D data, (batch_size, depth, height, width)
# filter: 4-D kernel (num_filters, depth, kernel_height, kernel_width)
new_node.inputs = [input, filter, bias]
new_node.strides = strides
new_node.padding = padding
new_node.name = 'Conv2d({0:s}, {1:s})'.format(input.name, filter.name)
return new_node
def __call__(self,
node_A,
node_B,
output_grad,
strides=(1, 1),
padding=(0, 0)):
new_node = Op.__call__(self)
new_node.inputs = [node_A, node_B, output_grad]
new_node.strides = strides
new_node.padding = padding
new_node.name = "Conv2dBackwardFilter(%s, %s)" % (node_A.name,
node_B.name)
return new_node
def __call__(self,
node_A,
node_B,
filter=(2, 2),
strides=(2, 2),
cache=None):
new_node = Op.__call__(self)
# node_B is the output_grad
new_node.inputs = [node_A, node_B]
new_node.filter = filter
new_node.strides = strides
new_node.cache = cache
new_node.name = 'MaxPoolGradientOp(%s)' % (node_A.name)
return new_node
def __call__(self, node_A):
new_node = Op.__call__(self)
new_node.inputs = [node_A]
new_node.name = 'Tanh({0:s})'.format(node_A.name)
return new_node
def __call__(self, node_A):
new_node = Op.__call__(self)
new_node.inputs = [node_A]
new_node.name = "Conv2dBackwardBiase(%s)" % (node_A.name)
return new_node
def __call__(self, node_A, node_B):
"""node_B is output_grad"""
new_node = Op.__call__(self)
new_node.inputs = [node_A, node_B]
new_node.name = "ReluGradient(%s)" % (node_A.name)
return new_node
def __call__(self, node_A):
new_node = Op.__call__(self)
new_node.inputs = [node_A]
new_node.name = "Relu(%s)" % (node_A.name)
return new_node
def __call__(self, node_A, node_B):
new_node = Op.__call__(self)
new_node.inputs = [node_A, node_B]
new_node.name = 'CrossEntropy({0:s}, {1:s})'.format(
node_A.name, node_B.name)
return new_node