Python as_symbols Examples

Example #1

def max_pooling_2d(data, size: tuple, step: tuple, reference=None):
    if reference is None:
        from paradox.neural_network.convolutional_neural_network.operator import MaxPooling2D
        return Symbol(operator=MaxPooling2D(size, step), inputs=as_symbols([data]), category=SymbolCategory.operator)
    else:
        from paradox.neural_network.convolutional_neural_network.operator import MaxReferencePooling2D
        return Symbol(operator=MaxReferencePooling2D(size, step), inputs=as_symbols([data, reference]), category=SymbolCategory.operator)

Example #2

def max_pooling_nd(data,
                   size: tuple,
                   step: tuple,
                   dimension: int,
                   reference=None):
    if reference is None:
        from paradox.neural_network.convolutional_neural_network.operator import MaxPoolingND
        return Symbol(operator=MaxPoolingND(dimension, size, step),
                      inputs=as_symbols([data]))
    else:
        from paradox.neural_network.convolutional_neural_network.operator import MaxReferencePoolingND
        return Symbol(operator=MaxReferencePoolingND(dimension, size, step),
                      inputs=as_symbols([data, reference]))

Example #3

def convolution_nd(data,
                   kernel,
                   dimension: int,
                   mode,
                   element_wise: bool = False):
    from paradox.neural_network.convolutional_neural_network.operator import ConvolutionND
    return Symbol(operator=ConvolutionND(dimension, mode, element_wise),
                  inputs=as_symbols([data, kernel]))

Example #4

def average_unpooling_nd(pooling,
                         size: tuple,
                         step: tuple,
                         dimension: int,
                         unpooling_size: int = None):
    from paradox.neural_network.convolutional_neural_network.operator import AverageUnpoolingND
    return Symbol(operator=AverageUnpoolingND(dimension, size, step,
                                              unpooling_size),
                  inputs=as_symbols([pooling]))

Example #5

def average_unpooling_2d(pooling, size: tuple, step: tuple, unpooling_size: tuple=None):
    from paradox.neural_network.convolutional_neural_network.operator import AverageUnpooling2D
    return Symbol(operator=AverageUnpooling2D(size, step, unpooling_size), inputs=as_symbols([pooling]), category=SymbolCategory.operator)

Example #6

def average_pooling_2d(data, size: tuple, step: tuple):
    from paradox.neural_network.convolutional_neural_network.operator import AveragePooling2D
    return Symbol(operator=AveragePooling2D(size, step), inputs=as_symbols([data]), category=SymbolCategory.operator)

Example #7

def max_unpooling_2d(data, pooling, size: tuple, step: tuple):
    from paradox.neural_network.convolutional_neural_network.operator import MaxUnpooling2D
    return Symbol(operator=MaxUnpooling2D(size, step), inputs=as_symbols([data, pooling]), category=SymbolCategory.operator)

Example #8

def average_pooling_nd(data, size: tuple, step: tuple, dimension: int):
    from paradox.neural_network.convolutional_neural_network.operator import AveragePoolingND
    return Symbol(operator=AveragePoolingND(dimension, size, step),
                  inputs=as_symbols([data]))

Example #9

def max_unpooling_nd(data, pooling, size: tuple, step: tuple, dimension: int):
    from paradox.neural_network.convolutional_neural_network.operator import MaxUnpoolingND
    return Symbol(operator=MaxUnpoolingND(dimension, size, step),
                  inputs=as_symbols([data, pooling]))