def __init__(self, input_model, config):
AbstractModelParser.__init__(self, input_model, config)
self._layer_dict = {'DenseLayer': 'Dense',
'Conv2DLayer': 'Conv2D',
'Conv2DDNNLayer': 'Conv2D',
'MaxPool2DLayer': 'MaxPooling2D',
'Pool2DLayer': 'AveragePooling2D',
'DropoutLayer': 'Dropout',
'FlattenLayer': 'Flatten',
'BatchNormLayer': 'BatchNormalization',
'NonlinearityLayer': 'Activation',
'ConcatLayer': 'Concatenate',
'GlobalPoolLayer': 'GlobalAveragePooling2D'}
self.activation_dict = {'rectify': 'relu',
'softmax': 'softmax',
'binary_tanh_unit': 'binary_tanh',
'binary_sigmoid_unit': 'binary_sigmoid',
'linear': 'linear'}
def __init__(self, input_model, config):
AbstractModelParser.__init__(self, input_model, config)
self._layer_dict = {
'InnerProduct': 'Dense',
'Convolution': 'Conv2D',
'MaxPooling2D': 'MaxPooling2D',
'AveragePooling2D': 'AveragePooling2D',
'ReLU': 'Activation',
'Softmax': 'Activation',
'Concat': 'Concatenate',
'LPInnerProduct': 'Dense',
'LPConvolution': 'Conv2D',
'LPAct': 'Activation'
}
self.activation_dict = {
'ReLU': 'relu',
'Softmax': 'softmax',
'Sigmoid': 'sigmoid',
'LPAct': 'relu'
}
def initialize_attributes(self, layer=None):
attributes = AbstractModelParser.initialize_attributes(self)
attributes.update(layer.get_config())
return attributes