def __init__(self, n_output_node, input_shape): super(MobileNetV2Generator, self).__init__(n_output_node, input_shape) """ configuration for complete net: self.cfg = [(1, 16, 1, 1), (6, 24, 2, 1) , # NOTE: change stride 2 -> 1 for CIFAR10 (6, 32, 3, 2), (6, 64, 4, 2), (6, 96, 3, 1), (6, 160, 3, 2), (6, 320, 1, 1)] """ # we try smaller net configuration (so autokeras will be able to expand the net) self.cfg = [(1, 16, 1, 1), (6, 24, 2, 1)] # , # NOTE: change stride 2 -> 1 for CIFAR10 #(6, 32, 3, 2) , #(6, 64, 4, 2), #(6, 96, 3, 1), #(6, 160, 3, 2), #(6, 320, 1, 1)] self.in_planes = 32 self.block_expansion = 1 self.n_dim = len(self.input_shape) - 1 if len(self.input_shape) > 4: raise ValueError('The input dimension is too high.') elif len(self.input_shape) < 2: raise ValueError('The input dimension is too low.') self.conv = get_conv_class(self.n_dim) self.dropout = get_dropout_class(self.n_dim) self.global_avg_pooling = get_global_avg_pooling_class(self.n_dim) self.adaptive_avg_pooling = get_global_avg_pooling_class(self.n_dim) self.batch_norm = get_batch_norm_class(self.n_dim)
def __init__(self, n_output_node, input_shape): super(ResNetGenerator, self).__init__(n_output_node, input_shape) # self.layers = [2, 2, 2, 2] self.in_planes = 64 self.block_expansion = 1 self.n_dim = len(self.input_shape) - 1 if len(self.input_shape) > 4: raise ValueError('The input dimension is too high.') elif len(self.input_shape) < 2: raise ValueError('The input dimension is too low.') self.conv = get_conv_class(self.n_dim) self.dropout = get_dropout_class(self.n_dim) self.global_avg_pooling = get_global_avg_pooling_class(self.n_dim) self.adaptive_avg_pooling = get_global_avg_pooling_class(self.n_dim) self.batch_norm = get_batch_norm_class(self.n_dim)
def __init__(self, n_output_node, input_shape): super().__init__(n_output_node, input_shape) # DenseNet Constant self.num_init_features = 64 self.growth_rate = 32 self.block_config = (6, 12, 24, 16) self.bn_size = 4 self.drop_rate = 0 # Stub layers self.n_dim = len(self.input_shape) - 1 self.conv = get_conv_class(self.n_dim) self.dropout = get_dropout_class(self.n_dim) self.global_avg_pooling = get_global_avg_pooling_class(self.n_dim) self.adaptive_avg_pooling = get_global_avg_pooling_class(self.n_dim) self.max_pooling = get_pooling_class(self.n_dim) self.avg_pooling = get_avg_pooling_class(self.n_dim) self.batch_norm = get_batch_norm_class(self.n_dim)
def __init__(self, n_output_node, input_shape): super(CnnGenerator, self).__init__(n_output_node, input_shape) self.n_dim = len(self.input_shape) - 1 if len(self.input_shape) > 4: raise ValueError('The input dimension is too high.') if len(self.input_shape) < 2: raise ValueError('The input dimension is too low.') self.conv = get_conv_class(self.n_dim) self.dropout = get_dropout_class(self.n_dim) self.global_avg_pooling = get_global_avg_pooling_class(self.n_dim) self.pooling = get_pooling_class(self.n_dim) self.batch_norm = get_batch_norm_class(self.n_dim)
def __init__(self, n_output_node, input_shape): """Initialize the instance. Args: n_output_node: An integer. Number of output nodes in the network. input_shape: A tuple. Input shape of the network. """ super(CnnGenerator, self).__init__(n_output_node, input_shape) self.n_dim = len(self.input_shape) - 1 if len(self.input_shape) > 4: raise ValueError('The input dimension is too high.') if len(self.input_shape) < 2: raise ValueError('The input dimension is too low.') self.conv = get_conv_class(self.n_dim) self.dropout = get_dropout_class(self.n_dim) self.global_avg_pooling = get_global_avg_pooling_class(self.n_dim) self.pooling = get_pooling_class(self.n_dim) self.batch_norm = get_batch_norm_class(self.n_dim)