class MultiConv(Layer):
def __init__(self,
convs,
channelsAxis=-1,
trainable=True,
name=None,
dtype=None):
super().__init__(trainable, name, dtype)
self.convs = convs
self.concat = Concatenate(channelsAxis)
def build(self, input_shape):
for conv in self.convs:
conv.build(input_shape)
self.concat.build(
[conv.compute_output_shape(input_shape) for conv in self.convs])
super().build(input_shape)
def call(self, inputs, **kwargs):
appliedConvs = []
for conv in self.convs:
appliedConvs.append(conv.call(inputs))
return self.concat.call(appliedConvs)
def build(self, input_shape):
with K.name_scope(
self.name
): # name scope used to make sure weights get unique names
self.conv_layers = []
self.residual_layers = []
name = 'conv1D_bottleneck_0'
with K.name_scope(
name
): # name scope used to make sure weights get unique names
self.bot_layers = Conv1D(filters=self.nb_filters,
kernel_size=1,
strides=self.strides,
padding=self.padding,
use_bias=False,
name=name)
self.bot_layers.build(input_shape)
bot_output = self.bot_layers.compute_output_shape(input_shape)
conv_output_shape = []
self._build_conv(bot_output, output_shape=conv_output_shape)
output_shape = self._build_residual(input_shape)
conv_output_shape.append(output_shape)
concate_layer = Concatenate(axis=2)
concate_layer.build(conv_output_shape)
output_shape = concate_layer.compute_output_shape(
conv_output_shape)
with K.name_scope('norm_0'):
self.batchnorm = BatchNormalization()
self.batchnorm.build(output_shape)
super(inception, self).build(input_shape)
