def serialize(self):
"""Serializes the layer for use with the transformer server.
"""
serialized = transformer_pb.Layer()
serialized.maxpool_data.window_data.CopyFrom(
self.window_data.serialize())
return serialized
def serialize(self):
"""Serializes the layer for the transformer server.
"""
serialized = transformer_pb.Layer()
serialized.concat_data.layers.extend(
[input_layer.serialize() for input_layer in self.input_layers])
serialized.concat_data.concat_along = self.concat_along.serialize()
return serialized
def serialize(self):
"""Serializes the layer for use with the transformer server.
"""
serialized = transformer_pb.Layer()
serialized.fullyconnected_data.weights.extend(
list(self.weights.numpy().flatten()))
serialized.fullyconnected_data.biases.extend(
list(self.biases.numpy().flatten()))
return serialized
def serialize(self):
"""Serializes the layer for use with the transformer server.
"""
serialized = transformer_pb.Layer()
serialized.normalize_data.means.extend(
list(self.means.numpy().flatten()))
serialized.normalize_data.standard_deviations.extend(
list(self.standard_deviations.numpy().flatten()))
return serialized
def serialize(self):
"""Serializes the layer for use with the transformer server.
"""
serialized = transformer_pb.Layer()
filters = self.filter_weights.numpy()
biases = self.biases.numpy()
conv2d_data = serialized.conv2d_data
conv2d_data.window_data.CopyFrom(self.window_data.serialize())
conv2d_data.filters.extend(list(filters.flatten()))
serialized.conv2d_data.biases.extend(list(biases.flatten()))
return serialized
def serialize(self):
"""Serializes the layer for use with the transformer server.
"""
serialized = transformer_pb.Layer()
serialized.hard_tanh_data.SetInParent()
return serialized
def serialize(self):
"""Serializes the layer for the transformer server.
"""
serialized = transformer_pb.Layer()
serialized.argmax_data.SetInParent()
return serialized