def to_conv_deeper_model(self, target_id, kernel_size):
"""Insert a relu-conv-bn block after the target block.
Args:
target_id: A convolutional layer ID. The new block should be inserted after the block.
kernel_size: An integer. The kernel size of the new convolutional layer.
"""
self.operation_history.append(('to_conv_deeper_model', target_id, kernel_size))
target = self.layer_list[target_id]
new_layers = deeper_conv_block(target, kernel_size, self.weighted)
output_id = self._conv_block_end_node(target_id)
self._insert_new_layers(new_layers, output_id)
def to_conv_deeper_model(self, target_id, kernel_size):
"""Insert a relu-conv-bn block after the target block.
Args:
target_id: A convolutional layer ID. The new block should be inserted after the block.
kernel_size: An integer. The kernel size of the new convolutional layer.
"""
self.operation_history.append(('to_conv_deeper_model', target_id, kernel_size))
target = self.layer_list[target_id]
new_layers = deeper_conv_block(target, kernel_size, self.weighted)
output_id = self._conv_block_end_node(target_id)
self._insert_new_layers(new_layers, output_id)
def to_conv_deeper_model(self, target_id, kernel_size):
"""Insert a convolution, batch-normalization, relu block after the target block.
Args:
target_id: A convolutional layer ID. The new block should be inserted after the relu layer
in its conv-batch-relu block.
kernel_size: An integer. The kernel size of the new convolutional layer.
Returns:
A new Keras model with the inserted block.
"""
self.operation_history.append(
('to_conv_deeper_model', target_id, kernel_size))
target = self.layer_list[target_id]
new_layers = deeper_conv_block(target, kernel_size, self.weighted)
output_id = self._conv_block_end_node(target_id)
self._insert_new_layers(new_layers, output_id)
def _deeper_conv_block(self, target, kernel_size):
return deeper_conv_block(target, kernel_size)