def parse_config_cnn(self, arguments, nnet_spec, conv_nnet_spec):
self.parse_config_dnn(arguments, nnet_spec)
# parse convolutional layer structure
self.conv_layer_configs = parse_conv_spec(conv_nnet_spec, self.batch_size)
# parse convolutional layer activation
# parse activation function, including maxout
if arguments.has_key('conv_activation'):
self.conv_activation_text = arguments['conv_activation']
self.conv_activation = parse_activation(arguments['conv_activation'])
# maxout not supported yet
# whether we use the fast version of convolution
if arguments.has_key('use_fast'):
self.use_fast = string2bool(arguments['use_fast'])
def parse_config_cnn(self, arguments, nnet_spec, conv_nnet_spec):
self.parse_config_dnn(arguments, nnet_spec)
# parse convolutional layer structure
self.conv_layer_configs = parse_conv_spec(conv_nnet_spec, self.batch_size)
# parse convolutional layer activation
# parse activation function, including maxout
if arguments.has_key('conv_activation'):
self.conv_activation_text = arguments['conv_activation']
self.conv_activation = parse_activation(arguments['conv_activation'])
# maxout not supported yet
# whether we use the fast version of convolution
if arguments.has_key('use_fast'):
self.use_fast = string_2_bool(arguments['use_fast'])
def parse_config_cldnn(self, arguments, nnet_spec, conv_nnet_spec, lstm_nnet_spec):
self.parse_config_common(arguments)
# parse CNN network structure
self.conv_layer_configs = parse_conv_spec(conv_nnet_spec, self.batch_size)
if arguments.has_key('conv_activation'):
self.conv_activation_text = arguments['conv_activation']
self.conv_activation = parse_activation(arguments['conv_activation'])
if arguments.has_key('use_fast'):
self.use_fast = string_2_bool(arguments['use_fast'])
# parse LSTM network structure
lstm_layers = lstm_nnet_spec.split(':')
self.lstm_layers_sizes = [int(lstm_layers[i]) for i in range(0, len(lstm_layers))]
# parse DNN network structure
fc_layers = nnet_spec.split(':')
self.hidden_layers_sizes = [int(fc_layers[i]) for i in range(0, len(fc_layers)-1)]
self.n_outs = int(fc_layers[-1])