def _build_computation_graph(self): ###################### BUILD NETWORK ########################## # whether or not to mirror the input images before feeding them into the network if self.flag_datalayer: layer_1_input = mirror_images(input=self.x, image_shape=(self.batch_size, 3, 256, 256), # bc01 format cropsize=227, rand=self.rand, flag_rand=self.rand_crop) else: layer_1_input = self.x # 4D tensor (going to be in bc01 format) # Start with 5 convolutional pooling layers log.debug("convpool layer 1...") convpool_layer1 = ConvPoolLayer(inputs_hook=((self.batch_size, 3, 227, 227), layer_1_input), filter_shape=(96, 3, 11, 11), convstride=4, padsize=0, group=1, poolsize=3, poolstride=2, bias_init=0.0, local_response_normalization=True) # Add this layer's parameters! self.params += convpool_layer1.get_params() log.debug("convpool layer 2...") convpool_layer2 = ConvPoolLayer(inputs_hook=((self.batch_size, 96, 27, 27, ), convpool_layer1.get_outputs()), filter_shape=(256, 96, 5, 5), convstride=1, padsize=2, group=2, poolsize=3, poolstride=2, bias_init=0.1, local_response_normalization=True) # Add this layer's parameters! self.params += convpool_layer2.get_params() log.debug("convpool layer 3...") convpool_layer3 = ConvPoolLayer(inputs_hook=((self.batch_size, 256, 13, 13), convpool_layer2.get_outputs()), filter_shape=(384, 256, 3, 3), convstride=1, padsize=1, group=1, poolsize=1, poolstride=0, bias_init=0.0, local_response_normalization=False) # Add this layer's parameters! self.params += convpool_layer3.get_params() log.debug("convpool layer 4...") convpool_layer4 = ConvPoolLayer(inputs_hook=((self.batch_size, 384, 13, 13), convpool_layer3.get_outputs()), filter_shape=(384, 384, 3, 3), convstride=1, padsize=1, group=2, poolsize=1, poolstride=0, bias_init=0.1, local_response_normalization=False) # Add this layer's parameters! self.params += convpool_layer4.get_params() log.debug("convpool layer 5...") convpool_layer5 = ConvPoolLayer(inputs_hook=((self.batch_size, 384, 13, 13), convpool_layer4.get_outputs()), filter_shape=(256, 384, 3, 3), convstride=1, padsize=1, group=2, poolsize=3, poolstride=2, bias_init=0.0, local_response_normalization=False) # Add this layer's parameters! self.params += convpool_layer5.get_params() # Now onto the fully-connected layers! fc_config = { 'activation': 'rectifier', # type of activation function to use for output 'weights_init': 'gaussian', # either 'gaussian' or 'uniform' - how to initialize weights 'weights_mean': 0.0, # mean for gaussian weights init 'weights_std': 0.005, # standard deviation for gaussian weights init 'bias_init': 0.0 # how to initialize the bias parameter } log.debug("fully connected layer 1 (model layer 6)...") # we want to have dropout applied to the training version, but not the test version. fc_layer6_input = T.flatten(convpool_layer5.get_outputs(), 2) fc_layer6 = BasicLayer(inputs_hook=(9216, fc_layer6_input), output_size=4096, noise='dropout', noise_level=0.5, **fc_config) # Add this layer's parameters! self.params += fc_layer6.get_params() # Add the dropout noise switch self.noise_switches += fc_layer6.get_noise_switch() log.debug("fully connected layer 2 (model layer 7)...") fc_layer7 = BasicLayer(inputs_hook=(4096, fc_layer6.get_outputs()), output_size=4096, noise='dropout', noise_level=0.5, **fc_config) # Add this layer's parameters! self.params += fc_layer7.get_params() # Add the dropout noise switch self.noise_switches += fc_layer7.get_noise_switch() # last layer is a softmax prediction output layer softmax_config = { 'weights_init': 'gaussian', 'weights_mean': 0.0, 'weights_std': 0.005, 'bias_init': 0.0 } log.debug("softmax classification layer (model layer 8)...") softmax_layer8 = SoftmaxLayer(inputs_hook=(4096, fc_layer7.get_outputs()), output_size=1000, **softmax_config) # Add this layer's parameters! self.params += softmax_layer8.get_params() # finally the softmax output from the whole thing! self.output = softmax_layer8.get_outputs() self.targets = softmax_layer8.get_targets() ##################### # Cost and monitors # ##################### self.train_cost = softmax_layer8.negative_log_likelihood() cost = softmax_layer8.negative_log_likelihood() errors = softmax_layer8.errors() train_errors = softmax_layer8.errors() self.monitors = OrderedDict([('cost', cost), ('errors', errors), ('dropout_errors', train_errors)]) ######################### # Compile the functions # ######################### log.debug("Compiling functions!") t = time.time() log.debug("f_run...") # use the actual argmax from the classification self.f_run = function(inputs=[self.x], outputs=softmax_layer8.get_argmax_prediction()) log.debug("compilation took %s", make_time_units_string(time.time() - t))
def _build_computation_graph(self): ###################### BUILD NETWORK ########################## # whether or not to mirror the input images before feeding them into the network if self.flag_datalayer: layer_1_input = mirror_images( input=self.x, image_shape=(self.batch_size, 3, 256, 256), # bc01 format cropsize=227, rand=self.rand, flag_rand=self.rand_crop) else: layer_1_input = self.x # 4D tensor (going to be in bc01 format) # Start with 5 convolutional pooling layers log.debug("convpool layer 1...") convpool_layer1 = ConvPoolLayer(inputs_hook=((self.batch_size, 3, 227, 227), layer_1_input), filter_shape=(96, 3, 11, 11), convstride=4, padsize=0, group=1, poolsize=3, poolstride=2, bias_init=0.0, local_response_normalization=True) # Add this layer's parameters! self.params += convpool_layer1.get_params() log.debug("convpool layer 2...") convpool_layer2 = ConvPoolLayer(inputs_hook=(( self.batch_size, 96, 27, 27, ), convpool_layer1.get_outputs()), filter_shape=(256, 96, 5, 5), convstride=1, padsize=2, group=2, poolsize=3, poolstride=2, bias_init=0.1, local_response_normalization=True) # Add this layer's parameters! self.params += convpool_layer2.get_params() log.debug("convpool layer 3...") convpool_layer3 = ConvPoolLayer( inputs_hook=((self.batch_size, 256, 13, 13), convpool_layer2.get_outputs()), filter_shape=(384, 256, 3, 3), convstride=1, padsize=1, group=1, poolsize=1, poolstride=0, bias_init=0.0, local_response_normalization=False) # Add this layer's parameters! self.params += convpool_layer3.get_params() log.debug("convpool layer 4...") convpool_layer4 = ConvPoolLayer( inputs_hook=((self.batch_size, 384, 13, 13), convpool_layer3.get_outputs()), filter_shape=(384, 384, 3, 3), convstride=1, padsize=1, group=2, poolsize=1, poolstride=0, bias_init=0.1, local_response_normalization=False) # Add this layer's parameters! self.params += convpool_layer4.get_params() log.debug("convpool layer 5...") convpool_layer5 = ConvPoolLayer( inputs_hook=((self.batch_size, 384, 13, 13), convpool_layer4.get_outputs()), filter_shape=(256, 384, 3, 3), convstride=1, padsize=1, group=2, poolsize=3, poolstride=2, bias_init=0.0, local_response_normalization=False) # Add this layer's parameters! self.params += convpool_layer5.get_params() # Now onto the fully-connected layers! fc_config = { 'activation': 'rectifier', # type of activation function to use for output 'weights_init': 'gaussian', # either 'gaussian' or 'uniform' - how to initialize weights 'weights_mean': 0.0, # mean for gaussian weights init 'weights_std': 0.005, # standard deviation for gaussian weights init 'bias_init': 0.0 # how to initialize the bias parameter } log.debug("fully connected layer 1 (model layer 6)...") # we want to have dropout applied to the training version, but not the test version. fc_layer6_input = T.flatten(convpool_layer5.get_outputs(), 2) fc_layer6 = BasicLayer(inputs_hook=(9216, fc_layer6_input), output_size=4096, noise='dropout', noise_level=0.5, **fc_config) # Add this layer's parameters! self.params += fc_layer6.get_params() # Add the dropout noise switch self.noise_switches += fc_layer6.get_noise_switch() log.debug("fully connected layer 2 (model layer 7)...") fc_layer7 = BasicLayer(inputs_hook=(4096, fc_layer6.get_outputs()), output_size=4096, noise='dropout', noise_level=0.5, **fc_config) # Add this layer's parameters! self.params += fc_layer7.get_params() # Add the dropout noise switch self.noise_switches += fc_layer7.get_noise_switch() # last layer is a softmax prediction output layer softmax_config = { 'weights_init': 'gaussian', 'weights_mean': 0.0, 'weights_std': 0.005, 'bias_init': 0.0 } log.debug("softmax classification layer (model layer 8)...") softmax_layer8 = SoftmaxLayer(inputs_hook=(4096, fc_layer7.get_outputs()), output_size=1000, **softmax_config) # Add this layer's parameters! self.params += softmax_layer8.get_params() # finally the softmax output from the whole thing! self.output = softmax_layer8.get_outputs() self.targets = softmax_layer8.get_targets() ##################### # Cost and monitors # ##################### self.train_cost = softmax_layer8.negative_log_likelihood() cost = softmax_layer8.negative_log_likelihood() errors = softmax_layer8.errors() train_errors = softmax_layer8.errors() self.monitors = OrderedDict([('cost', cost), ('errors', errors), ('dropout_errors', train_errors)]) ######################### # Compile the functions # ######################### log.debug("Compiling functions!") t = time.time() log.debug("f_run...") # use the actual argmax from the classification self.f_run = function(inputs=[self.x], outputs=softmax_layer8.get_argmax_prediction()) log.debug("compilation took %s", make_time_units_string(time.time() - t))