ValueError Traceback (most recent call last)
in
155 if name == 'main':
156 train = Train()
--> 157 train.train()
in train(self)
24
25 tf.set_random_seed(cfg.FLAGS.rng_seed)
---> 26 layers = self.net.create_architecture(sess, "TRAIN", self.imdb.num_classes, tag='default')
27 loss = layers['total_loss']
28 lr = tf.Variable(cfg.FLAGS.learning_rate, trainable=False)
E:\Faster-RCNN-TensorFlow-Python3-master\lib\nets\network.py in create_architecture(self, sess, mode, num_classes, tag, anchor_scales, anchor_ratios)
295 biases_regularizer=biases_regularizer,
296 biases_initializer=tf.constant_initializer(0.0)):
--> 297 rois, cls_prob, bbox_pred = self.build_network(sess, training)
298
299 layers_to_output = {'rois': rois}
E:\Faster-RCNN-TensorFlow-Python3-master\lib\nets\vgg16.py in build_network(self, sess, is_training)
28
29 # Build head
---> 30 net = self.build_head(is_training)
31
32 # Build rpn
E:\Faster-RCNN-TensorFlow-Python3-master\lib\nets\vgg16.py in build_head(self, is_training)
95 # Main network
96 # Layer 1
---> 97 net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3], trainable=False, scope='conv1')
98 net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1')
99
E:\Anconada\lib\site-packages\tf_slim\layers\layers.py in repeat(inputs, repetitions, layer, *args, **kwargs)
2646 for i in range(repetitions):
2647 kwargs['scope'] = scope + '_' + str(i + 1)
-> 2648 outputs = layer(outputs, *args, **kwargs)
2649 return outputs
2650
E:\Anconada\lib\site-packages\tf_slim\ops\arg_scope.py in func_with_args(*args, **kwargs)
182 current_args = current_scope[key_func].copy()
183 current_args.update(kwargs)
--> 184 return func(*args, **current_args)
185
186 _add_op(func)
E:\Anconada\lib\site-packages\tf_slim\layers\layers.py in convolution2d(inputs, num_outputs, kernel_size, stride, padding, data_format, rate, activation_fn, normalizer_fn, normalizer_params, weights_initializer, weights_regularizer, biases_initializer, biases_regularizer, reuse, variables_collections, outputs_collections, trainable, scope)
1169 trainable=True,
1170 scope=None):
-> 1171 return convolution(
1172 inputs,
1173 num_outputs,
E:\Anconada\lib\site-packages\tf_slim\ops\arg_scope.py in func_with_args(*args, **kwargs)
182 current_args = current_scope[key_func].copy()
183 current_args.update(kwargs)
--> 184 return func(*args, **current_args)
185
186 _add_op(func)
E:\Anconada\lib\site-packages\tf_slim\layers\layers.py in convolution(inputs, num_outputs, kernel_size, stride, padding, data_format, rate, activation_fn, normalizer_fn, normalizer_params, weights_initializer, weights_regularizer, biases_initializer, biases_regularizer, reuse, variables_collections, outputs_collections, trainable, scope, conv_dims)
1068 df = ('channels_first'
1069 if data_format and data_format.startswith('NC') else 'channels_last')
-> 1070 layer = layer_class(
1071 filters=num_outputs,
1072 kernel_size=kernel_size,
E:\Anconada\lib\site-packages\tensorflow\python\keras\legacy_tf_layers\convolutional.py in init(self, filters, kernel_size, strides, padding, data_format, dilation_rate, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, trainable, name, **kwargs)
305 name=None,
306 **kwargs):
--> 307 super(Conv2D, self).init(
308 filters=filters,
309 kernel_size=kernel_size,
E:\Anconada\lib\site-packages\tensorflow\python\keras\layers\convolutional.py in init(self, filters, kernel_size, strides, padding, data_format, dilation_rate, groups, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, **kwargs)
664 kernel_initializer=initializers.get(kernel_initializer),
665 bias_initializer=initializers.get(bias_initializer),
--> 666 kernel_regularizer=regularizers.get(kernel_regularizer),
667 bias_regularizer=regularizers.get(bias_regularizer),
668 activity_regularizer=regularizers.get(activity_regularizer),
E:\Anconada\lib\site-packages\tensorflow\python\keras\regularizers.py in get(identifier)
381 return identifier
382 else:
--> 383 raise ValueError(
384 'Could not interpret regularizer identifier: {}'.format(identifier))
ValueError: Could not interpret regularizer identifier: Tensor("mul_1:0", shape=(), dtype=float32)
这是什么问题导致的，改怎么修改
