AttributeError Traceback (most recent call last)
in ()
----> 1 train(BATCH_SIZE=36)
in train(BATCH_SIZE)
63
64 #判别器的损失；在一个batch的数据上进行一次参数更新
---> 65 d_loss = d.train_on_batch(X, y)
66 print("batch %d d_loss : %f" % (index, d_loss))
67
D:\Program Files\Anaconda3\lib\site-packages\keras\models.py in train_on_batch(self, x, y, class_weight, sample_weight)
1067 return self.model.train_on_batch(x, y,
1068 sample_weight=sample_weight,
-> 1069 class_weight=class_weight)
1070
1071 def test_on_batch(self, x, y,
D:\Program Files\Anaconda3\lib\site-packages\keras\engine\training.py in train_on_batch(self, x, y, sample_weight, class_weight)
1841 sample_weight=sample_weight,
1842 class_weight=class_weight,
-> 1843 check_batch_axis=True)
1844 if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
1845 ins = x + y + sample_weights + [1.]
D:\Program Files\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)
1428 output_shapes,
1429 check_batch_axis=False,
-> 1430 exception_prefix='target')
1431 sample_weights = _standardize_sample_weights(sample_weight,
1432 self._feed_output_names)
D:\Program Files\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
68 elif isinstance(data, list):
69 data = [x.values if x.class.name == 'DataFrame' else x for x in data]
---> 70 data = [np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data]
71 else:
72 data = data.values if data.class.name == 'DataFrame' else data
D:\Program Files\Anaconda3\lib\site-packages\keras\engine\training.py in (.0)
68 elif isinstance(data, list):
69 data = [x.values if x.class.name == 'DataFrame' else x for x in data]
---> 70 data = [np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data]
71 else:
72 data = data.values if data.class.name == 'DataFrame' else data
AttributeError: 'int' object has no attribute 'ndim'
AttributeError Traceback (most recent call last)
in ()
----> 1 train(BATCH_SIZE=36)
in train(BATCH_SIZE)
63
64 #判别器的损失；在一个batch的数据上进行一次参数更新
---> 65 d_loss = d.train_on_batch(X, y)
66 print("batch %d d_loss : %f" % (index, d_loss))
67
D:\Program Files\Anaconda3\lib\site-packages\keras\models.py in train_on_batch(self, x, y, class_weight, sample_weight)
1067 return self.model.train_on_batch(x, y,
1068 sample_weight=sample_weight,
-> 1069 class_weight=class_weight)
1070
1071 def test_on_batch(self, x, y,
D:\Program Files\Anaconda3\lib\site-packages\keras\engine\training.py in train_on_batch(self, x, y, sample_weight, class_weight)
1841 sample_weight=sample_weight,
1842 class_weight=class_weight,
-> 1843 check_batch_axis=True)
1844 if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
1845 ins = x + y + sample_weights + [1.]
D:\Program Files\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)
1428 output_shapes,
1429 check_batch_axis=False,
-> 1430 exception_prefix='target')
1431 sample_weights = _standardize_sample_weights(sample_weight,
1432 self._feed_output_names)
D:\Program Files\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
68 elif isinstance(data, list):
69 data = [x.values if x.class.name == 'DataFrame' else x for x in data]
---> 70 data = [np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data]
71 else:
72 data = data.values if data.class.name == 'DataFrame' else data
D:\Program Files\Anaconda3\lib\site-packages\keras\engine\training.py in (.0)
68 elif isinstance(data, list):
69 data = [x.values if x.class.name == 'DataFrame' else x for x in data]
---> 70 data = [np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data]
71 else:
72 data = data.values if data.class.name == 'DataFrame' else data
AttributeError: 'int' object has no attribute 'ndim'