使用Python Keras训练CNN 1D时发生AttributeError

时间:2018-11-12 09:43:04

标签: python machine-learning keras progress-bar conv-neural-network

我曾尝试构建CNN 1D,但口译员说:

  

AttributeError:“ ProgbarLogger”对象没有属性“ log_values”

这是代码段:

model = Sequential()
model.add(Conv1D(200, 20, activation='relu', padding='same',input_shape=(1154,1024))
print(model.summary())
model.compile(optimizer='adam',
          loss='sparse_categorical_crossentropy',
          metrics=['accuracy'])
history=model.fit(X, y,batch_size=10, epochs=25,validation_split=0.7)

这是错误:

Layer (type)                 Output Shape              Param #   
=================================================================
conv1d_1 (Conv1D)            (None, 1154, 200)         4096200   
=================================================================
Total params: 4,096,200
Trainable params: 4,096,200
Non-trainable params: 0
_________________________________________________________________
None

Train on 0 samples, validate on 1 samples
Epoch 1/25
Traceback (most recent call last):
  File "binary_classification.py", line 59, in <module>
    history=model.fit(X, y,batch_size=10, epochs=25,validation_split=0.7)
  File "/home/isabella/.local/lib/python3.6/site-packages/keras/engine/training.py",
line 1039, in fit
    validation_steps=validation_steps)
  File "/home/isabella/.local/lib/python3.6/site-packages/keras/engine/training_arrays.py",
line 217, in fit_loop
    callbacks.on_epoch_end(epoch, epoch_logs)
  File "/home/isabella/.local/lib/python3.6/site-packages/keras/callbacks.py",
line 79, in on_epoch_end
    callback.on_epoch_end(epoch, logs)
  File "/home/isabella/.local/lib/python3.6/site-packages/keras/callbacks.py",
line 338, in on_epoch_end
    self.progbar.update(self.seen, self.log_values)
AttributeError: 'ProgbarLogger' object has no attribute 'log_values'

X shape is :(1,1154,1024),
y shape is :(1,1154, 1 )

1 个答案:

答案 0 :(得分:4)

如果仔细看,您会在堆栈跟踪输出之前看到以下行:

Train on 0 samples, validate on 1 samples

没有培训数据!为什么?这是因为您已将validation_split设置为0.7,因此X(和y)中的头70%的数据点被留作验证,其余30%用于训练。 X中的数据点数量可能少于4,因此其30%会少于1,这意味着仍有零数据点需要训练。使用4个以上的数据点或删除validation_split自变量(或降低自变量,以便至少保留一个样本进行训练)。