在 keras 中训练 CNN 进行文本分类时出错“ValueError：输入 0 与图层不兼容”

Question

I am building a prediction model for sequence data using conv1d layer provided by Keras. This is how I did

input_layer = Input(shape=(500,)) 
layer = Conv1D(128,5,activation="relu")(input_layer)
layer = MaxPooling1D(pool_size=2)(layer)
layer = Flatten()(layer)
layer = Dense(128, activation='relu')(layer)
output_layer = Dense(10, activation='softmax')(layer)
classifier = Model(input_layer, output_layer)
classifier.summary()
classifier.compile(optimizer=optimizers.Adam(), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
return classifier

However, am facing the following error:

Traceback (most recent call last):
File "train.py", line 71, in <module>
classifier = create_cnn_model()
File "train.py", line 60, in create_cnn_model
layer = Conv1D(128,5, activation="relu")(input_layer)
File "C:\Python368\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn
_wrapper
return func(*args, **kwargs)
File "C:\Python368\lib\site-packages\keras\engine\base_layer.py", line 446, in __call__
self.assert_input_compatibility(inputs)
File "C:\Python368\lib\site-packages\keras\engine\base_layer.py", line 342, in assert_input_compat
ibility
str(K.ndim(x)))
ValueError: Input 0 is incompatible with layer conv1d_1: expected ndim=3, found ndim=2

I think the input_shape in the first layer is not setup right. How to set it up?

Answer 1

Right, conv layers need 3 dimensional input. I am assuming you have a univariate time series with 500 samples. You need to write a function to split the time series into steps. For example:

     x             y
[t-n,...,t-2,t-1]  t

So you are basically using the last n values to predict the next value in your series. Then your input shape will be [len(x), n, 1]