Lambda layer in Keras with keras.backend.one_hot gives TypeError

问题

I'm trying to train a character level CNN using Keras. I take as input a single word. I have already transformed the words into lists of indices, but when I try to feed it into one_hot, I get a TypeError.

>>> X_train[0]
array([31, 14, 23, 29, 27, 18, 12, 30, 21, 10, 27,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0], dtype=uint8)
>>> X_train.shape
(2226641, 98)

But when I try to create my model like this:

k_model = Sequential()
k_model.add(Lambda(K.one_hot, arguments={'num_classes': 100}, input_shape=(98,), output_shape=(98,100)))
k_model.add(Conv1D(filters=16, kernel_size=5, strides=1, padding='valid'))

I get TypeError: Value passed to parameter 'indices' has DataType float32 not in list of allowed values: uint8, int32, int64.

It's obviously not making it to a point where X_train is even read, so where is it getting a float value?

I would like to have an instance shape of (98, 100), where 100 is the number of classes.

I can't fit the entire dataset in memory.

回答1:

I would suggest a cleaner solution that would achieve the same result, how about:

k_model.add(Embedding(num_classes, num_classes,
                      embeddings_initializer='identity',
                      trainable=False,
                      name='onehot'))

You are essentially embedding things, it would make more sense to use one with fixed weights. It also gives you the flexibility to make the embedding trainable in the future.

来源：https://stackoverflow.com/questions/50442416/lambda-layer-in-keras-with-keras-backend-one-hot-gives-typeerror

标签

python

keras

keras-layer

one-hot-encoding