Keras LSTM 层值错误：尺寸必须相等，但为 17 和 2

Question

I'm working on a basic RNN model for a multiclass task and I'm facing some issues with output dimensions.

This is my input/output shapes:

input.shape = (50000, 2, 5) # (samples, features, feature_len)
output.shape = (50000, 17, 185) # (samples, features, feature_len) <-- one hot encoded 

input[0].shape =  (2, 5)
output[0].shape = (17, 185)

This is my model, using Keras functional API:

inp = tf.keras.Input(shape=(2, 5,))

x = tf.keras.layers.LSTM(128, input_shape=(2, 5,), return_sequences=True, activation='relu')(inp)
out = tf.keras.layers.Dense(185, activation='softmax')(x)

model = tf.keras.models.Model(inputs=inp, outputs=out)

This is my model.summary() :

Layer (type)                 Output Shape              Param #
=================================================================
input_1 (InputLayer)         [(None, 2, 5)]            0
_________________________________________________________________
lstm (LSTM)                  (None, 2, 128)            68608
_________________________________________________________________
dense (Dense)                (None, 2, 185)            23865
=================================================================
Total params: 92,473
Trainable params: 92,473
Non-trainable params: 0
_________________________________________________________________

Then I compile the model and run fit() :

model.compile(optimizer='adam',
              loss=tf.nn.softmax_cross_entropy_with_logits,
              metrics='accuracy')

model.fit(x=input, y=output, epochs=5)

And I'm getting a dimension error:

ValueError: Dimensions must be equal, but are 17 and 2 for '{{node Equal}} = Equal[T=DT_INT64, incompatible_shape_error=true](ArgMax, ArgMax_1)' with input shapes: [?,17], [?,2].

The error is clear, the model output a dimension 2 and my output has dimension 17 , although I understand the issue, I can't find a way of fixing it, any ideas?

Answer 1

I think your output shape is not "output[0].shape = (17, 185)" but "dense (Dense) (None, 2, 185) ".

You need to change your output shape or change your layer structure.

Answer 2

LSTM output is a list of encoder_outputs , when you specify return_sequences=True . hence; I suggest just using the last item of encoder_outputs as the input of your Dense layer. you can see the example section of this link to the documentation . It may help you.