`categorical_crossentropy` 损失中的 ValueError function：形状问题

Question

I am trying to develop a bio-tagging name entity recognition (multi-class) model. I have 9 classes and converted it to one-hot encoding. During the training I got following error:

ValueError: A target array with shape (2014, 120, 9) was passed for an output of shape (None, 9) while using as loss categorical_crossentropy . This loss expects targets to have the same shape as the output.

My code snippet:

from keras.utils import to_categorical
y = [to_categorical(i, num_classes=n_tags) for i in y]  ### One hot encoding

input = Input(shape=(max_len,))
embed = Embedding(input_dim=n_words + 1, output_dim=50,
                  input_length=max_len, mask_zero=True)(input)  # 50-dim embedding
lstm = Bidirectional(LSTM(units=130, return_sequences=True,
                           recurrent_dropout=0.2))(embed)  # variational biLSTM
(lstm, forward_h, forward_c, backward_h, backward_c) = Bidirectional(LSTM(units=130, return_sequences=True, return_state=True, recurrent_dropout=0.2))(lstm)  # variational biLSTM
state_h = Concatenate()([forward_h, backward_h])
state_c = Concatenate()([forward_c, backward_c])
context_vector, attention_weights = Attention(10)(lstm, state_h)   ### Attention mechanism  
output = Dense(9, activation="softmax")(context_vector)
model = Model(input, output)
model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['categorical_accuracy'])
model.summary()

history = model.fit(X,np.array(y), batch_size=32, epochs=15,verbose=1)  
#### Got error message during training

Answer 1

Don't use one hot encoding and categorical_crossentropy . Instead keep the y vector as it is and use sparse_categorical_crossentropy . See if this works.

Refer https://stackoverflow.com/a/50135466/14337775