Keras dense layer shape mismatch

Question

I am trying to make a multiclass classifier in Keras, but I am getting a dimension mismatch in the Dense layer.

MAX_SENT_LENGTH = 100
MAX_SENTS = 15
EMBEDDING_DIM = 100

x_train = data[:-nb_validation_samples]
y_train = labels[:-nb_validation_samples]
x_val = data[-nb_validation_samples:]
y_val = labels[-nb_validation_samples:]

embedding_layer = Embedding(len(word_index) + 1,
                            EMBEDDING_DIM,
                            weights=[embedding_matrix],
                            input_length=MAX_SENT_LENGTH,
                            trainable=True)

sentence_input = Input(shape=(MAX_SENT_LENGTH,), dtype='int32')
embedded_sequences = embedding_layer(sentence_input)
l_lstm = Bidirectional(LSTM(100))(embedded_sequences)
sentEncoder = Model(sentence_input, l_lstm)

review_input = Input(shape=(MAX_SENTS,MAX_SENT_LENGTH), dtype='int32')
review_encoder = TimeDistributed(sentEncoder)(review_input)
l_lstm_sent = Bidirectional(LSTM(100))(review_encoder)
preds = Dense(7, activation='softmax')(l_lstm_sent)
model = Model(review_input, preds)

model.compile(loss='sparse_categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['acc'])

model.fit(x_train, y_train, validation_data=(x_val, y_val), 
            epochs=10, batch_size=50)

The class labels are transformed into a 1-hot vector correctly, but when trying to fit the model, I am getting this mismatch error:

('Shape of data tensor:', (5327, 15, 100))
('Shape of label tensor:', (5327, 7))
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         (None, 15, 100)           0         
_________________________________________________________________
time_distributed_1 (TimeDist (None, 15, 200)           351500    
_________________________________________________________________
bidirectional_2 (Bidirection (None, 200)               240800    
_________________________________________________________________
dense_1 (Dense)              (None, 7)                 1407       
=================================================================
Total params: 592,501
Trainable params: 592,501
Non-trainable params: 0
_________________________________________________________________
None
ValueError: Error when checking target: expected dense_1 to have 
              shape (None, 1) but got array with shape (4262, 7)

Where does this (None, 1) dimension come from and how can I solve this error?

Answer 1

You should use loss='categorical_crossentropy' instead of loss='sparse_categorical_crossentropy' if your label is one-hot encoded. 'sparse_categorical_crossentropy' takes integer labels, and that's why (None,1) dimension is required.