[Python] ValueError: Classification metrics can't handle a mix of multiclass and...

I have Multi class labeled text classification problem with 2000 different labels. Doing classification using LSTM with Glove Embedding.

1. Label Encoder of target variable
2. LSTM layer with Embedd Layer
3. Error metric is F2 score

LabelEncoded target variable:


le = LabelEncoder()
le.fit(y)
train_y = le.transform(y_train)
test_y = le.transform(y_test)

LSTM network is like below with Glove Embeddings


np.random.seed(seed)
K.clear_session()
model = Sequential()
model.add(Embedding(max_features, embed_dim, input_length = X_train.shape[1],
weights=[embedding_matrix]))#,trainable=False
model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(num_classes, activation='softmax'))
model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy')
print(model.summary())

My error metric is F1 score. I build below function for Error metric


class Metrics(Callback):
def on_train_begin(self, logs={}):
self.val_f1s = []
self.val_recalls = []
self.val_precisions = []

def on_epoch_end(self, epoch, logs={}):
val_predict = (np.asarray(self.model.predict(self.validation_data[0]))).round()
val_targ = self.validation_data[1]
_val_f1 = f1_score(val_targ, val_predict)
_val_recall = recall_score(val_targ, val_predict)
_val_precision = precision_score(val_targ, val_predict)
self.val_f1s.append(_val_f1)
self.val_recalls.append(_val_recall)
self.val_precisions.append(_val_precision)
print("— val_f1: %f — val_precision: %f — val_recall %f" % (_val_f1, _val_precision, _val_recall))
return

metrics = Metrics()


##Model fit is

model.fit(X_train, train_y, validation_data=(X_test, test_y),epochs=10, batch_size=64, callbacks=[metrics])


Getting below error after 1st epoch:

ValueError: Classification metrics can't handle a mix of multiclass and continuous-multioutput targets


Where is the mistake in my code?

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