hyperas GRU naming mistake

test.ipynb

from keras.datasets import imdb
from keras import preprocessing

from keras.layers import Dense, Embedding, SimpleRNN, GRU, LSTM, Dropout
from keras.models import Sequential

from sklearn.model_selection import train_test_split
from sklearn.metrics import recall_score

from hyperas import optim
from hyperas.distributions import choice, uniform
from hyperopt import Trials, STATUS_OK, tpe

import numpy as np

def data():
    max_features = 10000
    maxlen = 500
    (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
    x_train = preprocessing.sequence.pad_sequences(x_train, maxlen=maxlen)
    x_test = preprocessing.sequence.pad_sequences(x_test, maxlen=maxlen)
    x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size = 0.5)
    return x_train, y_train, x_val, y_val

def create_model(x_train, y_train, x_val, y_val):
    model = Sequential()
    model.add(Embedding(10000, 32))
    model.add(GRU(
        {{choice([8,16,32])}},
        recurrent_dropout={{uniform(0,1)}},
        dropout={{uniform(0,1)}}))
    model.add(Dense(
        units={{choice([8,16,32])}},
        activation='relu'))
    model.add(Dropout(
        {{uniform(0,1)}}))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
    _ = model.fit(x_train,
              y_train,
              batch_size=128,
              epochs=1,#{{choice([1,2])}},
              verbose=2)
    y_pred = model.predict(x_val)
    y_scores = y_pred.reshape(-1)
    y_pred = np.array([ round(score) for score in y_scores])
    recall = recall_score(y_pred=y_pred, y_true=y_val)
    return {'loss': -recall, 'status': STATUS_OK, 'model': model}

if __name__ == '__main__':
    best_run, best_model = optim.minimize(model=create_model,
                                              data=data,
                                              algo=tpe.suggest,
                                              max_evals=2,
                                              trials=Trials(),
                                              notebook_name='test')