Настройка функциональной модели в Керасе

Я просто возился с Keras для развлечения (для продолжения обучения), и у меня возникли некоторые проблемы с указанием структуры данных в CNN.

Tutorial: https://github.com/bhaveshoswal/CNN-text-classification-keras
Data: https://catalog.data.gov/dataset/consumer-complaint-database/resource/2f297213-7198-4be1-af1e-2d2623e7f6e9

Итак, у меня настроена обработка данных, и все выглядит правильно.

from keras.layers import Input, Dense, Embedding, Conv2D, MaxPool2D
from keras.layers import Reshape, Flatten, Dropout, Concatenate
from keras.callbacks import ModelCheckpoint
from keras.optimizers import Adam
from keras.models import Model
from sklearn.model_selection import train_test_split
from helper_functions import load_data

print('Loading data')
x, y, vocabulary, vocabulary_inv = load_data()

# x.shape -> (50000, 5371)
# y.shape -> (50000, 10)
# len(vocabulary) -> 50111
# len(vocabulary_inv) -> 50111

X_train, X_test, y_train, y_test = train_test_split( x, y, test_size=0.2, random_state=42)

# X_train.shape -> (40000, 5371)
# y_train.shape -> (40000, 18)
# X_test.shape -> (10000, 5371)
# y_test.shape -> (10000, 18)

Когда я пытаюсь подогнать модель, я получаю ошибку, связанную с размерами набора данных.

sequence_length = x.shape[1] # 56
vocabulary_size = len(vocabulary_inv) # 18765
embedding_dim = 256
filter_sizes = [3,4,5]
num_filters = 512
drop = 0.5

epochs = 100
batch_size = 30

# this returns a tensor
print("Creating Model...")

inputs = Input(shape=(sequence_length,), dtype='int32')
embedding = Embedding(input_dim=vocabulary_size, output_dim=embedding_dim, input_length=sequence_length)(inputs)
reshape = Reshape((sequence_length,embedding_dim,1))(embedding)

conv_0 = Conv2D(num_filters, kernel_size=(filter_sizes[0], embedding_dim), padding='valid', kernel_initializer='normal', activation='relu')(reshape)
conv_1 = Conv2D(num_filters, kernel_size=(filter_sizes[1], embedding_dim), padding='valid', kernel_initializer='normal', activation='relu')(reshape)
conv_2 = Conv2D(num_filters, kernel_size=(filter_sizes[2], embedding_dim), padding='valid', kernel_initializer='normal', activation='relu')(reshape)

maxpool_0 = MaxPool2D(pool_size=(sequence_length - filter_sizes[0] + 1, 1), strides=(1,1), padding='valid')(conv_0)
maxpool_1 = MaxPool2D(pool_size=(sequence_length - filter_sizes[1] + 1, 1), strides=(1,1), padding='valid')(conv_1)
maxpool_2 = MaxPool2D(pool_size=(sequence_length - filter_sizes[2] + 1, 1), strides=(1,1), padding='valid')(conv_2)

concatenated_tensor = Concatenate(axis=1)([maxpool_0, maxpool_1, maxpool_2])
flatten = Flatten()(concatenated_tensor)
dropout = Dropout(drop)(flatten)
output = Dense(units=18, activation='softmax')(dropout)

# this creates a model that includes
model = Model(inputs=inputs, outputs=output)

checkpoint = ModelCheckpoint('weights.{epoch:03d}-{val_acc:.4f}.hdf5', monitor='val_acc', verbose=1, save_best_only=True, mode='auto')
adam = Adam(lr=1e-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)

#model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

print("Traning Model...")
model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, callbacks=[checkpoint], validation_data=(X_test, y_test))  # starts training

Вот сообщение об ошибке:

Traning Model...
Traceback (most recent call last):

  File "<ipython-input-294-835f1e289b39>", line 41, in <module>
    model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, callbacks=[checkpoint], validation_data=(X_test, y_test))  # starts training

  File "/Users/abrahammathew/anaconda3/lib/python3.6/site-packages/keras/engine/training.py", line 1630, in fit
    batch_size=batch_size)

  File "/Users/abrahammathew/anaconda3/lib/python3.6/site-packages/keras/engine/training.py", line 1480, in _standardize_user_data
    exception_prefix='target')

  File "/Users/abrahammathew/anaconda3/lib/python3.6/site-packages/keras/engine/training.py", line 123, in _standardize_input_data
    str(data_shape))

ValueError: Error when checking target: expected dense_11 to have shape (1,) but got array with shape (18,)

Учитывая эту ошибку, как должны выглядеть измерения данных при создании моделей Keras с помощью функционального API.