Creating labels for Text classification using keras
You can build the text classification application with CNN algorithm by Keras library.
Please take a look at this git repository. Here

As you can see, you need to create training and testing data by loading polarity data from files, splitting the data into words, generating labels and returning split sentences and labels.
And you can create the convolutional neural network with Dense, Embedding, Conv2D, MaxPool2D of keras.

Here is the final model training snippet.

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 data_helpers import load_data

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

# x.shape -&gt; (10662, 56)
# y.shape -&gt; (10662, 2)
# len(vocabulary) -&gt; 18765
# len(vocabulary_inv) -&gt; 18765

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

# X_train.shape -&gt; (8529, 56)
# y_train.shape -&gt; (8529, 2)
# X_test.shape -&gt; (2133, 56)
# y_test.shape -&gt; (2133, 2)


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=2, 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='binary_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


By running this code, you will get the trained model with the format of hd5. Finally, you can use your model for prediction.