keras_textcnn.py

# https://www.kaggle.com/yekenot/2dcnn-textclassifier
def model_cnn(embedding_matrix):
    filter_sizes = [1,2,3,5]
    num_filters = 36

    inp = Input(shape=(maxlen,))
    x = Embedding(max_features, embed_size, weights=[embedding_matrix])(inp)
    x = Reshape((maxlen, embed_size, 1))(x)

    maxpool_pool = []
    for i in range(len(filter_sizes)):
        conv = Conv2D(num_filters, kernel_size=(filter_sizes[i], embed_size),
                                     kernel_initializer='he_normal', activation='relu')(x)
        maxpool_pool.append(MaxPool2D(pool_size=(maxlen - filter_sizes[i] + 1, 1))(conv))

    z = Concatenate(axis=1)(maxpool_pool)   
    z = Flatten()(z)
    z = Dropout(0.1)(z)

    outp = Dense(1, activation="sigmoid")(z)

    model = Model(inputs=inp, outputs=outp)
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    
    return model