multi-view-network 1.0

Keras implementation of Multi-View Network by Guo et al.

Multi-View Network in Keras

This package is based on End-to-End Multi-View Networks for Text Classification by Hongyu Guo, Colin Cherry and Jiang Su (2017). The overall architecture of the Multi-View Network (MVN) was not really explained in painstaking details in the paper, so I had to make some guess work.

Feel free reach out to me at [e-mail] with any feedback.

Basic Usage

Assuming you have your corpus prepared as a list of documents, each represented by a list of embeddings (one per token), you can train the MVN this way:

import multi_view_network
import numpy as np

# Very important: the documents in embedded_corpus **need** to have
# the same number of embedded_tokens. If this is not the case
# you can use multi_view_network.pad_embedded_corpus() to pad
# the documents with 0-filled mock embeddings.
data = np.array(embedded_corpus)

# The output of the MVN is softmaxed so it's important to
# make sure the labels are one-hot encoded.
labels = np.array([[0, 1], [0, 1], [1, 0], etc.])

model = multi_view_network.BuildMultiViewNetwork(
    embeddings_dim=300, hidden_units=16, dropout_rate=0.2, output_units=2)
model.compile(optimizer='sgd', loss='categorical_crossentropy')
model.fit(data, labels, epochs=200, batch_size=32)

More Complex Architectures

The models.py module contains all the necessary Layers to build MVNs of arbitrary size and complexity. For example:

import multi_view_network

embeddings_dim = 300
hidden_units = 64
output_units = 2

inputs = keras.layers.Input(shape=(None, embeddings_dim))
s1 = SelectionLayer(name='s1')(inputs)
s2 = SelectionLayer(name='s2')(inputs)
s3 = SelectionLayer(name='s3')(inputs)
s4 = SelectionLayer(name='s4')(inputs)
s5 = SelectionLayer(name='s5')(inputs)
s6 = SelectionLayer(name='s6')(inputs)
s7 = SelectionLayer(name='s7')(inputs)
s8 = SelectionLayer(name='s8')(inputs)
v1 = ViewLayer(view_index=1, name='v1')(s1)
v2 = ViewLayer(view_index=2, name='v2')([s1, s2])
v3 = ViewLayer(view_index=3, name='v3')([s1, s2, s3])
v4 = ViewLayer(view_index=4, name='v4')([s1, s2, s3, s4])
v5 = ViewLayer(view_index=5, name='v5')([s1, s2, s3, s4, s5])
v6 = ViewLayer(view_index=6, name='v6')([s1, s2, s3, s4, s5, s6])
v7 = ViewLayer(view_index=7, name='v7')([s1, s2, s3, s4, s5, s6, s7])
v8 = ViewLayer(view_index='Last', name='v8')(s8)
concatenation = keras.layers.concatenate(
    [v1, v2, v3, v4, v5, v6, v7, v8], name='concatenation')
fully_connected = keras.layers.Dense(
    units=hidden_units, name='fully_connected')(concatenation)
dropout = keras.layers.Dropout(rate=dropout_rate)(fully_connected)
another_dense_layer = keras.layers.Dense(
    units=hidden_units, name='another_dense_layer')(dropout)
softmax = keras.layers.Dense(
    units=output_units, activation='softmax',
    name='softmax')(dropout)

model = keras.models.Model(inputs=inputs, outputs=softmax)

Utilities

The utils.py module contains a couple of functions that could come in handy when pre-processing your input. As mentioned above, it's important that when you coerce your list of embedded_documents to np.array() all the documents have a same number of embedded_tokens. Otherwise, the resulting array will have an incorrect .shape, which would cause Keras to throw an error (as the input wouldn't match the expected shape).

There are two utility functions you can use to solve this problem: pad_embedded_corpus() and cap_embedded_corpus(). The first one adds 0-filled mock embedded_tokens to each document until all documents have the same length. The second one crops each document so that only the first X tokens are maintained, achieving the same result.

For example:

import multi_view_network

embedded_corpus = [
    [
        [0, 0]
    ],
    [
        [0, 0],
        [1, 1]
    ],
    [
        [0, 0],
        [1, 1],
        [2, 1]
    ]
]

padded_corpus = multi_view_network.pad_embedded_corpus(embedded_corpus, embeddings_dim=2)
padded_corpus_sizes = [len(lst) for lst in padded_corpus]
# padded_corps_sizes
# >>> [3, 3, 3]

capped_corpus = multi_view_network.cap_embedded_corpus(embedded_corpus)
capped_corpus_sizes = [len(lst) for lst in capped_corpus]
#capped_corpus_sizes
# >>> [1, 1, 1]

Adding 0-filled vectors to the documents has no effect on the output and training performance of the MVN, and it's thus the recommended way to make sure all embedded_documents have the same length.