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<p>Example of using Hierarchical RNN (HRNN) to classify MNIST digits.</p>
<p>HRNNs can learn across multiple levels
of temporal hierarchy over a complex sequence.
Usually, the first recurrent layer of an HRNN
encodes a sentence (e.g. of word vectors)
into a sentence vector.
The second recurrent layer then encodes a sequence of
such vectors (encoded by the first layer) into a document vector.
This document vector is considered to preserve both
the word-level and sentence-level structure of the context.</p>
<h1 id="references">References</h1>
<ul>
<li>[A Hierarchical Neural Autoencoder for Paragraphs and Documents]
(https://arxiv.org/abs/1506.01057)
Encodes paragraphs and documents with HRNN.
Results have shown that HRNN outperforms standard
RNNs and may play some role in more sophisticated generation tasks like
summarization or question answering.</li>
<li>[Hierarchical recurrent neural network for skeleton based action recognition]
(http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298714)
Achieved state-of-the-art results on
skeleton based action recognition with 3 levels
of bidirectional HRNN combined with fully connected layers.</li>
</ul>
<p>In the below MNIST example the first LSTM layer first encodes every
column of pixels of shape (28, 1) to a column vector of shape (128,).
The second LSTM layer encodes then these 28 column vectors of shape (28, 128)
to a image vector representing the whole image.
A final Dense layer is added for prediction.</p>
<p>After 5 epochs: train acc: 0.9858, val acc: 0.9864</p>
<pre><code class="python">from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Model
from keras.layers import Input, Dense, TimeDistributed
from keras.layers import LSTM
# Training parameters.
batch_size = 32
num_classes = 10
epochs = 5
# Embedding dimensions.
row_hidden = 128
col_hidden = 128
# The data, split between train and test sets.
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Reshapes data to 4D for Hierarchical RNN.
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# Converts class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
row, col, pixel = x_train.shape[1:]
# 4D input.
x = Input(shape=(row, col, pixel))
# Encodes a row of pixels using TimeDistributed Wrapper.
encoded_rows = TimeDistributed(LSTM(row_hidden))(x)
# Encodes columns of encoded rows.
encoded_columns = LSTM(col_hidden)(encoded_rows)
# Final predictions and model.
prediction = Dense(num_classes, activation='softmax')(encoded_columns)
model = Model(x, prediction)
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
# Training.
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
# Evaluation.
scores = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
</code></pre>
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