fashion_mnist_hierarchical_rnn.py

"""This is an example of using Hierarchical RNN (HRNN) to classify FASHION MNIST images.

HRNNs can learn across multiple levels of temporal hiearchy 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.

# References
    - [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.
    - [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.

In the below FASHION 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.

"""
from __future__ import print_function

import keras
import fashion_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, shuffled and split between train and test sets.
(x_train, y_train), (x_test, y_test) = fashion_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])