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