203 lines
6.9 KiB
Python
203 lines
6.9 KiB
Python
# -*- coding: utf-8 -*-
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'''An implementation of sequence to sequence learning for performing addition
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Input: "535+61"
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Output: "596"
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Padding is handled by using a repeated sentinel character (space)
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Input may optionally be inverted, shown to increase performance in many tasks in:
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"Learning to Execute"
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http://arxiv.org/abs/1410.4615
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and
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"Sequence to Sequence Learning with Neural Networks"
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http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf
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Theoretically it introduces shorter term dependencies between source and target.
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Two digits inverted:
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+ One layer LSTM (128 HN), 5k training examples = 99% train/test accuracy in 55 epochs
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Three digits inverted:
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+ One layer LSTM (128 HN), 50k training examples = 99% train/test accuracy in 100 epochs
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Four digits inverted:
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+ One layer LSTM (128 HN), 400k training examples = 99% train/test accuracy in 20 epochs
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Five digits inverted:
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+ One layer LSTM (128 HN), 550k training examples = 99% train/test accuracy in 30 epochs
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'''
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from __future__ import print_function
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from keras.models import Sequential
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from keras import layers
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import numpy as np
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from six.moves import range
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class CharacterTable(object):
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"""Given a set of characters:
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+ Encode them to a one hot integer representation
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+ Decode the one hot integer representation to their character output
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+ Decode a vector of probabilities to their character output
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"""
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def __init__(self, chars):
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"""Initialize character table.
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# Arguments
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chars: Characters that can appear in the input.
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"""
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self.chars = sorted(set(chars))
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self.char_indices = dict((c, i) for i, c in enumerate(self.chars))
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self.indices_char = dict((i, c) for i, c in enumerate(self.chars))
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def encode(self, C, num_rows):
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"""One hot encode given string C.
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# Arguments
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num_rows: Number of rows in the returned one hot encoding. This is
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used to keep the # of rows for each data the same.
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"""
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x = np.zeros((num_rows, len(self.chars)))
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for i, c in enumerate(C):
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x[i, self.char_indices[c]] = 1
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return x
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def decode(self, x, calc_argmax=True):
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if calc_argmax:
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x = x.argmax(axis=-1)
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return ''.join(self.indices_char[x] for x in x)
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class colors:
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ok = '\033[92m'
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fail = '\033[91m'
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close = '\033[0m'
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# Parameters for the model and dataset.
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TRAINING_SIZE = 50000
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DIGITS = 3
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INVERT = True
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# Maximum length of input is 'int + int' (e.g., '345+678'). Maximum length of
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# int is DIGITS.
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MAxLEN = DIGITS + 1 + DIGITS
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# All the numbers, plus sign and space for padding.
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chars = '0123456789+ '
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ctable = CharacterTable(chars)
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questions = []
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expected = []
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seen = set()
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print('Generating data...')
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while len(questions) < TRAINING_SIZE:
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f = lambda: int(''.join(np.random.choice(list('0123456789'))
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for i in range(np.random.randint(1, DIGITS + 1))))
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a, b = f(), f()
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# Skip any addition questions we've already seen
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# Also skip any such that x+Y == Y+x (hence the sorting).
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key = tuple(sorted((a, b)))
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if key in seen:
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continue
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seen.add(key)
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# Pad the data with spaces such that it is always MAxLEN.
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q = '{}+{}'.format(a, b)
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query = q + ' ' * (MAxLEN - len(q))
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ans = str(a + b)
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# Answers can be of maximum size DIGITS + 1.
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ans += ' ' * (DIGITS + 1 - len(ans))
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if INVERT:
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# Reverse the query, e.g., '12+345 ' becomes ' 543+21'. (Note the
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# space used for padding.)
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query = query[::-1]
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questions.append(query)
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expected.append(ans)
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print('Total addition questions:', len(questions))
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print('Vectorization...')
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x = np.zeros((len(questions), MAxLEN, len(chars)), dtype=np.bool)
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y = np.zeros((len(questions), DIGITS + 1, len(chars)), dtype=np.bool)
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for i, sentence in enumerate(questions):
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x[i] = ctable.encode(sentence, MAxLEN)
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for i, sentence in enumerate(expected):
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y[i] = ctable.encode(sentence, DIGITS + 1)
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# Shuffle (x, y) in unison as the later parts of x will almost all be larger
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# digits.
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indices = np.arange(len(y))
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np.random.shuffle(indices)
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x = x[indices]
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y = y[indices]
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# Explicitly set apart 10% for validation data that we never train over.
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split_at = len(x) - len(x) // 10
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(x_train, x_val) = x[:split_at], x[split_at:]
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(y_train, y_val) = y[:split_at], y[split_at:]
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print('Training Data:')
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print(x_train.shape)
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print(y_train.shape)
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print('Validation Data:')
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print(x_val.shape)
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print(y_val.shape)
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# Try replacing GRU, or SimpleRNN.
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RNN = layers.LSTM
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HIDDEN_SIZE = 128
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BATCH_SIZE = 128
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LAYERS = 1
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print('Build model...')
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model = Sequential()
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# "Encode" the input sequence using an RNN, producing an output of HIDDEN_SIZE.
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# Note: In a situation where your input sequences have a variable length,
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# use input_shape=(None, num_feature).
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model.add(RNN(HIDDEN_SIZE, input_shape=(MAxLEN, len(chars))))
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# As the decoder RNN's input, repeatedly provide with the last hidden state of
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# RNN for each time step. Repeat 'DIGITS + 1' times as that's the maximum
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# length of output, e.g., when DIGITS=3, max output is 999+999=1998.
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model.add(layers.RepeatVector(DIGITS + 1))
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# The decoder RNN could be multiple layers stacked or a single layer.
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for _ in range(LAYERS):
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# By setting return_sequences to True, return not only the last output but
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# all the outputs so far in the form of (num_samples, timesteps,
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# output_dim). This is necessary as TimeDistributed in the below expects
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# the first dimension to be the timesteps.
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model.add(RNN(HIDDEN_SIZE, return_sequences=True))
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# Apply a dense layer to the every temporal slice of an input. For each of step
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# of the output sequence, decide which character should be chosen.
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model.add(layers.TimeDistributed(layers.Dense(len(chars))))
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model.add(layers.Activation('softmax'))
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model.compile(loss='categorical_crossentropy',
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optimizer='adam',
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metrics=['accuracy'])
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model.summary()
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# Train the model each generation and show predictions against the validation
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# dataset.
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for iteration in range(1, 200):
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print()
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print('-' * 50)
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print('Iteration', iteration)
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model.fit(x_train, y_train,
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batch_size=BATCH_SIZE,
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epochs=1,
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validation_data=(x_val, y_val))
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# Select 10 samples from the validation set at random so we can visualize
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# errors.
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for i in range(10):
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ind = np.random.randint(0, len(x_val))
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rowx, rowy = x_val[np.array([ind])], y_val[np.array([ind])]
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preds = model.predict_classes(rowx, verbose=0)
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q = ctable.decode(rowx[0])
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correct = ctable.decode(rowy[0])
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guess = ctable.decode(preds[0], calc_argmax=False)
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print('Q', q[::-1] if INVERT else q)
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print('T', correct)
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if correct == guess:
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print(colors.ok + '☑' + colors.close, end=" ")
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else:
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print(colors.fail + '☒' + colors.close, end=" ")
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print(guess)
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print('---')
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