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