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Style fixes in example scripts

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This commit is contained in:
Francois Chollet 2017-03-15 21:13:31 -07:00
parent 1a0792ae13
commit 459d7fe3d7
2 changed files with 54 additions and 22 deletions
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43
examples/babi_memnn.py
View File

@ -38,7 +38,8 @@ def tokenize(sent):
def parse_stories(lines, only_supporting=False):
'''Parse stories provided in the bAbi tasks format
If only_supporting is true, only the sentences that support the answer are kept.
If only_supporting is true, only the sentences
that support the answer are kept.
'''
data = []
story = []
@ -68,9 +69,12 @@ def parse_stories(lines, only_supporting=False):
def get_stories(f, only_supporting=False, max_length=None):
'''Given a file name, read the file, retrieve the stories, and then convert the sentences into a single story.
'''Given a file name, read the file,
retrieve the stories,
and then convert the sentences into a single story.
If max_length is supplied, any stories longer than max_length tokens will be discarded.
If max_length is supplied,
any stories longer than max_length tokens will be discarded.
'''
data = parse_stories(f.readlines(), only_supporting=only_supporting)
flatten = lambda data: reduce(lambda x, y: x + y, data)
@ -85,7 +89,8 @@ def vectorize_stories(data, word_idx, story_maxlen, query_maxlen):
for story, query, answer in data:
x = [word_idx[w] for w in story]
xq = [word_idx[w] for w in query]
y = np.zeros(len(word_idx) + 1) # let's not forget that index 0 is reserved
# let's not forget that index 0 is reserved
y = np.zeros(len(word_idx) + 1)
y[word_idx[answer]] = 1
X.append(x)
Xq.append(xq)
@ -115,7 +120,11 @@ print('Extracting stories for the challenge:', challenge_type)
train_stories = get_stories(tar.extractfile(challenge.format('train')))
test_stories = get_stories(tar.extractfile(challenge.format('test')))
vocab = sorted(reduce(lambda x, y: x | y, (set(story + q + [answer]) for story, q, answer in train_stories + test_stories)))
vocab = set()
for story, q, answer in train_stories + test_stories:
vocab |= set(story + q + [answer])
vocab = sorted(vocab)
# Reserve 0 for masking via pad_sequences
vocab_size = len(vocab) + 1
story_maxlen = max(map(len, (x for x, _, _ in train_stories + test_stories)))
@ -134,8 +143,14 @@ print('-')
print('Vectorizing the word sequences...')
word_idx = dict((c, i + 1) for i, c in enumerate(vocab))
inputs_train, queries_train, answers_train = vectorize_stories(train_stories, word_idx, story_maxlen, query_maxlen)
inputs_test, queries_test, answers_test = vectorize_stories(test_stories, word_idx, story_maxlen, query_maxlen)
inputs_train, queries_train, answers_train = vectorize_stories(train_stories,
word_idx,
story_maxlen,
query_maxlen)
inputs_test, queries_test, answers_test = vectorize_stories(test_stories,
word_idx,
story_maxlen,
query_maxlen)
print('-')
print('inputs: integer tensor of shape (samples, max_length)')
@ -179,14 +194,16 @@ question_encoder.add(Embedding(input_dim=vocab_size,
question_encoder.add(Dropout(0.3))
# output: (samples, query_maxlen, embedding_dim)
# encode input sequence and questions (which are indices) to sequences of dense vectors
# encode input sequence and questions (which are indices)
# to sequences of dense vectors
input_encoded_m = input_encoder_m(input_sequence)
input_encoded_c = input_encoder_c(input_sequence)
question_encoded = question_encoder(question)
# compute a 'match' between the first input vector sequence
# and the question vector sequence
match = dot([input_encoded_m, question_encoded], axes=(2, 2)) # (samples, story_maxlen, query_maxlen)
# shape: `(samples, story_maxlen, query_maxlen)`
match = dot([input_encoded_m, question_encoded], axes=(2, 2))
match = Activation('softmax')(match)
# add the match matrix with the second input vector sequence
@ -209,10 +226,10 @@ answer = Activation('softmax')(answer)
# build the final model
model = Model([input_sequence, question], answer)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy',
metrics=['accuracy'])
metrics=['accuracy'])
# train
model.fit([inputs_train, queries_train], answers_train,
batch_size=32,
epochs=120,
validation_data=([inputs_test, queries_test], answers_test))
batch_size=32,
epochs=120,
validation_data=([inputs_test, queries_test], answers_test))

33
examples/babi_rnn.py
View File

@ -83,7 +83,8 @@ def tokenize(sent):
def parse_stories(lines, only_supporting=False):
'''Parse stories provided in the bAbi tasks format
If only_supporting is true, only the sentences that support the answer are kept.
If only_supporting is true,
only the sentences that support the answer are kept.
'''
data = []
story = []
@ -113,9 +114,11 @@ def parse_stories(lines, only_supporting=False):
def get_stories(f, only_supporting=False, max_length=None):
'''Given a file name, read the file, retrieve the stories, and then convert the sentences into a single story.
'''Given a file name, read the file, retrieve the stories,
and then convert the sentences into a single story.
If max_length is supplied, any stories longer than max_length tokens will be discarded.
If max_length is supplied,
any stories longer than max_length tokens will be discarded.
'''
data = parse_stories(f.readlines(), only_supporting=only_supporting)
flatten = lambda data: reduce(lambda x, y: x + y, data)
@ -130,7 +133,8 @@ def vectorize_stories(data, word_idx, story_maxlen, query_maxlen):
for story, query, answer in data:
x = [word_idx[w] for w in story]
xq = [word_idx[w] for w in query]
y = np.zeros(len(word_idx) + 1) # let's not forget that index 0 is reserved
# let's not forget that index 0 is reserved
y = np.zeros(len(word_idx) + 1)
y[word_idx[answer]] = 1
xs.append(x)
xqs.append(xq)
@ -140,10 +144,13 @@ def vectorize_stories(data, word_idx, story_maxlen, query_maxlen):
RNN = recurrent.LSTM
EMBED_HIDDEN_SIZE = 50
SENT_HIDDEN_SIZE = 100
QUERy_HIDDEN_SIZE = 100
QUERY_HIDDEN_SIZE = 100
BATCH_SIZE = 32
EPOCHS = 40
print('RNN / Embed / Sent / Query = {}, {}, {}, {}'.format(RNN, EMBED_HIDDEN_SIZE, SENT_HIDDEN_SIZE, QUERy_HIDDEN_SIZE))
print('RNN / Embed / Sent / Query = {}, {}, {}, {}'.format(RNN,
EMBED_HIDDEN_SIZE,
SENT_HIDDEN_SIZE,
QUERY_HIDDEN_SIZE))
try:
path = get_file('babi-tasks-v1-2.tar.gz', origin='https://s3.amazonaws.com/text-datasets/babi_tasks_1-20_v1-2.tar.gz')
@ -164,7 +171,11 @@ challenge = 'tasks_1-20_v1-2/en/qa2_two-supporting-facts_{}.txt'
train = get_stories(tar.extractfile(challenge.format('train')))
test = get_stories(tar.extractfile(challenge.format('test')))
vocab = sorted(reduce(lambda x, y: x | y, (set(story + q + [answer]) for story, q, answer in train + test)))
vocab = set()
for story, q, answer in train + test:
vocab |= set(story + q + [answer])
vocab = sorted(vocab)
# Reserve 0 for masking via pad_sequences
vocab_size = len(vocab) + 1
word_idx = dict((c, i + 1) for i, c in enumerate(vocab))
@ -203,6 +214,10 @@ model.compile(optimizer='adam',
metrics=['accuracy'])
print('Training')
model.fit([x, xq], y, batch_size=BATCH_SIZE, epochs=EPOCHS, validation_split=0.05)
loss, acc = model.evaluate([tx, txq], ty, batch_size=BATCH_SIZE)
model.fit([x, xq], y,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_split=0.05)
loss, acc = model.evaluate([tx, txq], ty,
batch_size=BATCH_SIZE)
print('Test loss / test accuracy = {:.4f} / {:.4f}'.format(loss, acc))