def train():
ds_train = data.Dataset.load('imdb_train.bin')
ds_val = data.Dataset.load('imdb_test.bin')
# use the embedding trained on Simple English Wikipedia
factory = TokenModelFactory(ds_train.num_classes,
ds_train.tokenizer.token_index,
max_tokens=MAX_LEN,
embedding_type='fasttext.wiki.simple',
embedding_dims=300)
word_encoder_model = YoonKimCNN()
# word_encoder_model = AttentionRNN()
# word_encoder_model = StackedRNN()
# word_encoder_model = BasicRNN()
# freeze word embeddings
model = factory.build_model(token_encoder_model=word_encoder_model,
trainable_embeddings=False)
# use experiment.train as wrapper for Keras.fit()
experiment.train(x=ds_train.X,
y=ds_train.y,
validation_data=(ds_val.X, ds_val.y),
model=model,
word_encoder_model=word_encoder_model)
def test_train():
X, y, _, _ = imdb(10)
# use the special tokenizer used for constructing the embeddings
tokenizer = SpacyTokenizer()
# preprocess data (once)
experiment.setup_data(X, y, tokenizer, 'data.bin', max_len=100)
# load data
ds = Dataset.load('data.bin')
# construct base
factory = TokenModelFactory(ds.num_classes,
ds.tokenizer.token_index,
max_tokens=100,
embedding_type='glove.6B.50d',
embedding_dims=50)
# choose a model
word_encoder_model = YoonKimCNN()
# build a model
model = factory.build_model(token_encoder_model=word_encoder_model,
trainable_embeddings=False)
# use experiment.train as wrapper for Keras.fit()
experiment.train(x=ds.X,
y=ds.y,
validation_split=0.1,
model=model,
word_encoder_model=word_encoder_model,
epochs=1,
batch_size=32)
def train():
ds = Dataset.load(path)
X_train, _, y_train, _ = ds.train_val_split()
print(ds.tokenizer.decode_texts(X_train[:10]))
print(y_train[:10])
# RNN models can use `max_tokens=None` to indicate variable length words per mini-batch.
factory = TokenModelFactory(
2, ds.tokenizer.token_index, max_tokens=max_len, embedding_type='glove.6B.300d')
# 2, ds.tokenizer.token_index, max_tokens=max_len, embedding_type='fasttext.simple')
word_encoder_model = YoonKimCNN()
# word_encoder_model = AlexCNN(dropout_rate=[0, 0])
# word_encoder_model = AttentionRNN()
# word_encoder_model = StackedRNN()
word_encoder_model = BasicRNN()
model = factory.build_model(
token_encoder_model=word_encoder_model, trainable_embeddings=False)
model.compile(optimizer='sgd',
loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.1)
def test_train():
X, y, _, _ = imdb(10)
tokenizer = SpacyTokenizer()
tokenizer.build_vocab(X)
# only select top 10k tokens
tokenizer.apply_encoding_options(limit_top_tokens=20)
X_encoded = tokenizer.encode_texts(X)
X_padded = tokenizer.pad_sequences(
X_encoded, fixed_token_seq_length=max_len)
y_cat = keras.utils.to_categorical(y, num_classes=2)
ds = Dataset(X_padded, y_cat, tokenizer=tokenizer)
X_train, _, y_train, _ = ds.train_val_split()
# RNN models can use `max_tokens=None` to indicate variable length words per mini-batch.
factory = TokenModelFactory(
2, ds.tokenizer.token_index, max_tokens=max_len, embedding_type='glove.6B.300d')
word_encoder_model = YoonKimCNN(dropout_rate=0)
model = factory.build_model(
token_encoder_model=word_encoder_model, trainable_embeddings=False)
sgd = keras.optimizers.SGD(
lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
model.fit(X_train, y_train, epochs=1, batch_size=32, validation_split=0.1)
def _test_build(token_encoder_model):
test_index = {'hello': 1, 'kitty': 2}
if token_encoder_model.allows_dynamic_length():
factory = TokenModelFactory(1,
test_index,
max_tokens=None,
embedding_type=None)
model = factory.build_model(token_encoder_model)
model.compile(optimizer='adam', loss='categorical_crossentropy')
model.summary()
else:
# Should fail since this model does not allow dynamic mini-batches.
factory = TokenModelFactory(1,
test_index,
max_tokens=None,
embedding_type=None)
with pytest.raises(ValueError):
factory.build_model(token_encoder_model)
factory = TokenModelFactory(1,
test_index,
max_tokens=100,
embedding_type=None)
model = factory.build_model(token_encoder_model)
model.compile(optimizer='adam', loss='categorical_crossentropy')
model.summary()
def train(word_encoder_model, lr, batch_size, results_base_dir):
ds = Dataset.load(proc_path)
factory = TokenModelFactory(ds.num_classes,
ds.tokenizer.token_index,
max_tokens=max_len,
embedding_type="fasttext.wiki.de",
embedding_dims=300)
model = factory.build_model(token_encoder_model=word_encoder_model,
trainable_embeddings=False)
experiment.train(x=ds.X,
y=ds.y,
validation_split=0.1,
model=model,
word_encoder_model=word_encoder_model,
epochs=5)
def train():
ds_train = data.Dataset.load('imdb_train.bin')
ds_val = data.Dataset.load('imdb_test.bin')
factory = TokenModelFactory(ds_train.num_classes,
ds_train.tokenizer.token_index,
max_tokens=MAX_LEN,
embedding_dims=EMB_DIMS,
embedding_type=None)
word_encoder_model = AveragingEncoder()
# freeze word embeddings
model = factory.build_model(token_encoder_model=word_encoder_model,
trainable_embeddings=True)
# use experiment.train as wrapper for Keras.fit()
experiment.train(x=ds_train.X,
y=ds_train.y,
validation_data=(ds_val.X, ds_val.y),
model=model,
word_encoder_model=word_encoder_model,
epochs=EPOCHS)
def test_train_multi_label():
X, y = ['what is up', 'yes yes', 'no no no'], [["foo", "bar"], ["foo"],
["bar", "haha"]]
# use the special tokenizer used for constructing the embeddings
tokenizer = SimpleTokenizer()
# preprocess data (once)
experiment.setup_data(X, y, tokenizer, 'data.bin', max_len=100)
# load data
ds = Dataset.load('data.bin')
# construct base
factory = TokenModelFactory(ds.num_classes,
ds.tokenizer.token_index,
max_tokens=100,
embedding_type='glove.6B.50d',
embedding_dims=50)
# choose a model
word_encoder_model = YoonKimCNN()
# build a model
model = factory.build_model(token_encoder_model=word_encoder_model,
trainable_embeddings=False,
output_activation="sigmoid")
# use experiment.train as wrapper for Keras.fit()
experiment.train(x=ds.X,
y=ds.y,
validation_split=0.1,
model=model,
word_encoder_model=word_encoder_model,
epochs=1,
batch_size=32)