def main(examples, args):
print('Total Number of Examples:', len(examples))
weights_file = os.path.expanduser(args.weights)
print('Weights loading from:', weights_file)
viz = Visualizer(padding=args.padding,
input_vocab=args.human_vocab,
output_vocab=args.machine_vocab)
print('Loading models')
pred_model = simpleNMT(trainable=False,
pad_length=args.padding,
n_chars=viz.input_vocab.size(),
n_labels=viz.output_vocab.size())
pred_model.load_weights(weights_file, by_name=True)
pred_model.compile(optimizer='adam', loss='categorical_crossentropy')
proba_model = simpleNMT(trainable=False,
pad_length=args.padding,
n_chars=viz.input_vocab.size(),
n_labels=viz.output_vocab.size(),
return_probabilities=True)
proba_model.load_weights(weights_file, by_name=True)
proba_model.compile(optimizer='adam', loss='categorical_crossentropy')
viz.set_models(pred_model, proba_model)
print('Models loaded')
for example in examples:
viz.attention_map(example)
print('Completed visualizations')
def main(examples, args):
print('Total Number of Examples:', len(examples))
weights_file = os.path.expanduser(args.weights)
print('Weights loading from:', weights_file)
viz = Visualizer(padding=args.padding,
input_vocab=args.human_vocab,
output_vocab=args.machine_vocab)
print('Loading models')
pred_model = simpleNMT(trainable=False,
pad_length=args.padding,
n_chars=viz.input_vocab.size(),
n_labels=viz.output_vocab.size())
pred_model.load_weights(weights_file, by_name=True)
pred_model.compile(optimizer='adam', loss='categorical_crossentropy')
proba_model = simpleNMT(trainable=False,
pad_length=args.padding,
n_chars=viz.input_vocab.size(),
n_labels=viz.output_vocab.size(),
return_probabilities=True)
proba_model.load_weights(weights_file, by_name=True)
proba_model.compile(optimizer='adam', loss='categorical_crossentropy')
viz.set_models(pred_model, proba_model)
print('Models loaded')
for example in examples:
viz.attention_map(example)
print('Completed visualizations')
def main(args):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
# Dataset functions
input_vocab = Vocabulary('./data/human_vocab.json', padding=args.padding)
output_vocab = Vocabulary('./data/machine_vocab.json',
padding=args.padding)
print('Loading datasets.')
training = Data(args.training_data, input_vocab, output_vocab)
validation = Data(args.validation_data, input_vocab, output_vocab)
training.load()
validation.load()
training.transform()
validation.transform()
print('Datasets Loaded.')
print('Compiling Model.')
model = simpleNMT(pad_length=args.padding,
n_chars=input_vocab.size(),
n_labels=output_vocab.size(),
embedding_learnable=False,
encoder_units=256,
decoder_units=256,
trainable=True,
return_probabilities=False)
model.summary()
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy', all_acc])
print('Model Compiled.')
print('Training. Ctrl+C to end early.')
try:
kwargs = dict(generator=training.generator(args.batch_size),
steps_per_epoch=100,
validation_data=validation.generator(args.batch_size),
validation_steps=100,
callbacks=[cp],
workers=1,
verbose=1,
epochs=args.epochs)
model.fit_generator(**kwargs)
except KeyboardInterrupt as e:
print('Model training stopped early.')
print('Model training complete.')
run_examples(model, input_vocab, output_vocab)
def main(args):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
# Dataset functions
input_vocab = Vocabulary('./data/human_vocab.json', padding=args.padding)
output_vocab = Vocabulary('./data/machine_vocab.json',
padding=args.padding)
print('Loading datasets.')
training = Data(args.training_data, input_vocab, output_vocab)
validation = Data(args.validation_data, input_vocab, output_vocab)
training.load()
validation.load()
training.transform()
validation.transform()
print('Datasets Loaded.')
print('Compiling Model.')
model = simpleNMT(pad_length=args.padding,
n_chars=input_vocab.size(),
n_labels=output_vocab.size(),
embedding_learnable=False,
encoder_units=256,
decoder_units=256,
trainable=True,
return_probabilities=False)
model.summary()
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy', all_acc])
print('Model Compiled.')
print('Training. Ctrl+C to end early.')
try:
model.fit_generator(generator=training.generator(args.batch_size),
steps_per_epoch=100,
validation_data=validation.generator(args.batch_size),
validation_steps=100,
callbacks=[cp],
workers=1,
verbose=1,
epochs=args.epochs)
except KeyboardInterrupt as e:
print('Model training stopped early.')
print('Model training complete.')
run_examples(model, input_vocab, output_vocab)
