dims = (10, 13, 5, 4)
config_dict = dict()
config_dict['batch_size'] = dims[2]
config_dict['num_subwords'] = dims[1]
config_dict['num_words'] = dims[0]
config_dict['subword_embedding_size'] = dims[3]
config_dict['input_vocab_size'] = 42
config_dict['output_vocab_size'] = 42
config_dict['subword_RNN_hidden_state_size'] = 6 # 2 more than subword_embedding_size for testing
config_dict['LM_RNN_hidden_state_size'] = 8 # 2 more than subword_RNN_hidden_state_size
config_dict['table_width'] = 0.08
config_dict['max_out_dim'] = 11 # 3 more than LM_RNN_hidden_state_size
config_dict['max_out_K'] = 3
baseline_model = BaselineModel(config_dict)
baseline_model.initialize()
y_hat_CE = baseline_model.cost(subword_id_input_=x, subword_id_input_mask_=x_mask, subword_id_target_=y, subword_id_target_mask_=y_mask)
cg = ComputationGraph(y_hat_CE)
# cost = cg.outputs[0]
f_cross_entropy = theano.function([x, x_mask, y, y_mask], [y_hat_CE])
print('Graph inputs')
print(cg.inputs)
print(cg.inputs)
num_times = 5
for data in stream.get_epoch_iterator(as_dict=True):
num_times -= 1
def run_training(config, tr_stream, dev_stream=None, use_bokeh=True):
# Monitoring extensions
try:
from blocks_extras.extensions.plot import Plot
BOKEH_AVAILABLE = True
except ImportError:
BOKEH_AVAILABLE = False
print('Bokeh avalablity: ' + str(BOKEH_AVAILABLE))
logger = logging.getLogger(__name__)
# Create Theano variables
logger.info('Creating theano variables')
x = T.tensor3('features', dtype=config.params['data_dtype'])
x_mask = T.tensor3('features_mask', dtype=config.params['mask_dtype'])
y = T.matrix('targets', dtype=config.params['data_dtype'])
y_mask = T.matrix('targets_mask', dtype=config.params['mask_dtype'])
# Construct model
logger.info('Building baseline model')
baseline_model = BaselineModel(config.params)
baseline_model.initialize()
cost = baseline_model.cost(subword_id_input_=x, subword_id_input_mask_=x_mask,
subword_id_target_=y, subword_id_target_mask_=y_mask)
logger.info('Creating computational graph')
cg = ComputationGraph(cost)
# apply dropout for regularization
if config.params['dropout'] < 1.0:
# dropout is applied to the output of maxout in ghog
logger.info('Applying dropout')
dropout_inputs = [x for x in cg.intermediary_variables
if x.name == 'maxout_apply_output']
print(cg.intermediary_variables)
print(cg.variables)
print(cg.inputs)
print(cg.parameters)
print(dropout_inputs)
cg = apply_dropout(cg, dropout_inputs, config.params['dropout'])
logger.info("Initializing extensions")
extensions = [
FinishAfter(after_n_batches=config.params['finish_after']),
TrainingDataMonitoring([cost], after_batch=True),
Printing(after_batch=True)
#CheckpointNMT(config['saveto'], every_n_batches=config['save_freq'])]
]
# Plot cost in bokeh if necessary
if use_bokeh and BOKEH_AVAILABLE:
extensions.append(
Plot('Baseline model', channels=[['baselinemodel_cost_cost']],
after_batch=True))
# Set up training algorithm
logger.info("Initializing training algorithm")
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=CompositeRule([StepClipping(config.params['step_clipping']), eval(config.params['step_rule'])()])
)
# Initialize main loop
logger.info("Initializing main loop")
main_loop = MainLoop(
model=baseline_model,
algorithm=algorithm,
data_stream=tr_stream,
extensions=extensions
)
# Train
main_loop.run()
print('DONE TRAINING')