diff options
Diffstat (limited to 'train.py')
| -rwxr-xr-x | train.py | 71 |
1 files changed, 54 insertions, 17 deletions
@@ -27,6 +27,8 @@ import datastream logging.basicConfig(level='INFO') logger = logging.getLogger(__name__) +sys.setrecursionlimit(1500) + if __name__ == "__main__": if len(sys.argv) != 2: print >> sys.stderr, 'Usage: %s config' % sys.argv[0] @@ -37,34 +39,64 @@ if __name__ == "__main__": class GenText(SimpleExtension): def __init__(self, model, init_text, max_bytes, **kwargs): + super(GenText, self).__init__(**kwargs) + self.init_text = init_text self.max_bytes = max_bytes - out = model.out[:, -1, :] / numpy.float32(config.sample_temperature) prob = tensor.nnet.softmax(out) cg = ComputationGraph([prob]) assert(len(cg.inputs) == 1) assert(cg.inputs[0].name == 'bytes') - self.f = theano.function(inputs=cg.inputs, outputs=[prob]) - super(GenText, self).__init__(**kwargs) + state_vars = [theano.shared(v[0:1, :].zeros_like().eval(), v.name+'-gen') + for v, _ in model.states] + givens = [(v, x) for (v, _), x in zip(model.states, state_vars)] + updates= [(x, upd) for x, (_, upd) in zip(state_vars, model.states)] + + self.f = theano.function(inputs=cg.inputs, outputs=[prob], + givens=givens, updates=updates) + self.reset_states = theano.function(inputs=[], outputs=[], + updates=[(v, v.zeros_like()) for v in state_vars]) def do(self, which_callback, *args): + + print "Sample:" + print "-------" + + self.reset_states() + v = numpy.array([ord(i) for i in self.init_text], - dtype='int16')[None, :].repeat(axis=0, repeats=config.num_seqs) + dtype='int16')[None, :] + prob, = self.f(v) + sys.stdout.write(self.init_text) while v.shape[1] < self.max_bytes: - prob, = self.f(v) prob = prob / 1.00001 - pred = numpy.zeros((prob.shape[0],), dtype='int16') - for i in range(prob.shape[0]): - pred[i] = numpy.random.multinomial(1, prob[i, :]).nonzero()[0][0] - v = numpy.concatenate([v, pred[:, None]], axis=1) + pred = numpy.random.multinomial(1, prob[0, :]).nonzero()[0][0] + + v = numpy.concatenate([v, pred[None, None]], axis=1) + sys.stdout.write(chr(int(pred))) + sys.stdout.flush() + + prob, = self.f(pred[None, None]) + print + print "-------" + print + - for i in range(v.shape[0]): - print "Sample:", ''.join([chr(int(v[i, j])) for j in range(v.shape[1])]) +class ResetStates(SimpleExtension): + def __init__(self, state_vars, **kwargs): + super(ResetStates, self).__init__(**kwargs) + + self.f = theano.function( + inputs=[], outputs=[], + updates=[(v, v.zeros_like()) for v in state_vars]) + + def do(self, which_callback, *args): + self.f() def train_model(m, train_stream, dump_path=None): @@ -76,17 +108,17 @@ def train_model(m, train_stream, dump_path=None): step_rule=config.step_rule, params=cg.parameters) - algorithm.add_updates(m.updates) + algorithm.add_updates(m.states) # Load the parameters from a dumped model if dump_path is not None: try: - logger.info('Loading parameters...') with closing(numpy.load(dump_path)) as source: + logger.info('Loading parameters...') param_values = {'/' + name.replace(BRICK_DELIMITER, '/'): source[name] for name in source.keys() if name != 'pkl' and not 'None' in name} - model.set_param_values(param_values) + model.set_param_values(param_values) except IOError: pass @@ -96,19 +128,24 @@ def train_model(m, train_stream, dump_path=None): algorithm=algorithm, extensions=[ Checkpoint(path=dump_path, - after_epoch=False, every_n_epochs=config.save_freq), + after_epoch=False, + use_cpickle=True, + every_n_epochs=config.save_freq), TrainingDataMonitoring( [m.cost_reg, m.error_rate_reg, m.cost, m.error_rate], prefix='train', every_n_epochs=1), Printing(every_n_epochs=1, after_epoch=False), - Plot(document='tr_'+model_name+'_'+config.param_desc, + Plot(document='text_'+model_name+'_'+config.param_desc, channels=[['train_cost', 'train_cost_reg'], ['train_error_rate', 'train_error_rate_reg']], server_url='http://eos21:4201/', every_n_epochs=1, after_epoch=False), - GenText(m, ' ', config.sample_len, every_n_epochs=1, after_epoch=False) + GenText(m, '\nalex\ttu crois ?\n', config.sample_len, + every_n_epochs=config.sample_freq, + after_epoch=False, before_training=True), + ResetStates([v for v, _ in m.states], after_epoch=True) ] ) main_loop.run() |