diff options
| -rw-r--r-- | model/lstm.py | 25 |
1 files changed, 18 insertions, 7 deletions
diff --git a/model/lstm.py b/model/lstm.py index abd44e0..4d715d5 100644 --- a/model/lstm.py +++ b/model/lstm.py @@ -15,8 +15,8 @@ class Model(): def __init__(self, config): inp = tensor.imatrix('bytes') - in_onehot = tensor.eq(tensor.arange(config.io_dim, dtype='int16').reshape((1, 1, config.io_dim)), - inp[:, :, None]) + in_onehot = tensor.eq(tensor.arange(config.io_dim, dtype='int32').reshape((1, 1, config.io_dim)), + inp[:, :, None]).astype(theano.config.floatX) in_onehot.name = 'in_onehot' # Construct hidden states @@ -54,6 +54,9 @@ class Model(): hidden.append(new_hidden) + for i, (u, v) in enumerate(states): + print "**** state", i, u.dtype, v.dtype + hidden = [s.dimshuffle(1, 0, 2) for s in hidden] # Construct output from hidden states @@ -66,16 +69,22 @@ class Model(): name='top_linear_%d'%i) bricks.append(top_linear) out_i = top_linear.apply(state) + print "**** out", i, out_i.dtype out = out_i if out is None else out + out_i out.name = 'out_part_%d'%i # Do prediction and calculate cost - pred = out.argmax(axis=2) + pred = out.argmax(axis=2).astype('int32') + print "**** inp", inp.dtype + print "**** out", out.dtype + print "**** pred", pred.dtype cost = Softmax().categorical_cross_entropy(inp[:, 1:].flatten(), out[:, :-1, :].reshape((inp.shape[0]*(inp.shape[1]-1), config.io_dim))).mean() - error_rate = tensor.neq(inp[:, 1:].flatten(), pred[:, :-1].flatten()).mean() + error_rate = tensor.neq(inp[:, 1:].flatten(), pred[:, :-1].flatten()).astype(theano.config.floatX).mean() + print "**** cost", cost.dtype + print "**** error_rate", error_rate.dtype # Initialize all bricks for brick in bricks: @@ -91,13 +100,15 @@ class Model(): if config.i_dropout > 0: cg = apply_dropout(cg, hidden[1:], config.i_dropout) [cost_reg, error_rate_reg] = cg.outputs + print "**** cost_reg", cost_reg.dtype + print "**** error_rate_reg", error_rate_reg.dtype # add l1 regularization if config.l1_reg > 0: l1pen = sum(abs(st).mean() for st in hidden[1:]) cost_reg = cost_reg + config.l1_reg * l1pen - cost_reg += 1e-10 # so that it is not the same Theano variable + cost_reg += 1e-10 # so that it is not the same Theano variable as cost error_rate_reg += 1e-10 # put stuff into self that is usefull for training or extensions @@ -107,8 +118,8 @@ class Model(): cost_reg.name = 'cost_reg' error_rate.name = 'error_rate' error_rate_reg.name = 'error_rate_reg' - self.monitor_vars = [[cost, cost_reg], - [error_rate, error_rate_reg]] + self.monitor_vars = [[cost_reg], + [error_rate_reg]] self.out = out self.pred = pred |