diff options
Diffstat (limited to 'lstm.py')
| -rw-r--r-- | lstm.py | 49 |
1 files changed, 29 insertions, 20 deletions
@@ -14,31 +14,34 @@ from blocks.graph import ComputationGraph, apply_noise, apply_dropout # An epoch will be composed of 'num_seqs' sequences of len 'seq_len' # divided in chunks of lengh 'seq_div_size' num_seqs = 20 -seq_len = 2000 -seq_div_size = 100 +seq_len = 5000 +seq_div_size = 200 io_dim = 256 -hidden_dims = [512, 512, 512] +hidden_dims = [1024, 1024, 1024] activation_function = Tanh() i2h_all = True # input to all hidden layers or only first layer h2o_all = True # all hiden layers to output or only last layer -w_noise_std = 0.01 +w_noise_std = 0.02 i_dropout = 0.5 -step_rule = 'momentum' +l1_reg = 0 + +step_rule = 'adadelta' learning_rate = 0.1 momentum = 0.9 -param_desc = '%s-%sIH,%sHO-n%s-d%s-%dx%d(%d)-%s' % ( +param_desc = '%s-%sIH,%sHO-n%s-d%s-l1r%s-%dx%d(%d)-%s' % ( repr(hidden_dims), 'all' if i2h_all else 'first', 'all' if h2o_all else 'last', repr(w_noise_std), repr(i_dropout), + repr(l1_reg), num_seqs, seq_len, seq_div_size, step_rule ) @@ -46,8 +49,9 @@ param_desc = '%s-%sIH,%sHO-n%s-d%s-%dx%d(%d)-%s' % ( save_freq = 5 # parameters for sample generation -sample_len = 60 -sample_temperature = 0.3 +sample_len = 1000 +sample_temperature = 0.7 #0.5 +sample_freq = 10 if step_rule == 'rmsprop': step_rule = RMSProp() @@ -68,9 +72,9 @@ class Model(): # Construct hidden states dims = [io_dim] + hidden_dims - states = [in_onehot.dimshuffle(1, 0, 2)] + hidden = [in_onehot.dimshuffle(1, 0, 2)] bricks = [] - updates = [] + states = [] for i in xrange(1, len(dims)): init_state = theano.shared(numpy.zeros((num_seqs, dims[i])).astype(theano.config.floatX), name='st0_%d'%i) @@ -80,32 +84,32 @@ class Model(): linear = Linear(input_dim=dims[i-1], output_dim=4*dims[i], name="lstm_in_%d"%i) bricks.append(linear) - inter = linear.apply(states[-1]) + inter = linear.apply(hidden[-1]) if i2h_all and i > 1: linear2 = Linear(input_dim=dims[0], output_dim=4*dims[i], name="lstm_in0_%d"%i) bricks.append(linear2) - inter = inter + linear2.apply(states[0]) + inter = inter + linear2.apply(hidden[0]) inter.name = 'inter_bis_%d'%i lstm = LSTM(dim=dims[i], activation=activation_function, name="lstm_rec_%d"%i) bricks.append(lstm) - new_states, new_cells = lstm.apply(inter, + new_hidden, new_cells = lstm.apply(inter, states=init_state, cells=init_cell) - updates.append((init_state, new_states[-1, :, :])) - updates.append((init_cell, new_cells[-1, :, :])) + states.append((init_state, new_hidden[-1, :, :])) + states.append((init_cell, new_cells[-1, :, :])) - states.append(new_states) + hidden.append(new_hidden) - states = [s.dimshuffle(1, 0, 2) for s in states] + hidden = [s.dimshuffle(1, 0, 2) for s in hidden] # Construct output from hidden states out = None - layers = zip(dims, states)[1:] + layers = zip(dims, hidden)[1:] if not h2o_all: layers = [layers[-1]] for i, (dim, state) in enumerate(layers): @@ -136,9 +140,14 @@ class Model(): noise_vars = VariableFilter(roles=[WEIGHT])(cg) cg = apply_noise(cg, noise_vars, w_noise_std) if i_dropout > 0: - cg = apply_dropout(cg, states[1:], i_dropout) + cg = apply_dropout(cg, hidden[1:], i_dropout) [cost_reg, error_rate_reg] = cg.outputs + # add l1 regularization + if l1_reg > 0: + l1pen = sum(abs(st).mean() for st in hidden[1:]) + cost_reg = cost_reg + l1_reg * l1pen + self.cost = cost self.error_rate = error_rate self.cost_reg = cost_reg @@ -146,5 +155,5 @@ class Model(): self.out = out self.pred = pred - self.updates = updates + self.states = states |