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Diffstat (limited to 'gfgru.py')
| -rw-r--r-- | gfgru.py | 237 |
1 files changed, 237 insertions, 0 deletions
diff --git a/gfgru.py b/gfgru.py new file mode 100644 index 0000000..9a612e1 --- /dev/null +++ b/gfgru.py @@ -0,0 +1,237 @@ +import theano +from theano import tensor +import numpy + +from blocks.algorithms import Momentum, AdaDelta, RMSProp +from blocks.bricks import Tanh, Logistic, Softmax, Rectifier, Linear, MLP, Initializable, Identity +from blocks.bricks.base import application, lazy +from blocks.bricks.recurrent import BaseRecurrent, recurrent +from blocks.initialization import IsotropicGaussian, Constant +from blocks.utils import shared_floatx_zeros + +from blocks.filter import VariableFilter +from blocks.roles import WEIGHT, INITIAL_STATE, add_role +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 = 10 +seq_len = 2000 +seq_div_size = 100 + +io_dim = 256 + +recurrent_blocks = [ +# (256, Tanh(), [2048], [Rectifier()]), + (256, Tanh(), [], []), + (256, Tanh(), [], []), + (256, Tanh(), [512], [Rectifier()]), + (256, Tanh(), [512], [Rectifier()]), + ] + +control_hidden = [512] +control_hidden_activations = [Tanh()] + +output_hidden = [512] +output_hidden_activations = [Rectifier()] + +weight_noise_std = 0.02 +recurrent_dropout = 0.5 +control_dropout = 0.5 + +step_rule = 'adadelta' +learning_rate = 0.1 +momentum = 0.9 + + +param_desc = '%s,c%s,o%s-n%s-d%s,%s-%dx%d(%d)-%s' % ( + repr(map(lambda (a, b, c, d): (a, c), recurrent_blocks)), + repr(control_hidden), repr(output_hidden), + repr(weight_noise_std), + repr(recurrent_dropout), repr(control_dropout), + num_seqs, seq_len, seq_div_size, + step_rule + ) + +save_freq = 1 + +# parameters for sample generation +sample_len = 100 +sample_temperature = 0.7 #0.5 +sample_freq = 1 + +if step_rule == 'rmsprop': + step_rule = RMSProp() +elif step_rule == 'adadelta': + step_rule = AdaDelta() +elif step_rule == 'momentum': + step_rule = Momentum(learning_rate=learning_rate, momentum=momentum) +else: + assert(False) + + +class GFGRU(BaseRecurrent, Initializable): + @lazy(allocation=['input_dim', 'recurrent_blocks', 'control_hidden', 'control_hidden_activations']) + def __init__(self, input_dim=None, recurrent_blocks=None, control_hidden=None, control_hidden_activations=None, **kwargs): + super(GFGRU, self).__init__(**kwargs) + + self.input_dim = input_dim + self.recurrent_blocks = recurrent_blocks + self.control_hidden = control_hidden + self.control_hidden_activations = control_hidden_activations + + self.children = control_hidden_activations + + def _allocate(self): + for (_, a, _, b) in recurrent_blocks: + self.children.append(a) + for c in b: + self.children.append(c) + + logistic = Logistic() + self.children.append(logistic) + + self.hidden_total_dim = sum(x for (x, _, _, _) in self.recurrent_blocks) + + control_idim = self.hidden_total_dim + self.input_dim + control_odim = len(self.recurrent_blocks) * (len(self.recurrent_blocks) + 2) + self.control = MLP(dims=[control_idim] + self.control_hidden + [control_odim], + activations=self.control_hidden_activations + [logistic], + name='control') + + self.children.append(self.control) + + self.blocks = [] + self.params = [] + self.initial_states = {} + for i, (dim, act, hdim, hact) in enumerate(self.recurrent_blocks): + idim = self.input_dim + self.hidden_total_dim + if i > 0: + idim = idim + self.recurrent_blocks[i-1][0] + rgate = MLP(dims=[self.hidden_total_dim, self.hidden_total_dim], + activations=[logistic], + name='rgate%d'%i) + idims = [idim] + hdim + if hdim == []: + inter = Identity() + else: + inter = MLP(dims=idims, activations=hact, name='inter%d'%i) + zgate = MLP(dims=[idims[-1], dim], activations=[logistic], name='zgate%d'%i) + nstate = MLP(dims=[idims[-1], dim], activations=[act], name='nstate%d'%i) + for brick in [rgate, inter, zgate, nstate]: + self.children.append(brick) + self.blocks.append((rgate, inter, zgate, nstate)) + + init_states = shared_floatx_zeros((self.hidden_total_dim,), name='initial_states') + self.params = [init_states] + add_role(self.params[0], INITIAL_STATE) + + def get_dim(self, name): + if name == 'states': + return self.hidden_total_dim + return super(GFLSTM, self).get_dim(name) + + @recurrent(sequences=['inputs'], states=['states'], + outputs=['states'], contexts=[]) + def apply(self, inputs=None, states=None): + concat_states = states + + states = [] + offset = 0 + for (dim, _, _, _) in self.recurrent_blocks: + states.append(concat_states[:, offset:offset+dim]) + offset += dim + + concat_input_states = tensor.concatenate([inputs, concat_states], axis=1) + + control = self.control.apply(concat_input_states) + + new_states = [] + for i, (rgate, inter, zgate, nstate) in enumerate(self.blocks): + controls = control[:, i * (len(self.recurrent_blocks)+2):(i+1) * (len(self.recurrent_blocks)+2)] + rgate_v = rgate.apply(concat_states) + r_inputs = tensor.concatenate([s * controls[:, j][:, None] for j, s in enumerate(states)], axis=1) + r_inputs = r_inputs * (1 - rgate_v * controls[:, -1][:, None]) + + more_inputs = [inputs] + if i > 0: + more_inputs = more_inputs + [new_states[-1]] + inter_inputs = tensor.concatenate([r_inputs] + more_inputs, axis=1) + + inter_v = inter.apply(inter_inputs) + zgate_v = zgate.apply(inter_v) + nstate_v = nstate.apply(inter_v) + + nstate_v = nstate_v * (1 - zgate_v * controls[:, -2][:, None]) + new_states.append(nstate_v) + + return tensor.concatenate(new_states, axis=1) + + @application + def initial_state(self, state_name, batch_size, *args, **kwargs): + return tensor.repeat(self.params[0][None, :], repeats=batch_size, axis=0) + + + + +class Model(): + def __init__(self): + inp = tensor.lmatrix('bytes') + + in_onehot = tensor.eq(tensor.arange(io_dim, dtype='int16').reshape((1, 1, io_dim)), + inp[:, :, None]) + in_onehot.name = 'in_onehot' + + gfgru = GFGRU(input_dim=io_dim, + recurrent_blocks=recurrent_blocks, + control_hidden=control_hidden, + control_hidden_activations=control_hidden_activations) + + hidden_total_dim = sum(x for (x, _, _, _) in recurrent_blocks) + + prev_states = theano.shared(numpy.zeros((num_seqs, hidden_total_dim)).astype(theano.config.floatX), + name='states_save') + states = gfgru.apply(in_onehot.dimshuffle(1, 0, 2), + states=prev_states).dimshuffle(1, 0, 2) + new_states = states[:, -1, :] + + out_mlp = MLP(dims=[hidden_total_dim] + output_hidden + [io_dim], + activations=output_hidden_activations + [None], + name='output_mlp') + out = out_mlp.apply(states.reshape((inp.shape[0]*inp.shape[1], hidden_total_dim))).reshape((inp.shape[0], inp.shape[1], io_dim)) + + + + # Do prediction and calculate cost + pred = out.argmax(axis=2) + + cost = Softmax().categorical_cross_entropy(inp[:, 1:].flatten(), + out[:, :-1, :].reshape((inp.shape[0]*(inp.shape[1]-1), + io_dim))) + error_rate = tensor.neq(inp[:, 1:].flatten(), pred[:, :-1].flatten()).mean() + + # Initialize all bricks + for brick in [gfgru, out_mlp]: + brick.weights_init = IsotropicGaussian(0.1) + brick.biases_init = Constant(0.) + brick.initialize() + + # Apply noise and dropout + cg = ComputationGraph([cost, error_rate]) + if weight_noise_std > 0: + noise_vars = VariableFilter(roles=[WEIGHT])(cg) + cg = apply_noise(cg, noise_vars, weight_noise_std) + # if i_dropout > 0: + # cg = apply_dropout(cg, hidden[1:], i_dropout) + [cost_reg, error_rate_reg] = cg.outputs + + + self.cost = cost + self.error_rate = error_rate + self.cost_reg = cost_reg + self.error_rate_reg = error_rate_reg + self.out = out + self.pred = pred + + self.states = [(prev_states, new_states)] + |