From 2f479926c16d2911d0dd878c21de082abfc5b237 Mon Sep 17 00:00:00 2001 From: Alex Auvolat Date: Tue, 8 Mar 2016 13:26:28 +0100 Subject: Revive project --- model/cchlstm.py | 248 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 248 insertions(+) create mode 100644 model/cchlstm.py (limited to 'model/cchlstm.py') diff --git a/model/cchlstm.py b/model/cchlstm.py new file mode 100644 index 0000000..78c9a1f --- /dev/null +++ b/model/cchlstm.py @@ -0,0 +1,248 @@ +import theano +from theano import tensor +import numpy + +from theano.tensor.shared_randomstreams import RandomStreams + +from blocks.algorithms import Momentum, AdaDelta, RMSProp +from blocks.bricks import Tanh, Softmax, Linear, MLP, Initializable +from blocks.bricks.lookup import LookupTable +from blocks.bricks.recurrent import LSTM, BaseRecurrent, recurrent +from blocks.initialization import IsotropicGaussian, Constant + +from blocks.filter import VariableFilter +from blocks.roles import WEIGHT +from blocks.graph import ComputationGraph, apply_noise, apply_dropout + +rng = RandomStreams() + +# An epoch will be composed of 'num_seqs' sequences of len 'seq_len' +# divided in chunks of lengh 'seq_div_size' +num_seqs = 50 +seq_len = 2000 +seq_div_size = 100 + +io_dim = 256 + +# Model structure +hidden_dims = [512, 512, 512, 512, 512] +activation_function = Tanh() + +cond_cert = [0.5, 0.5, 0.5, 0.5] +block_prob = [0.1, 0.1, 0.1, 0.1] + +# Regularization +w_noise_std = 0.02 + +# Step rule +step_rule = 'adadelta' +learning_rate = 0.1 +momentum = 0.9 + + +param_desc = '%s(x%sp%s)-n%s-%dx%d(%d)-%s' % ( + repr(hidden_dims), repr(cond_cert), repr(block_prob), + repr(w_noise_std), + num_seqs, seq_len, seq_div_size, + step_rule + ) + +save_freq = 5 +on_irc = False + +# parameters for sample generation +sample_len = 200 +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 CCHLSTM(BaseRecurrent, Initializable): + def __init__(self, io_dim, hidden_dims, cond_cert, activation=None, **kwargs): + super(CCHLSTM, self).__init__(**kwargs) + + self.cond_cert = cond_cert + + self.io_dim = io_dim + self.hidden_dims = hidden_dims + + self.children = [] + self.layers = [] + + self.softmax = Softmax() + self.children.append(self.softmax) + + for i, d in enumerate(hidden_dims): + i0 = LookupTable(length=io_dim, + dim=4*d, + name='i0-%d'%i) + self.children.append(i0) + + if i > 0: + i1 = Linear(input_dim=hidden_dims[i-1], + output_dim=4*d, + name='i1-%d'%i) + self.children.append(i1) + else: + i1 = None + + lstm = LSTM(dim=d, activation=activation, + name='LSTM-%d'%i) + self.children.append(lstm) + + o = Linear(input_dim=d, + output_dim=io_dim, + name='o-%d'%i) + self.children.append(o) + + self.layers.append((i0, i1, lstm, o)) + + + @recurrent(contexts=[]) + def apply(self, inputs, **kwargs): + + l0i, _, l0l, l0o = self.layers[0] + l0iv = l0i.apply(inputs) + new_states0, new_cells0 = l0l.apply(states=kwargs['states0'], + cells=kwargs['cells0'], + inputs=l0iv, + iterate=False) + l0ov = l0o.apply(new_states0) + + pos = l0ov + ps = new_states0 + + passnext = tensor.ones((inputs.shape[0],)) + out_sc = [new_states0, new_cells0, passnext] + + for i, (cch, (i0, i1, l, o)) in enumerate(zip(self.cond_cert, self.layers[1:])): + pop = self.softmax.apply(pos) + best = pop.max(axis=1) + passnext = passnext * tensor.le(best, cch) * kwargs['pass%d'%i] + + i0v = i0.apply(inputs) + i1v = i1.apply(ps) + + prev_states = kwargs['states%d'%i] + prev_cells = kwargs['cells%d'%i] + new_states, new_cells = l.apply(inputs=i0v + i1v, + states=prev_states, + cells=prev_cells, + iterate=False) + new_states = tensor.switch(passnext[:, None], new_states, prev_states) + new_cells = tensor.switch(passnext[:, None], new_cells, prev_cells) + out_sc += [new_states, new_cells, passnext] + + ov = o.apply(new_states) + pos = tensor.switch(passnext[:, None], pos + ov, pos) + ps = new_states + + return [pos] + out_sc + + def get_dim(self, name): + dims = {'pred': self.io_dim} + for i, d in enumerate(self.hidden_dims): + dims['states%d'%i] = dims['cells%d'%i] = d + if name in dims: + return dims[name] + return super(CCHLSTM, self).get_dim(name) + + @apply.property('sequences') + def apply_sequences(self): + return ['inputs'] + ['pass%d'%i for i in range(len(self.hidden_dims)-1)] + + @apply.property('states') + def apply_states(self): + ret = [] + for i in range(len(self.hidden_dims)): + ret += ['states%d'%i, 'cells%d'%i] + return ret + + @apply.property('outputs') + def apply_outputs(self): + ret = ['pred'] + for i in range(len(self.hidden_dims)): + ret += ['states%d'%i, 'cells%d'%i, 'active%d'%i] + return ret + + +class Model(): + def __init__(self): + inp = tensor.lmatrix('bytes') + + # Make state vars + state_vars = {} + for i, d in enumerate(hidden_dims): + state_vars['states%d'%i] = theano.shared(numpy.zeros((num_seqs, d)) + .astype(theano.config.floatX), + name='states%d'%i) + state_vars['cells%d'%i] = theano.shared(numpy.zeros((num_seqs, d)) + .astype(theano.config.floatX), + name='cells%d'%i) + # Construct brick + cchlstm = CCHLSTM(io_dim=io_dim, + hidden_dims=hidden_dims, + cond_cert=cond_cert, + activation=activation_function) + + # Random pass + passdict = {} + for i, p in enumerate(block_prob): + passdict['pass%d'%i] = rng.binomial(size=(inp.shape[1], inp.shape[0]), p=1-p) + + # Apply it + outs = cchlstm.apply(inputs=inp.dimshuffle(1, 0), + **dict(state_vars.items() + passdict.items())) + states = [] + active_prop = [] + for i in range(len(hidden_dims)): + states.append((state_vars['states%d'%i], outs[3*i+1][-1, :, :])) + states.append((state_vars['cells%d'%i], outs[3*i+2][-1, :, :])) + active_prop.append(outs[3*i+3].mean()) + active_prop[-1].name = 'active_prop_%d'%i + + out = outs[0].dimshuffle(1, 0, 2) + + # 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 [cchlstm]: + brick.weights_init = IsotropicGaussian(0.1) + brick.biases_init = Constant(0.) + brick.initialize() + + # Apply noise and dropoutvars + cg = ComputationGraph([cost, error_rate]) + if w_noise_std > 0: + noise_vars = VariableFilter(roles=[WEIGHT])(cg) + cg = apply_noise(cg, noise_vars, w_noise_std) + [cost_reg, error_rate_reg] = cg.outputs + + self.sgd_cost = cost_reg + self.monitor_vars = [[cost, cost_reg], + [error_rate, error_rate_reg], + active_prop] + + cost.name = 'cost' + cost_reg.name = 'cost_reg' + error_rate.name = 'error_rate' + error_rate_reg.name = 'error_rate_reg' + + self.out = out + self.pred = pred + + self.states = states + -- cgit v1.2.3