Revive project

1 files changed, 248 insertions, 0 deletions

diff --git a/model/cchlstm.py b/model/cchlstm.py
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+++ b/model/cchlstm.py
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+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
+