Connect it to IRC ; add GFGRU model

1 files changed, 237 insertions, 0 deletions

diff --git a/gfgru.py b/gfgru.py
new file mode 100644
index 0000000..9a612e1
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+++ b/gfgru.py
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+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)]
+