This HPC stuff doesn't work very well.

1 files changed, 134 insertions, 0 deletions

diff --git a/model/hpc_gru.py b/model/hpc_gru.py
new file mode 100644
index 0000000..bd01633
--- /dev/null
+++ b/model/hpc_gru.py
@@ -0,0 +1,134 @@
+# HPC-GRU : Hierarchical Predictive Coding GRU
+
+import theano
+from theano import tensor
+import numpy
+
+from blocks.bricks import Softmax, Tanh, Logistic, Linear, MLP, Identity
+from blocks.bricks.recurrent import GatedRecurrent
+
+from blocks.filter import VariableFilter
+from blocks.roles import WEIGHT
+from blocks.graph import ComputationGraph, apply_noise
+
+
+class Model():
+    def __init__(self, config):
+        inp = tensor.imatrix('bytes')
+
+        embed = theano.shared(config.embedding_matrix.astype(theano.config.floatX),
+                              name='embedding_matrix')
+        in_repr = embed[inp.flatten(), :].reshape((inp.shape[0], inp.shape[1], config.repr_dim))
+        in_repr.name = 'in_repr'
+
+        bricks = []
+        states = []
+
+        # Construct predictive GRU hierarchy
+        hidden = []
+        costs = []
+        next_target = in_repr.dimshuffle(1, 0, 2)
+        for i, (hdim, cf, q) in enumerate(zip(config.hidden_dims,
+                                                   config.cost_factors,
+                                                   config.hidden_q)):
+            init_state = theano.shared(numpy.zeros((config.num_seqs, hdim)).astype(theano.config.floatX),
+                                       name='st0_%d'%i)
+
+            linear = Linear(input_dim=config.repr_dim, output_dim=3*hdim,
+                            name="lstm_in_%d"%i)
+            lstm = GatedRecurrent(dim=hdim, activation=config.activation_function,
+                        name="lstm_rec_%d"%i)
+            linear2 = Linear(input_dim=hdim, output_dim=config.repr_dim, name='lstm_out_%d'%i)
+            tanh = Tanh('lstm_out_tanh_%d'%i)
+            bricks += [linear, lstm, linear2, tanh]
+            if i > 0:
+                linear1 = Linear(input_dim=config.hidden_dims[i-1], output_dim=3*hdim,
+                                 name='lstm_in2_%d'%i)
+                bricks += [linear1]
+
+            next_target = tensor.cast(next_target, dtype=theano.config.floatX)
+            inter = linear.apply(theano.gradient.disconnected_grad(next_target))
+            if i > 0:
+                inter += linear1.apply(theano.gradient.disconnected_grad(hidden[-1][:-1,:,:]))
+            new_hidden = lstm.apply(inputs=inter[:,:,:hdim],
+                                    gate_inputs=inter[:,:,hdim:],
+                                    states=init_state)
+            states.append((init_state, new_hidden[-1, :, :]))
+
+            hidden += [tensor.concatenate([init_state[None,:,:], new_hidden],axis=0)]
+            pred = tanh.apply(linear2.apply(hidden[-1][:-1,:,:]))
+            costs += [numpy.float32(cf) * (-next_target * pred).sum(axis=2).mean()]
+            costs += [numpy.float32(cf) * q * abs(pred).sum(axis=2).mean()]
+            diff = next_target - pred
+            next_target = tensor.ge(diff, 0.5) - tensor.le(diff, -0.5)
+
+
+        # Construct output from hidden states
+        hidden = [s.dimshuffle(1, 0, 2) for s in hidden]
+
+        out_parts = []
+        out_dims = config.out_hidden + [config.io_dim]
+        for i, (dim, state) in enumerate(zip(config.hidden_dims, hidden)):
+            pred_linear = Linear(input_dim=dim, output_dim=out_dims[0],
+                                name='pred_linear_%d'%i)
+            bricks.append(pred_linear)
+            lin = theano.gradient.disconnected_grad(state)
+            out_parts.append(pred_linear.apply(lin))
+
+        # Do prediction and calculate cost
+        out = sum(out_parts)
+
+        if len(out_dims) > 1:
+            out = config.out_hidden_act[0](name='out_act0').apply(out)
+            mlp = MLP(dims=out_dims,
+                      activations=[x(name='out_act%d'%i) for i, x in enumerate(config.out_hidden_act[1:])]
+                                 +[Identity()],
+                      name='out_mlp')
+            bricks.append(mlp)
+            out = mlp.apply(out.reshape((inp.shape[0]*(inp.shape[1]+1),-1))
+                           ).reshape((inp.shape[0],inp.shape[1]+1,-1))
+
+        pred = out.argmax(axis=2)
+
+        cost = Softmax().categorical_cross_entropy(inp.flatten(),
+                                                   out[:,:-1,:].reshape((inp.shape[0]*inp.shape[1],
+                                                                config.io_dim))).mean()
+        error_rate = tensor.neq(inp.flatten(), pred[:,:-1].flatten()).mean()
+
+        sgd_cost = cost + sum(costs)
+            
+        # Initialize all bricks
+        for brick in bricks:
+            brick.weights_init = config.weights_init
+            brick.biases_init = config.biases_init
+            brick.initialize()
+
+        # apply noise
+        cg = ComputationGraph([sgd_cost, cost, error_rate]+costs)
+        if config.weight_noise > 0:
+            noise_vars = VariableFilter(roles=[WEIGHT])(cg)
+            cg = apply_noise(cg, noise_vars, config.weight_noise)
+        sgd_cost = cg.outputs[0]
+        cost = cg.outputs[1]
+        error_rate = cg.outputs[2]
+        costs = cg.outputs[3:]
+
+
+        # put stuff into self that is usefull for training or extensions
+        self.sgd_cost = sgd_cost
+
+        sgd_cost.name = 'sgd_cost'
+        for i in range(len(costs)):
+            costs[i].name = 'pred_cost_%d'%i
+        cost.name = 'cost'
+        error_rate.name = 'error_rate'
+        self.monitor_vars = [costs, [cost],
+                             [error_rate]]
+
+        self.out = out[:,1:,:]
+        self.pred = pred[:,1:]
+
+        self.states = states
+
+
+# vim: set sts=4 ts=4 sw=4 tw=0 et :