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# 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 :
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