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# HPC-LSTM : Hierarchical Predictive Coding LSTM
import theano
from theano import tensor
import numpy
from blocks.bricks import Softmax, Tanh, Logistic, Linear, MLP, Identity
from blocks.bricks.recurrent import LSTM
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
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 LSTM 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)
init_cell = theano.shared(numpy.zeros((config.num_seqs, hdim)).astype(theano.config.floatX),
name='cell0_%d'%i)
linear = Linear(input_dim=config.repr_dim, output_dim=4*hdim,
name="lstm_in_%d"%i)
lstm = LSTM(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=4*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, new_cells = lstm.apply(inter,
states=init_state,
cells=init_cell)
states.append((init_state, new_hidden[-1, :, :]))
states.append((init_cell, new_cells[-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 = IsotropicGaussian(0.1)
brick.biases_init = Constant(0.)
brick.initialize()
# 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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