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import theano
from theano import tensor
import numpy
from blocks.bricks import Softmax, Linear
from blocks.bricks.recurrent import recurrent, LSTM
from blocks.filter import VariableFilter
from blocks.roles import WEIGHT
from blocks.graph import ComputationGraph, apply_noise, apply_dropout
class CondLSTM(LSTM):
@recurrent(sequences=['inputs', 'run_mask', 'rst_in_mask', 'rst_out_mask'],
states=['states', 'cells'],
contexts=[], outputs=['states', 'cells', 'outputs'])
def apply_cond(self, inputs, states, cells, run_mask=None, rst_in_mask=None, rst_out_mask=None):
init_states, init_cells = self.initial_states(states.shape[0])
if rst_in_mask:
states = tensor.switch(rst_in_mask[:, None], init_states, states)
cells = tensor.switch(rst_in_mask[:, None], init_cells, cells)
states, cells = self.apply(iterate=False,
inputs=inputs, states=states, cells=cells,
mask=run_mask)
outputs = states
if rst_out_mask:
states = tensor.switch(rst_out_mask[:, None], init_states, states)
cells = tensor.switch(rst_out_mask[:, None], init_cells, cells)
return states, cells, outputs
def compare_matrix(inp, chars):
chars = numpy.array(map(ord, chars), dtype='int8')
assert(inp.ndim == 2)
return tensor.eq(inp[:, :, None], chars[None, None, :]).sum(axis=2).astype(theano.config.floatX)
class Model():
def __init__(self, config):
inp = tensor.imatrix('bytes')
in_onehot = tensor.eq(tensor.arange(config.io_dim, dtype='int32').reshape((1, 1, config.io_dim)),
inp[:, :, None]).astype(theano.config.floatX)
in_onehot.name = 'in_onehot'
hidden_dim = sum(p['dim'] for p in config.layers)
recvalues = tensor.concatenate([in_onehot.dimshuffle(1, 0, 2),
tensor.zeros((inp.shape[1], inp.shape[0], hidden_dim))],
axis=2)
# Construct hidden states
indim = config.io_dim
bricks = []
states = []
for i in xrange(1, len(config.layers)+1):
p = config.layers[i-1]
init_state = theano.shared(numpy.zeros((config.num_seqs, p['dim'])).astype(theano.config.floatX),
name='st0_%d'%i)
init_cell = theano.shared(numpy.zeros((config.num_seqs, p['dim'])).astype(theano.config.floatX),
name='cell0_%d'%i)
linear = Linear(input_dim=indim, output_dim=4*p['dim'],
name="lstm_in_%d"%i)
bricks.append(linear)
inter = linear.apply(recvalues[:, :, :indim])
lstm = CondLSTM(dim=p['dim'], activation=config.activation_function,
name="lstm_rec_%d"%i)
bricks.append(lstm)
run_mask = None
if 'run_on' in p:
run_mask = compare_matrix(inp.T, p['run_on'])
rst_in_mask = None
if 'reset_before' in p:
rst_in_mask = compare_matrix(inp.T, p['reset_before'])
rst_out_mask = None
if 'reset_after' in p:
rst_out_mask = compare_matrix(inp.T, p['reset_after'])
new_hidden, new_cells, rec_out = \
lstm.apply_cond(inputs=inter,
states=init_state, cells=init_cell,
run_mask=run_mask,
rst_in_mask=rst_in_mask, rst_out_mask=rst_out_mask)
states.append((init_state, new_hidden[-1, :, :]))
states.append((init_cell, new_cells[-1, :, :]))
indim2 = indim + p['dim']
recvalues = tensor.set_subtensor(recvalues[:, :, indim:indim2],
rec_out)
indim = indim2
print "**** recvalues", recvalues.dtype
for i, (u, v) in enumerate(states):
print "**** state", i, u.dtype, v.dtype
recvalues = recvalues.dimshuffle(1, 0, 2)
# Construct output from hidden states
top_linear = Linear(input_dim=indim, output_dim=config.io_dim,
name="top_linear")
bricks.append(top_linear)
out = top_linear.apply(recvalues)
out.name = 'out'
# Do prediction and calculate cost
pred = out.argmax(axis=2).astype('int32')
print "**** inp", inp.dtype
print "**** out", out.dtype
print "**** pred", pred.dtype
cost = Softmax().categorical_cross_entropy(inp[:, 1:].flatten(),
out[:, :-1, :].reshape((inp.shape[0]*(inp.shape[1]-1),
config.io_dim))).mean()
cost.name = 'cost'
error_rate = tensor.neq(inp[:, 1:].flatten(), pred[:, :-1].flatten()).astype(theano.config.floatX).mean()
print "**** cost", cost.dtype
print "**** error_rate", error_rate.dtype
# Initialize all bricks
for brick in bricks:
brick.weights_init = config.weights_init
brick.biases_init = config.biases_init
brick.initialize()
# Apply noise and dropout
cg = ComputationGraph([cost, error_rate])
if config.w_noise_std > 0:
noise_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, noise_vars, config.w_noise_std)
if config.i_dropout > 0:
cg = apply_dropout(cg, hidden[1:], config.i_dropout)
[cost_reg, error_rate_reg] = cg.outputs
print "**** cost_reg", cost_reg.dtype
print "**** error_rate_reg", error_rate_reg.dtype
# add l1 regularization
if config.l1_reg > 0:
l1pen = sum(abs(st).mean() for st in hidden[1:])
cost_reg = cost_reg + config.l1_reg * l1pen
if config.l1_reg_weight > 0:
l1pen_w = sum(abs(w).mean() for w in VariableFilter(roles=[WEIGHT])(cg))
cost_reg = cost_reg + config.l1_reg_weight * l1pen_w
cost_reg += 1e-10 # so that it is not the same Theano variable as cost
error_rate_reg += 1e-10
# put stuff into self that is usefull for training or extensions
self.sgd_cost = cost_reg
cost.name = 'cost'
cost_reg.name = 'cost_reg'
error_rate.name = 'error_rate'
error_rate_reg.name = 'error_rate_reg'
self.monitor_vars = [[cost],
[cost_reg],
[error_rate_reg]]
self.out = out
self.pred = pred
self.states = states
# vim: set sts=4 ts=4 sw=4 tw=0 et :
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