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import theano
from theano import tensor
import numpy
from blocks.algorithms import Momentum, AdaDelta, RMSProp
from blocks.bricks import Tanh, Softmax, Linear, MLP
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
# 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
hidden_dims = [512, 512]
activation_function = Tanh()
all_hidden_for_output = False
w_noise_std = 0.01
i_dropout = 0.5
step_rule = 'adadelta'
param_desc = '%s-%sHO-n%s-d%s-%dx%d(%d)-%s' % (
repr(hidden_dims),
'all' if all_hidden_for_output else 'last',
repr(w_noise_std),
repr(i_dropout),
num_seqs, seq_len, seq_div_size,
step_rule
)
if step_rule == 'rmsprop':
step_rule = RMSProp()
elif step_rule == 'adadelta':
step_rule = AdaDelta()
else:
assert(False)
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])
dims = [io_dim] + hidden_dims
states = [in_onehot.dimshuffle(1, 0, 2)]
bricks = []
updates = []
for i in xrange(1, len(dims)):
init_state = theano.shared(numpy.zeros((num_seqs, dims[i])).astype(theano.config.floatX),
name='st0_%d'%i)
init_cell = theano.shared(numpy.zeros((num_seqs, dims[i])).astype(theano.config.floatX),
name='cell0_%d'%i)
linear = Linear(input_dim=dims[i-1], output_dim=4*dims[i],
name="lstm_in_%d"%i)
lstm = LSTM(dim=dims[i], activation=activation_function,
name="lstm_rec_%d"%i)
new_states, new_cells = lstm.apply(linear.apply(states[-1]),
states=init_state,
cells=init_cell)
updates.append((init_state, new_states[-1, :, :]))
updates.append((init_cell, new_cells[-1, :, :]))
states.append(new_states)
bricks = bricks + [linear, lstm]
states = [s.dimshuffle(1, 0, 2).reshape((inp.shape[0] * inp.shape[1], dim))
for dim, s in zip(dims, states)]
if all_hidden_for_output:
top_linear = MLP(dims=[sum(hidden_dims), io_dim],
activations=[Softmax()],
name="pred_mlp")
bricks.append(top_linear)
out = top_linear.apply(tensor.concatenate(states[1:], axis=1))
else:
top_linear = MLP(dims=[hidden_dims[-1], io_dim],
activations=[None],
name="pred_mlp")
bricks.append(top_linear)
out = top_linear.apply(states[-1])
out = out.reshape((inp.shape[0], inp.shape[1], io_dim))
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
for brick in bricks:
brick.weights_init = IsotropicGaussian(0.1)
brick.biases_init = Constant(0.)
brick.initialize()
# apply noise
cg = ComputationGraph([cost, error_rate])
if w_noise_std > 0:
noise_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, noise_vars, w_noise_std)
if i_dropout > 0:
cg = apply_dropout(cg, states[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.pred = pred
self.updates = updates
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