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rnn.py
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rnn.py
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import numpy as np
import theano
import theano.tensor as T
from theano import shared
from collections import OrderedDict
from init import init_weight
dtype=T.config.floatX
print "loaded rnn.py"
# Simple RNN class
# optional parameters:
# - activation: lambda x: x ; T.nnet.softmax ;T.nnet.sigmoid
# - cost function: 'mse' 'bce' 'cce'
class Rnn:
def __init__(self, n_in, n_hid, n_out, lr=0.05, single_output=True, output_activation=T.nnet.softmax, cost_function='nll'):
self.n_in = n_in
self.n_hid = n_hid
self.n_out = n_out
self.W_in = init_weight((self.n_in, self.n_hid),'W_in')
self.W_out = init_weight((self.n_hid, self.n_out),'W_out')
self.W_rec = init_weight((self.n_hid, self.n_hid),'W_rec', 'svd')
self.b_hid = shared(np.zeros(shape = n_hid, dtype=dtype))
self.b_out = shared(np.zeros(shape = n_out, dtype=dtype))
self.params = [self.W_in,self.W_out,self.W_rec,self.b_out,self.b_hid]
self.activation = output_activation
def step(x_t, h_tm1):
h_t = T.tanh(T.dot(x_t, self.W_in) + T.dot(h_tm1, self.W_rec) + self.b_hid)
y_t = self.activation(T.dot(h_t, self.W_out) + self.b_out)
return [h_t, y_t]
X = T.matrix() # sequence of vector
Y = T.matrix() # sequence of vector
if single_output:
Y = T.vector()
h0 = shared(np.zeros(shape=self.n_hid, dtype=dtype)) # initial hidden state
lr = shared(np.cast[dtype](lr))
[h_vals, y_vals], _ = theano.scan(fn=step,
sequences=X,
outputs_info=[h0, None])
if single_output:
self.output = y_vals[-1]
else:
self.output = y_vals
cxe = T.mean(T.nnet.binary_crossentropy(self.output, Y))
nll = -T.mean(Y * T.log(self.output)+ (1.- Y) * T.log(1. - self.output))
mse = T.mean((self.output - Y) ** 2)
cost = 0
if cost_function == 'mse':
cost = mse
elif cost_function == 'cxe':
cost = cxe
else:
cost = nll
gparams = T.grad(cost, self.params)
updates = OrderedDict()
for param, gparam in zip(self.params, gparams):
updates[param] = param - gparam * lr
self.loss = theano.function(inputs = [X, Y], outputs = cost)
self.train = theano.function(inputs = [X, Y], outputs = cost, updates=updates)
self.predictions = theano.function(inputs = [X], outputs = self.output)
self.debug = theano.function(inputs = [X, Y], outputs = [X.shape, Y.shape, y_vals.shape, self.output.shape])
# Same class with MiniBatch support
class RnnMiniBatch:
def __init__(self, n_in, n_hid, n_out, lr=0.05, batch_size=64, single_output=True, output_activation=T.nnet.softmax, cost_function='nll'):
self.n_in = n_in
self.n_hid = n_hid
self.n_out = n_out
self.W_in = init_weight((self.n_in, self.n_hid),'W_in')
self.W_out = init_weight((self.n_hid, self.n_out),'W_out')
self.W_rec = init_weight((self.n_hid, self.n_hid),'W_rec', 'svd')
self.b_hid = shared(np.zeros(shape = n_hid, dtype=dtype))
self.b_out = shared(np.zeros(shape = n_out, dtype=dtype))
self.params = [self.W_in,self.W_out,self.W_rec,self.b_out,self.b_hid]
self.activation = output_activation
def step(x_t, h_tm1):
h_t = T.tanh(T.dot(x_t, self.W_in) + T.dot(h_tm1, self.W_rec) + self.b_hid)
y_t = T.nnet.softmax(T.dot(h_t, self.W_out) + self.b_out)
return [h_t, y_t]
X = T.tensor3() # batch of sequence of vector
Y = T.tensor3() # batch of sequence of vector (should be 0 when X is not null)
if single_output:
Y = T.matrix()
else:
Y = T.tensor3()
h0 = shared(np.zeros(shape=(batch_size,self.n_hid), dtype=dtype)) # initial hidden state
lr = shared(np.cast[dtype](lr))
[h_vals, y_vals], _ = theano.scan(fn=step,
sequences=X.dimshuffle(1,0,2),
outputs_info=[h0, None])
if single_output:
self.output = y_vals[-1]
else:
self.output = y_vals.dimshuffle(1,0,2)
cxe = T.mean(T.nnet.binary_crossentropy(self.output, Y))
nll = -T.mean(Y * T.log(self.output)+ (1.- Y) * T.log(1. - self.output))
mse = T.mean((self.output - Y) ** 2)
cost = 0
if cost_function == 'mse':
cost = mse
elif cost_function == 'cxe':
cost = cxe
else:
cost = nll
gparams = T.grad(cost, self.params)
updates = OrderedDict()
for param, gparam in zip(self.params, gparams):
updates[param] = param - gparam * lr
self.loss = theano.function(inputs = [X, Y], outputs = cost)
self.train = theano.function(inputs = [X, Y], outputs = cost, updates=updates)
self.predictions = theano.function(inputs = [X], outputs = self.output)
self.debug = theano.function(inputs = [X, Y], outputs = [X.shape, Y.shape, y_vals.shape, self.output.shape])