def __init__(self, input_dim, output_dim, activation = numpy.tanh, dtype=None):
super(ReservoirNode, self).__init__(input_dim=input_dim, output_dim=output_dim, dtype=dtype)
self.activation = activation
self.W_in = 1. * (numpy.random.randint(0, 2, (input_dim, output_dim)) * 2 - 1)
self.b = numpy.zeros((output_dim,))
self.W = sparseReservoirMatrix((output_dim, output_dim), 0.27)
self.state = numpy.zeros((output_dim,))
self.params = [self.W_in, self.W, self.b]
def __init__(self, input, n_in, n_out, activation = T.tanh):
self.input = input
W_in_values = 1. * (numpy.random.randint(0, 2, (n_in, n_out)) * 2 - 1)
self.W_in = theano.shared(value = W_in_values, name ='W_in')
W_res_values = sparseReservoirMatrix((n_out, n_out), 0.27)
self.W = theano.shared(value = W_res_values, name ='W')
b_values = numpy.zeros((n_out,), dtype= theano.config.floatX)
self.b = theano.shared(value= b_values, name ='b')
self.output = numpy.zeros((n_out,), dtype= theano.config.floatX)
self.output = theano.shared(value= b_values, name ='out')
self.output = activation(T.dot(input, self.W_in) + self.b + T.dot(self.output, self.W))
self.params = [self.W_in, self.W, self.b]
def __init__(self, input, n_in, n_out, activation=T.tanh):
self.input = input
W_in_values = 1. * (numpy.random.randint(0, 2, (n_in, n_out)) * 2 - 1)
self.W_in = theano.shared(value=W_in_values, name='W_in')
W_res_values = sparseReservoirMatrix((n_out, n_out), 0.27)
self.W = theano.shared(value=W_res_values, name='W')
b_values = numpy.zeros((n_out, ), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, name='b')
self.output = numpy.zeros((n_out, ), dtype=theano.config.floatX)
self.output = theano.shared(value=b_values, name='out')
self.output = activation(
T.dot(input, self.W_in) + self.b + T.dot(self.output, self.W))
self.params = [self.W_in, self.W, self.b]
def __init__(self,
input_dim,
output_dim,
activation=numpy.tanh,
dtype=None):
super(ReservoirNode, self).__init__(input_dim=input_dim,
output_dim=output_dim,
dtype=dtype)
self.activation = activation
self.W_in = 1. * (numpy.random.randint(0, 2,
(input_dim, output_dim)) * 2 -
1)
self.b = numpy.zeros((output_dim, ))
self.W = sparseReservoirMatrix((output_dim, output_dim), 0.27)
self.state = numpy.zeros((output_dim, ))
self.params = [self.W_in, self.W, self.b]
import mdp
import cPickle as pickle
from my_utils import draw, sparseReservoirMatrix, setVocLut
vocabulary, lut = setVocLut()
f = open('../languages/tong6words.enum')
sentences = pickle.load(f)
words = []
for sentence in sentences:
words.extend(sentence)
in_d = len(vocabulary)
out_d = 200
w_reservoir = sparseReservoirMatrix((out_d, out_d), 0.27)
reservoir = og.nodes.ReservoirNode(input_dim=in_d,
output_dim=out_d,
input_scaling=1,
reset_states=False,
w=w_reservoir,
spectral_radius=0.97)
readout = og.nodes.RidgeRegressionNode(input_dim=reservoir.output_dim,
output_dim=len(vocabulary),
ridge_param=0.)
flow = mdp.Flow([reservoir, readout], verbose=1)
flownode = mdp.hinet.FlowNode(flow)
""" Train """
from my_utils import setVocLut, sparseReservoirMatrix
import cPickle as pickle
import time
tstart = time.time()
vocabulary, lut = setVocLut()
f = open('../languages/tong6words.enum')
sentences = pickle.load(f)
print "... loaded", len(sentences), "sentences."
for k in range(len(sentences)):
sentences[k].insert(0,'.')
in_d = len(vocabulary)
out_d = 10
w_reservoir = sparseReservoirMatrix((out_d,out_d), 0.27)
reservoir = og.nodes.ReservoirNode(input_dim=in_d
,output_dim=out_d
,input_scaling=1
,reset_states=False
#,w_in=w_input
,w=w_reservoir
,spectral_radius=0.97)
""" Build dataset """
print "... building dataset"
source = []
train_label = []
for s in sentences:
words = []
