def alloc_params(self):
# Refer to Ch. 2 pg. 10 of Sutskever's thesis
hps = self.hps
# Initial hidden state
self.params['h0'] = zeros((hps.hidden_size, hps.hidden_layers))
# Input to hidden, note if first layer is recurrent bih is redundant
self.params['Wih'] = vp_init((hps.hidden_size, hps.output_size))
self.params['bih'] = zeros((hps.hidden_size, 1))
# recurrent weight
# NOTE Initialization important for grad check, don't use vp_init?
self.params['Whh'] = vp_init((hps.hidden_size, hps.hidden_size))
self.params['bhh'] = zeros((hps.hidden_size, 1))
# Weights between hidden layers
for k in xrange(1, hps.hidden_layers):
self.params['Wh%d' % k] = vp_init((hps.hidden_size, hps.hidden_size))
self.params['bh%d' % k] = zeros((hps.hidden_size, 1))
# Hidden to output
self.params['Who'] = vp_init((hps.output_size, hps.hidden_size))
self.params['bho'] = zeros((hps.output_size, 1))
# Keep around last hidden state in case want to resume RNN from there
self.last_h = None
self.count_params()
def alloc_params(self):
# Refer to Ch. 2 pg. 10 of Sutskever's thesis
hps = self.hps
# Initial hidden state
self.params['h0'] = zeros((hps.hidden_size, hps.hidden_layers))
# Input to hidden, note if first layer is recurrent bih is redundant
self.params['Wih'] = vp_init((hps.hidden_size, hps.output_size))
self.params['bih'] = zeros((hps.hidden_size, 1))
# recurrent weight
# NOTE Initialization important for grad check, don't use vp_init?
self.params['Whh'] = vp_init((hps.hidden_size, hps.hidden_size))
self.params['bhh'] = zeros((hps.hidden_size, 1))
# Weights between hidden layers
for k in xrange(1, hps.hidden_layers):
self.params['Wh%d' % k] = vp_init(
(hps.hidden_size, hps.hidden_size))
self.params['bh%d' % k] = zeros((hps.hidden_size, 1))
# Hidden to output
self.params['Who'] = vp_init((hps.output_size, hps.hidden_size))
self.params['bho'] = zeros((hps.output_size, 1))
# Keep around last hidden state in case want to resume RNN from there
self.last_h = None
self.count_params()
def alloc_params(self):
hps = self.hps
self.params['Wih'] = vp_init((hps.hidden_size, hps.input_size))
self.params['bih'] = zeros((hps.hidden_size, 1))
for k in xrange(hps.hidden_layers - 1):
self.params['W%d' % (k+1)] = vp_init((hps.hidden_size, hps.hidden_size))
self.params['b%d' % (k+1)] = zeros((hps.hidden_size, 1))
self.params['Who'] = vp_init((hps.output_size, hps.hidden_size))
self.params['bho'] = zeros((hps.output_size, 1))
self.count_params()
def alloc_params(self):
hps = self.hps
self.params['Wih'] = vp_init((hps.hidden_size, hps.input_size))
self.params['bih'] = zeros((hps.hidden_size, 1))
for k in xrange(hps.hidden_layers - 1):
self.params['W%d' % (k + 1)] = vp_init(
(hps.hidden_size, hps.hidden_size))
self.params['b%d' % (k + 1)] = zeros((hps.hidden_size, 1))
self.params['Who'] = vp_init((hps.output_size, hps.hidden_size))
self.params['bho'] = zeros((hps.output_size, 1))
self.count_params()
def alloc_params(self):
hps = self.hps
self.params['Wih'] = vp_init((hps.hidden_size, hps.input_size))
self.params['Wsh'] = vp_init((hps.hidden_size, hps.source_size))
self.params['bih'] = zeros((hps.hidden_size, 1))
for k in xrange(hps.hidden_layers - 1):
self.params['W%d' % (k+1)] = vp_init((hps.hidden_size, hps.hidden_size))
self.params['b%d' % (k+1)] = zeros((hps.hidden_size, 1))
self.params['Who'] = vp_init((hps.output_size, hps.hidden_size))
self.params['bho'] = zeros((hps.output_size, 1))
self.count_params()
# Allocate grads as well
self.grads = {}
for k in self.params:
self.grads[k] = empty(self.params[k].shape)
logger.info('Allocated gradients')
