def create_parameters(self):
n_in, n_hidden = self.n_in, self.n_hidden
activation = self.activation
self.w1 = create_shared(random_init((n_in,)), name="w1")
self.w2 = create_shared(random_init((n_hidden,)), name="w2")
bias_val = random_init((1,))[0]
self.bias = theano.shared(np.cast[theano.config.floatX](bias_val))
rlayer = RCNN((n_in+1), n_hidden, activation=activation, order=2)
self.rlayer = rlayer
self.layers = [ rlayer ]
def create_parameters(self):
n_in, n_hidden = self.n_in, self.n_hidden
activation = self.activation
self.w1 = create_shared(random_init((n_in, )), name="w1")
self.w2 = create_shared(random_init((n_hidden, )), name="w2")
bias_val = random_init((1, ))[0]
self.bias = theano.shared(np.cast[theano.config.floatX](bias_val))
rlayer = RCNN((n_in + 1), n_hidden, activation=activation, order=2)
self.rlayer = rlayer
self.layers = [rlayer]
def create_parameters(self):
n_in, n_genclassess = self.n_in, self.n_genclassess
activation = self.activation
seed = self.seed
print 'in create parameters'
self.w_s = create_shared(random_init((n_in, n_genclassess), seed=seed),
name="w1")
bias_val_s = random_init((n_genclassess, ), seed=seed)[0]
self.bias_s = theano.shared(np.cast[theano.config.floatX](bias_val_s))
self.layers = []
def __init__(self, n_in, n_out, activation=tanh,
order=1, clip_gradients=False):
self.n_in = n_in
self.n_out = n_out
self.activation = activation
self.order = order
self.clip_gradients = clip_gradients
self.input_shape = (None, 1, n_in, None)
self.filter_shape = (n_out, 1, n_in, order*2-1)
self.W = create_shared(random_init(self.filter_shape), name="W")
self.bias = create_shared(random_init((n_out,)), name="bias")