def __init__(self, num_input, num_output, input_layers, name=""):
self.name = name
if len(input_layers) >= 2:
print "number of input layers: %s" % len(input_layers)
print "len of list comprehension: %s" % len([input_layer.output() for input_layer in input_layers])
self.X = T.concatenate([input_layer.output() for input_layer in input_layers], axis=1)
else:
self.X = input_layers[0].output()
self.W_yh = random_weights((num_input, num_output),name=name+"_W_yh")
self.b_y = random_weights((num_output,), name=name+"_b_y")
self.params = [self.W_yh]
self.bias = [self.b_y]
def __init__(self, num_input, num_cells, input_layer=None, name=""):
"""
LSTM Layer
Takes as input sequence of inputs, returns sequence of outputs
"""
self.name = name
self.num_input = num_input
self.num_cells = num_cells
self.X = input_layer.output()
self.h0 = theano.shared(floatX(np.zeros(num_cells)))
self.s0 = theano.shared(floatX(np.zeros(num_cells)))
self.W_gx = random_weights((num_input, num_cells))
self.W_ix = random_weights((num_input, num_cells))
self.W_fx = random_weights((num_input, num_cells))
self.W_ox = random_weights((num_input, num_cells))
self.W_gh = random_weights((num_cells, num_cells))
self.W_ih = random_weights((num_cells, num_cells))
self.W_fh = random_weights((num_cells, num_cells))
self.W_oh = random_weights((num_cells, num_cells))
self.b_g = zeros(num_cells)
self.b_i = zeros(num_cells)
self.b_f = zeros(num_cells)
self.b_o = zeros(num_cells)
self.params = [self.W_gx, self.W_ix, self.W_ox, self.W_fx,
self.W_gh, self.W_ih, self.W_oh, self.W_fh,
self.b_g, self.b_i, self.b_f, self.b_o,
]
def __init__(self, num_input, num_cells, input_layer=None, name=""):
"""
LSTM Layer
Takes as input sequence of inputs, returns sequence of outputs
"""
self.name = name
self.num_input = num_input
self.num_cells = num_cells
self.X = input_layer.output()
self.h0 = theano.shared(floatX(np.zeros(num_cells)))
self.s0 = theano.shared(floatX(np.zeros(num_cells)))
self.W_gx = random_weights((num_input, num_cells))
self.W_ix = random_weights((num_input, num_cells))
self.W_fx = random_weights((num_input, num_cells))
self.W_ox = random_weights((num_input, num_cells))
self.W_gh = random_weights((num_cells, num_cells))
self.W_ih = random_weights((num_cells, num_cells))
self.W_fh = random_weights((num_cells, num_cells))
self.W_oh = random_weights((num_cells, num_cells))
self.b_g = zeros(num_cells)
self.b_i = zeros(num_cells)
self.b_f = zeros(num_cells)
self.b_o = zeros(num_cells)
self.params = [self.W_gx, self.W_ix, self.W_ox, self.W_fx,
self.W_gh, self.W_ih, self.W_oh, self.W_fh,
self.b_g, self.b_i, self.b_f, self.b_o,
]
def __init__(self, num_input, num_output, input_layers, name=""):
self.name = name
if len(input_layers) >= 2:
print "number of input layers: %s" % len(input_layers)
print "len of list comprehension: %s" % len(
[input_layer.output() for input_layer in input_layers])
self.X = T.concatenate(
[input_layer.output() for input_layer in input_layers], axis=1)
else:
self.X = input_layers[0].output()
self.W_yh = random_weights((num_input, num_output),
name=name + "_W_yh")
self.b_y = random_weights((num_output, ), name=name + "_b_y")
self.params = [self.W_yh]
self.bias = [self.b_y]
def __init__(self,
input_layer,
filter_shape,
subsample=(1, 1),
name="",
border_mode='valid'):
self.X = input_layer.output()
self.border_mode = border_mode
self.name = name
self.subsample = subsample
self.filter_shape = filter_shape
fan_in = np.prod(filter_shape[1:])
fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) /
np.prod(subsample))
W_bound = np.sqrt(6. / (fan_in + fan_out))
rng = np.random.RandomState(23455)
self.W = theano.shared(np.asarray(rng.uniform(low=-W_bound,
high=W_bound,
size=filter_shape),
dtype=theano.config.floatX),
name=name + " W",
borrow=True)
self.b = random_weights((filter_shape[0], ), name=name + " bias")
self.params = [self.W]
self.bias = [self.b]
self.get_weight = theano.function([], self.W)
def __init__(self, num_input, num_output, input_layer, name=""):
self.num_input = num_input
self.num_output = num_output
self.X = input_layer.output()
self.W_yh = random_weights((num_input, num_output),name="W_yh")
self.b_y = zeros(num_output, name="b_y")
self.params = [self.W_yh, self.b_y]
def __init__(self, num_input, num_output, input_layer, name=""):
self.num_input = num_input
self.num_output = num_output
self.X = input_layer.output()
self.W_yh = random_weights((num_input, num_output), name="W_yh")
self.b_y = zeros(num_output, name="b_y")
self.params = [self.W_yh, self.b_y]
def __init__(self, num_input, num_cells, input_layer=None, name=""):
"""
LSTM Layer
Takes as input sequence of inputs, returns sequence of outputs
Currently takes only one input layer
"""
self.name = name
self.num_input = num_input
self.num_cells = num_cells
#Setting the X as the input layer
self.X = input_layer.output()
self.h0 = theano.shared(floatX(np.zeros((1, num_cells))))
self.s0 = theano.shared(floatX(np.zeros((1, num_cells))))
#Initializing the weights
self.W_gx = random_weights((num_input, num_cells),
name=self.name + "W_gx")
self.W_ix = random_weights((num_input, num_cells),
name=self.name + "W_ix")
self.W_fx = random_weights((num_input, num_cells),
name=self.name + "W_fx")
self.W_ox = random_weights((num_input, num_cells),
name=self.name + "W_ox")
self.W_gh = random_weights((num_cells, num_cells),
name=self.name + "W_gh")
self.W_ih = random_weights((num_cells, num_cells),
name=self.name + "W_ih")
self.W_fh = random_weights((num_cells, num_cells),
name=self.name + "W_fh")
self.W_oh = random_weights((num_cells, num_cells),
name=self.name + "W_oh")
self.b_g = zeros(num_cells, name=self.name + "b_g")
self.b_i = zeros(num_cells, name=self.name + "b_i")
self.b_f = zeros(num_cells, name=self.name + "b_f")
self.b_o = zeros(num_cells, name=self.name + "b_o")
self.params = [
self.W_gx,
self.W_ix,
self.W_ox,
self.W_fx,
self.W_gh,
self.W_ih,
self.W_oh,
self.W_fh,
self.b_g,
self.b_i,
self.b_f,
self.b_o,
]
self.output()
def __init__(self, num_input, num_output, input_layers, name=""):
if len(input_layers) >= 2:
self.X = T.concatenate([input_layer.output() for input_layer in input_layers], axis=1)
else:
self.X = input_layers[0].output()
self.W_yh = random_weights((num_input, num_output),name="W_yh")
self.b_y = zeros(num_output, name="b_y")
self.params = [self.W_yh, self.b_y]
def __init__(self, num_input, num_output, input_layer, temperature=1.0, name=""):
self.name = ""
self.X = input_layer.output()
self.params = []
self.temp = temperature
self.W_yh = random_weights((num_input, num_output))
self.b_y = zeros(num_output)
self.params = [self.W_yh, self.b_y]
def __init__(self, input_layer, num_input, num_output, name=""):
self.X = input_layer.output()
self.name = name
W_bound = np.sqrt(6. / (num_input + num_output))
rng = np.random.RandomState(23495)
self.W = theano.shared(np.asarray(rng.uniform(low=-W_bound, high=W_bound, size=(num_input,num_output)), dtype=theano.config.floatX), name = name + " W",borrow=True)
self.b = random_weights((num_output,), name=name+"_b")
self.params = [self.W]
self.bias = [self.b]
def __init__(self, num_input, num_output, input_layer, temperature=1.0, name=""):
self.name = ""
self.X = input_layer.output()
self.params = []
self.temp = temperature
self.W_yh = random_weights((num_input, num_output))
self.b_y = zeros(num_output)
self.params = [self.W_yh, self.b_y]
def __init__(self, num_input, num_output, input_layers, name=""):
if len(input_layers) >= 2:
self.X = T.concatenate(
[input_layer.output() for input_layer in input_layers], axis=1)
else:
self.X = input_layers[0].output()
self.W_yh = random_weights((num_input, num_output), name="W_yh")
self.b_y = zeros(num_output, name="b_y")
self.params = [self.W_yh, self.b_y]
def __init__self(self, num_input, num_output, input_layer, name=""):
self.X = input_layer.output()
self.num_input = num_input
self.num_output = num_output
self.W = random_weights((num_input, num_output))
self.b = zeros(num_output)
self.params = [self.W, self.B]
def __init__self(self, num_input, num_output, input_layer, name=""):
self.X = input_layer.output()
self.num_input = num_input
self.num_output = num_output
self.W = random_weights((num_input, num_output))
self.b = zeros(num_output)
self.params = [self.W, self.B]
def __init__(self, input_layer, num_input, num_cells,batch_size = 8, name="", go_backwards=False, return_sequences = False):
"""
LSTM Layer
"""
self.name = name
self.num_input = num_input
self.num_cells = num_cells
self.return_sequences = return_sequences
self.X = input_layer.output()
self.h0 = theano.shared(floatX(np.zeros(num_cells,)))
self.s0 = theano.shared(floatX(np.zeros(num_cells,)))
self.go_backwards = go_backwards
W_bound_sx = np.sqrt(6. / (num_input + num_cells))
rng = np.random.RandomState(23456)
self.W_gx = theano.shared(np.asarray(rng.uniform(low=-W_bound_sx, high=W_bound_sx, size=(num_input,num_cells)), dtype=theano.config.floatX), name = name + " W_gx",borrow=True)
self.W_ix = theano.shared(np.asarray(rng.uniform(low=-W_bound_sx, high=W_bound_sx, size=(num_input,num_cells)), dtype=theano.config.floatX), name = name + " W_ix",borrow=True)
self.W_fx = theano.shared(np.asarray(rng.uniform(low=-W_bound_sx, high=W_bound_sx, size=(num_input,num_cells)), dtype=theano.config.floatX), name = name + " W_fx",borrow=True)
self.W_ox = theano.shared(np.asarray(rng.uniform(low=-W_bound_sx, high=W_bound_sx, size=(num_input,num_cells)), dtype=theano.config.floatX), name = name + " W_ox",borrow=True)
W_bound_sh = np.sqrt(6. / (num_cells + num_cells))
self.W_gh = theano.shared(np.asarray(rng.uniform(low=-W_bound_sh, high=W_bound_sh, size=(num_cells,num_cells)), dtype=theano.config.floatX), name = name + " W_gh",borrow=True)
self.W_ih = theano.shared(np.asarray(rng.uniform(low=-W_bound_sh, high=W_bound_sh, size=(num_cells,num_cells)), dtype=theano.config.floatX), name = name + " W_ih",borrow=True)
self.W_fh = theano.shared(np.asarray(rng.uniform(low=-W_bound_sh, high=W_bound_sh, size=(num_cells,num_cells)), dtype=theano.config.floatX), name = name + " W_fh",borrow=True)
self.W_oh = theano.shared(np.asarray(rng.uniform(low=-W_bound_sh, high=W_bound_sh, size=(num_cells,num_cells)), dtype=theano.config.floatX), name = name + " W_oh",borrow=True)
self.b_g = random_weights((num_cells,), name=self.name+" b_g")
self.b_i = random_weights((num_cells,), name=self.name+" b_i")
self.b_f = random_weights((num_cells,), name=self.name+" b_f")
self.b_o = random_weights((num_cells,), name=self.name+" b_o")
self.params = [self.W_gx, self.W_ix, self.W_ox, self.W_fx,
self.W_gh, self.W_ih, self.W_oh, self.W_fh,
]
self.bias = [self.b_g, self.b_i, self.b_f, self.b_o]
def __init__(self, num_input, num_cells, input_layers=None, name="", go_backwards=False):
"""
GRU Layer
Takes as input sequence of inputs, returns sequence of outputs
"""
self.name = name
self.num_input = num_input
self.num_cells = num_cells
if len(input_layers) >= 2:
self.X = T.concatenate([input_layer.output() for input_layer in input_layers], axis=1)
else:
self.X = input_layers[0].output()
self.s0 = zeros(num_cells)
self.go_backwards = go_backwards
self.U_z = random_weights((num_input, num_cells), name=self.name+"U_z")
self.W_z = random_weights((num_cells, num_cells), name=self.name+"W_z")
self.U_r = random_weights((num_input, num_cells), name=self.name+"U_r")
self.W_r = random_weights((num_cells, num_cells), name=self.name+"W_r")
self.U_h = random_weights((num_input, num_cells), name=self.name+"U_h")
self.W_h = random_weights((num_cells, num_cells), name=self.name+"W_h")
self.b_z = zeros(num_cells, name=self.name+"b_z")
self.b_r = zeros(num_cells, name=self.name+"b_r")
self.b_h = zeros(num_cells, name=self.name+"b_h")
self.params = [ self.U_z, self.W_z, self.U_r,
self.W_r, self.U_h, self.W_h,
self.b_z, self.b_r, self.b_h
]
self.output()
def __init__(self, input_layer, num_input, num_output, name=""):
self.X = input_layer.output()
self.name = name
W_bound = np.sqrt(6. / (num_input + num_output))
rng = np.random.RandomState(23495)
self.W = theano.shared(np.asarray(rng.uniform(low=-W_bound,
high=W_bound,
size=(num_input,
num_output)),
dtype=theano.config.floatX),
name=name + " W",
borrow=True)
self.b = random_weights((num_output, ), name=name + "_b")
self.params = [self.W]
self.bias = [self.b]
def __init__(self, input_layer, filter_shape, subsample=(1,1),name="",border_mode='valid'):
self.X = input_layer.output()
self.border_mode = border_mode
self.name = name
self.subsample = subsample
self.filter_shape = filter_shape
fan_in = np.prod(filter_shape[1:])
fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) / np.prod(subsample))
W_bound = np.sqrt(6. / (fan_in + fan_out))
rng = np.random.RandomState(23455)
self.W = theano.shared(np.asarray(rng.uniform(low=-W_bound, high=W_bound, size=filter_shape), dtype=theano.config.floatX), name = name + " W",borrow=True)
self.b = random_weights((filter_shape[0],), name=name+" bias")
self.params = [self.W]
self.bias = [self.b]
self.get_weight = theano.function([],self.W)
def __init__(self, num_input, num_cells, input_layers=None, name="", go_backwards=False):
"""
LSTM Layer
Takes as input sequence of inputs, returns sequence of outputs
"""
self.name = name
self.num_input = num_input
self.num_cells = num_cells
if len(input_layers) >= 2:
self.X = T.concatenate([input_layer.output() for input_layer in input_layers], axis=1)
else:
self.X = input_layers[0].output()
self.h0 = theano.shared(floatX(np.zeros(num_cells)))
self.s0 = theano.shared(floatX(np.zeros(num_cells)))
self.go_backwards = go_backwards
self.W_gx = random_weights((num_input, num_cells), name=self.name+"W_gx")
self.W_ix = random_weights((num_input, num_cells), name=self.name+"W_ix")
self.W_fx = random_weights((num_input, num_cells), name=self.name+"W_fx")
self.W_ox = random_weights((num_input, num_cells), name=self.name+"W_ox")
self.W_gh = random_weights((num_cells, num_cells), name=self.name+"W_gh")
self.W_ih = random_weights((num_cells, num_cells), name=self.name+"W_ih")
self.W_fh = random_weights((num_cells, num_cells), name=self.name+"W_fh")
self.W_oh = random_weights((num_cells, num_cells), name=self.name+"W_oh")
self.b_g = zeros(num_cells, name=self.name+"b_g")
self.b_i = zeros(num_cells, name=self.name+"b_i")
self.b_f = zeros(num_cells, name=self.name+"b_f")
self.b_o = zeros(num_cells, name=self.name+"b_o")
self.params = [self.W_gx, self.W_ix, self.W_ox, self.W_fx,
self.W_gh, self.W_ih, self.W_oh, self.W_fh,
self.b_g, self.b_i, self.b_f, self.b_o,
]
self.output()
def __init__(self, num_input, num_cells, input_layer=None, name=""):
"""
LSTM Layer
Takes as input sequence of inputs, returns sequence of outputs
Currently takes only one input layer
"""
self.name = name
self.num_input = num_input
self.num_cells = num_cells
#Setting the X as the input layer
self.X = input_layer.output()
self.h0 = theano.shared(floatX(np.zeros((1, num_cells))))
self.s0 = theano.shared(floatX(np.zeros((1, num_cells))))
#Initializing the weights
self.W_gx = random_weights((num_input, num_cells), name=self.name+"W_gx")
self.W_ix = random_weights((num_input, num_cells), name=self.name+"W_ix")
self.W_fx = random_weights((num_input, num_cells), name=self.name+"W_fx")
self.W_ox = random_weights((num_input, num_cells), name=self.name+"W_ox")
self.W_gh = random_weights((num_cells, num_cells), name=self.name+"W_gh")
self.W_ih = random_weights((num_cells, num_cells), name=self.name+"W_ih")
self.W_fh = random_weights((num_cells, num_cells), name=self.name+"W_fh")
self.W_oh = random_weights((num_cells, num_cells), name=self.name+"W_oh")
self.b_g = zeros(num_cells, name=self.name+"b_g")
self.b_i = zeros(num_cells, name=self.name+"b_i")
self.b_f = zeros(num_cells, name=self.name+"b_f")
self.b_o = zeros(num_cells, name=self.name+"b_o")
self.params = [self.W_gx, self.W_ix, self.W_ox, self.W_fx,
self.W_gh, self.W_ih, self.W_oh, self.W_fh,
self.b_g, self.b_i, self.b_f, self.b_o,]
self.output()
def __init__(self,
num_input,
num_cells,
input_layers=None,
name="",
go_backwards=False):
"""
GRU Layer
Takes as input sequence of inputs, returns sequence of outputs
"""
self.name = name
self.num_input = num_input
self.num_cells = num_cells
if len(input_layers) >= 2:
self.X = T.concatenate(
[input_layer.output() for input_layer in input_layers], axis=1)
else:
self.X = input_layers[0].output()
self.s0 = zeros(num_cells)
self.go_backwards = go_backwards
self.U_z = random_weights((num_input, num_cells),
name=self.name + "U_z")
self.W_z = random_weights((num_cells, num_cells),
name=self.name + "W_z")
self.U_r = random_weights((num_input, num_cells),
name=self.name + "U_r")
self.W_r = random_weights((num_cells, num_cells),
name=self.name + "W_r")
self.U_h = random_weights((num_input, num_cells),
name=self.name + "U_h")
self.W_h = random_weights((num_cells, num_cells),
name=self.name + "W_h")
self.b_z = zeros(num_cells, name=self.name + "b_z")
self.b_r = zeros(num_cells, name=self.name + "b_r")
self.b_h = zeros(num_cells, name=self.name + "b_h")
self.params = [
self.U_z, self.W_z, self.U_r, self.W_r, self.U_h, self.W_h,
self.b_z, self.b_r, self.b_h
]
self.output()
def reset_state(self):
self.W_yh = random_weights((self.num_input, self.num_output),
name="W_yh")
self.b_y = zeros(self.num_output, name="b_y")
self.params = [self.W_yh, self.b_y]
def reset_state(self):
self.W_yh = random_weights((self.num_input, self.num_output),name="W_yh")
self.b_y = zeros(self.num_output, name="b_y")
self.params = [self.W_yh, self.b_y]
def __init__(self,
num_input,
num_cells,
input_layers=None,
name="",
go_backwards=False):
"""
LSTM Layer
Takes as input sequence of inputs, returns sequence of outputs
"""
self.name = name
self.num_input = num_input
self.num_cells = num_cells
if len(input_layers) >= 2:
self.X = T.concatenate(
[input_layer.output() for input_layer in input_layers], axis=1)
else:
self.X = input_layers[0].output()
self.h0 = theano.shared(floatX(np.zeros(num_cells)))
self.s0 = theano.shared(floatX(np.zeros(num_cells)))
self.go_backwards = go_backwards
self.W_gx = random_weights((num_input, num_cells),
name=self.name + "W_gx")
self.W_ix = random_weights((num_input, num_cells),
name=self.name + "W_ix")
self.W_fx = random_weights((num_input, num_cells),
name=self.name + "W_fx")
self.W_ox = random_weights((num_input, num_cells),
name=self.name + "W_ox")
self.W_gh = random_weights((num_cells, num_cells),
name=self.name + "W_gh")
self.W_ih = random_weights((num_cells, num_cells),
name=self.name + "W_ih")
self.W_fh = random_weights((num_cells, num_cells),
name=self.name + "W_fh")
self.W_oh = random_weights((num_cells, num_cells),
name=self.name + "W_oh")
self.b_g = zeros(num_cells, name=self.name + "b_g")
self.b_i = zeros(num_cells, name=self.name + "b_i")
self.b_f = zeros(num_cells, name=self.name + "b_f")
self.b_o = zeros(num_cells, name=self.name + "b_o")
self.params = [
self.W_gx,
self.W_ix,
self.W_ox,
self.W_fx,
self.W_gh,
self.W_ih,
self.W_oh,
self.W_fh,
self.b_g,
self.b_i,
self.b_f,
self.b_o,
]
self.output()
def __init__(self,
input_layer,
num_input,
num_cells,
batch_size=8,
name="",
go_backwards=False,
return_sequences=False):
"""
LSTM Layer
"""
self.name = name
self.num_input = num_input
self.num_cells = num_cells
self.return_sequences = return_sequences
self.X = input_layer.output()
self.h0 = theano.shared(floatX(np.zeros(num_cells, )))
self.s0 = theano.shared(floatX(np.zeros(num_cells, )))
self.go_backwards = go_backwards
W_bound_sx = np.sqrt(6. / (num_input + num_cells))
rng = np.random.RandomState(23456)
self.W_gx = theano.shared(np.asarray(rng.uniform(low=-W_bound_sx,
high=W_bound_sx,
size=(num_input,
num_cells)),
dtype=theano.config.floatX),
name=name + " W_gx",
borrow=True)
self.W_ix = theano.shared(np.asarray(rng.uniform(low=-W_bound_sx,
high=W_bound_sx,
size=(num_input,
num_cells)),
dtype=theano.config.floatX),
name=name + " W_ix",
borrow=True)
self.W_fx = theano.shared(np.asarray(rng.uniform(low=-W_bound_sx,
high=W_bound_sx,
size=(num_input,
num_cells)),
dtype=theano.config.floatX),
name=name + " W_fx",
borrow=True)
self.W_ox = theano.shared(np.asarray(rng.uniform(low=-W_bound_sx,
high=W_bound_sx,
size=(num_input,
num_cells)),
dtype=theano.config.floatX),
name=name + " W_ox",
borrow=True)
W_bound_sh = np.sqrt(6. / (num_cells + num_cells))
self.W_gh = theano.shared(np.asarray(rng.uniform(low=-W_bound_sh,
high=W_bound_sh,
size=(num_cells,
num_cells)),
dtype=theano.config.floatX),
name=name + " W_gh",
borrow=True)
self.W_ih = theano.shared(np.asarray(rng.uniform(low=-W_bound_sh,
high=W_bound_sh,
size=(num_cells,
num_cells)),
dtype=theano.config.floatX),
name=name + " W_ih",
borrow=True)
self.W_fh = theano.shared(np.asarray(rng.uniform(low=-W_bound_sh,
high=W_bound_sh,
size=(num_cells,
num_cells)),
dtype=theano.config.floatX),
name=name + " W_fh",
borrow=True)
self.W_oh = theano.shared(np.asarray(rng.uniform(low=-W_bound_sh,
high=W_bound_sh,
size=(num_cells,
num_cells)),
dtype=theano.config.floatX),
name=name + " W_oh",
borrow=True)
self.b_g = random_weights((num_cells, ), name=self.name + " b_g")
self.b_i = random_weights((num_cells, ), name=self.name + " b_i")
self.b_f = random_weights((num_cells, ), name=self.name + " b_f")
self.b_o = random_weights((num_cells, ), name=self.name + " b_o")
self.params = [
self.W_gx,
self.W_ix,
self.W_ox,
self.W_fx,
self.W_gh,
self.W_ih,
self.W_oh,
self.W_fh,
]
self.bias = [self.b_g, self.b_i, self.b_f, self.b_o]