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_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, 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
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_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_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, 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, 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(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, 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, num_input, num_output, input_layers, 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="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_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_output, input_layers, 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="W_yh")
self.b_y = zeros(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 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.s0 = zeros(self.num_cells)
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 reset_state(self):
self.s0 = zeros(self.num_cells)
