def __init__(self, input_size, output_size, activation="identity", name="Dense"):
self.input_size = input_size
self.output_size = output_size
self.name = name
self.activation = activation
self.activation_fct = factories.make_activation_function(self.activation)
# Regression output weights and biases
self.W = sharedX(value=np.zeros((self.input_size, self.output_size)), name=self.name+'_W')
self.b = sharedX(value=np.zeros(output_size), name=self.name+'_b')
def __init__(self, input_size, output_size, activation="identity", name="Dense"):
self.input_size = input_size
self.output_size = output_size
self.name = name
self.activation = activation
self.activation_fct = factories.make_activation_function(self.activation)
# Regression output weights and biases
self.W = sharedX(value=np.zeros((self.input_size, self.output_size)), name=self.name+'_W')
self.b = sharedX(value=np.zeros(output_size), name=self.name+'_b')
def __init__(self, input_size, output_size, activation="identity", name="DenseNormalized", eps=1e-5):
self.input_size = input_size
self.output_size = output_size
self.name = name
self.activation = activation
self.activation_fct = factories.make_activation_function(self.activation)
self.eps = eps
# Regression output weights, biases and gains
self.W = sharedX(value=np.zeros((self.input_size, self.output_size)), name=self.name+'_W')
self.b = sharedX(value=np.zeros(output_size), name=self.name+'_b')
self.g = sharedX(value=np.ones(output_size), name=self.name+'_g')
def __init__(self, input_size, hidden_size, activation="tanh", name="GRU"):
self.input_size = input_size
self.hidden_size = hidden_size
self.name = name
self.activation = activation
self.activation_fct = factories.make_activation_function(self.activation)
# Input weights (z:update, r:reset)
# Concatenation of the weights in that order: Wz, Wr, Wh
self.W = sharedX(value=np.zeros((input_size, 3*hidden_size)), name=self.name+'_W')
# self.Wh = sharedX(value=np.zeros((input_size, 2*hidden_size)), name=self.name+'_Wh')
# Biases (z:update, r:reset)
# Concatenation of the biases in that order: bz, br, bh
self.b = sharedX(value=np.zeros(3*hidden_size), name=self.name+'_b')
# self.bh = sharedX(value=np.zeros(hidden_size), name=self.name+'_bh')
# Recurrence weights (z:update, r:reset)
# Concatenation of the recurrence weights in that order: Uz, Ur
self.U = sharedX(value=np.zeros((hidden_size, 2*hidden_size)), name=self.name+'_U')
self.Uh = sharedX(value=np.zeros((hidden_size, hidden_size)), name=self.name+'_Uh')
def __init__(self, input_size, hidden_size, activation="tanh", name="GRU"):
self.input_size = input_size
self.hidden_size = hidden_size
self.name = name
self.activation = activation
self.activation_fct = factories.make_activation_function(self.activation)
# Input weights (z:update, r:reset)
# Concatenation of the weights in that order: Wz, Wr, Wh
self.W = sharedX(value=np.zeros((input_size, 3*hidden_size)), name=self.name+'_W')
# self.Wh = sharedX(value=np.zeros((input_size, 2*hidden_size)), name=self.name+'_Wh')
# Biases (z:update, r:reset)
# Concatenation of the biases in that order: bz, br, bh
self.b = sharedX(value=np.zeros(3*hidden_size), name=self.name+'_b')
# self.bh = sharedX(value=np.zeros(hidden_size), name=self.name+'_bh')
# Recurrence weights (z:update, r:reset)
# Concatenation of the recurrence weights in that order: Uz, Ur
self.U = sharedX(value=np.zeros((hidden_size, 2*hidden_size)), name=self.name+'_U')
self.Uh = sharedX(value=np.zeros((hidden_size, hidden_size)), name=self.name+'_Uh')
def __init__(self, input_size, hidden_size, activation="tanh", name="LSTM"):
self.input_size = input_size
self.hidden_size = hidden_size
self.name = name
self.activation = activation
self.activation_fct = factories.make_activation_function(self.activation)
# Input weights (i:input, o:output, f:forget, m:memory)
# Concatenation of the weights in that order: Wi, Wo, Wf, Wm
self.W = sharedX(value=np.zeros((input_size, 4*hidden_size)), name=self.name+'_W')
# Biases (i:input, o:output, f:forget, m:memory)
# Concatenation of the biases in that order: bi, bo, bf, bm
self.b = sharedX(value=np.zeros(4*hidden_size), name=self.name+'_b')
# Recurrence weights (i:input, o:output, f:forget, m:memory)
# Concatenation of the recurrence weights in that order: Ui, Uo, Uf, Um
self.U = sharedX(value=np.zeros((hidden_size, 4*hidden_size)), name=self.name+'_U')
# Peepholes (i:input, o:output, f:forget, m:memory)
self.Vi = sharedX(value=np.ones(hidden_size), name=self.name+'_Vi')
self.Vo = sharedX(value=np.ones(hidden_size), name=self.name+'_Vo')
self.Vf = sharedX(value=np.ones(hidden_size), name=self.name+'_Vf')
def __init__(self, input_size, hidden_size, activation="tanh", name="LSTM"):
self.input_size = input_size
self.hidden_size = hidden_size
self.name = name
self.activation = activation
self.activation_fct = factories.make_activation_function(self.activation)
# Input weights (i:input, o:output, f:forget, m:memory)
# Concatenation of the weights in that order: Wi, Wo, Wf, Wm
self.W = sharedX(value=np.zeros((input_size, 4*hidden_size)), name=self.name+'_W')
# Biases (i:input, o:output, f:forget, m:memory)
# Concatenation of the biases in that order: bi, bo, bf, bm
self.b = sharedX(value=np.zeros(4*hidden_size), name=self.name+'_b')
# Recurrence weights (i:input, o:output, f:forget, m:memory)
# Concatenation of the recurrence weights in that order: Ui, Uo, Uf, Um
self.U = sharedX(value=np.zeros((hidden_size, 4*hidden_size)), name=self.name+'_U')
# Peepholes (i:input, o:output, f:forget, m:memory)
self.Vi = sharedX(value=np.ones(hidden_size), name=self.name+'_Vi')
self.Vo = sharedX(value=np.ones(hidden_size), name=self.name+'_Vo')
self.Vf = sharedX(value=np.ones(hidden_size), name=self.name+'_Vf')
def __init__(self, input_size, hidden_size, activation="tanh", name="GRU", eps=1e-5):
self.input_size = input_size
self.hidden_size = hidden_size
self.name = name
self.activation = activation
self.activation_fct = factories.make_activation_function(self.activation)
self.eps = eps
# Input weights (z:update, r:reset)
# Concatenation of the weights in that order: Wz, Wr, Wh
self.W = sharedX(value=np.zeros((input_size, 3*hidden_size)), name=self.name+'_W')
self.b_x = sharedX(value=np.zeros(3 * hidden_size), name=self.name + '_b_x')
self.b_u = sharedX(value=np.zeros(2 * hidden_size), name=self.name + '_b_u')
self.b_uh = sharedX(value=np.zeros(hidden_size), name=self.name+'_b_uh')
self.g_x = sharedX(value=np.ones(3 * hidden_size), name=self.name + '_g_x')
self.g_u = sharedX(value=np.ones(2*hidden_size), name=self.name+'_g_u')
self.g_uh = sharedX(value=np.ones(hidden_size), name=self.name+'_g_uh')
# Recurrence weights (z:update, r:reset)
# Concatenation of the recurrence weights in that order: Uz, Ur
self.U = sharedX(value=np.zeros((hidden_size, 2*hidden_size)), name=self.name+'_U')
self.Uh = sharedX(value=np.zeros((hidden_size, hidden_size)), name=self.name+'_Uh')
