"""
** deeplean-ai.com **
** dl-lab **
created by :: GauravBh1010tt
"""
import numpy as np
from keras import backend as K
from keras.engine.topology import Layer
from keras import initializers, regularizers, constraints , activations
np.random.seed(21)
class ntn(Layer):
def __init__(self,inp_size, out_size, activation='tanh', **kwargs):
super(ntn, self).__init__(**kwargs)
self.k = out_size
self.d = inp_size
self.activation = activations.get(activation)
self.test_out = 0
def build(self,input_shape):
self.W = self.add_weight(name='w',shape=(self.k, self.d, self.d),
initializer='glorot_uniform',trainable=True)
#initializer='ones',trainable=False)
self.V = self.add_weight(name='v',shape=(self.k, self.d*2),
initializer='glorot_uniform',trainable=True)
#initializer='ones',trainable=False)
#self.b = self.add_weight(name='b',shape=(self.k,1),
# initializer='glorot_uniform',trainable=True)
# initializer='ones',trainable=False)
self.U = self.add_weight(name='u',shape=(self.k,1),
# initializer='ones',trainable=False)
initializer='glorot_uniform',trainable=True)
super(ntn, self).build(input_shape)
def call(self , x, mask=None):
e1=x[0].T
e2=x[1].T
batch_size = K.shape(x[0])[0]
sim = []
V_out = K.dot(self.V, K.concatenate([e1,e2],axis=0))
for i in range(self.k):
temp = K.batch_dot(K.dot(e1.T,self.W[i,:,:]),e2.T,axes=1)
sim.append(temp)
sim=K.reshape(sim,(self.k,batch_size))
tensor_bi_product = self.activation(V_out+sim)
tensor_bi_product = K.dot(self.U.T, tensor_bi_product).T
return tensor_bi_product
def compute_output_shape(self, input_shape):
return (input_shape[0][0],1)
class Similarity(Layer):
def __init__(self, v_dim, kernel_regularizer=None, **kwargs):
self.v_dim = v_dim
self.kernel_regularizer = regularizers.get(kernel_regularizer)
super(Similarity, self).__init__(**kwargs)
def build(self,input_shape):
self.W = self.add_weight(name='w',shape=(self.v_dim, self.v_dim),
initializer='glorot_uniform',
regularizer=self.kernel_regularizer,
trainable=True)
super(Similarity, self).build(input_shape)
def call(self, data, mask=None):
v1 = data[0]
v2 = data[1]
sim = K.dot(v1,self.W)
sim = K.batch_dot(sim,v2,axes=1)
return sim
def compute_output_shape(self, input_shape):
return (input_shape[0][0],1)
class Abs(Layer):
def __init__(self, **kwargs):
super(Abs, self).__init__(**kwargs)
def call(self, x, mask=None):
inp1, inp2 = x[0],x[1]
return K.abs(inp1-inp2)
def get_output_shape_for(self, input_shape):
return input_shape
class Exp(Layer):
def __init__(self, **kwargs):
super(Exp, self).__init__(**kwargs)
def call(self, x, mask=None):
h1 = x[0]
h2 = x[1]
dif = K.sum(K.abs(h1-h2),axis=1)
h = K.exp(-dif)
#print h.shape
h=K.clip(h,1e-7,1.0-1e-7)
h = K.reshape(h, (h.shape[0],1))
return h
def compute_output_shape(self, input_shape):
#print 'input shape:',input_shape
out_shape = list(input_shape[0])
out_shape[-1] = 1
#print 'output shape:',out_shape
return tuple(out_shape)
def mse(y_true, y_pred):
#print y_true.shape.eval(),y_pred.shape.eval()
y_true=K.clip(y_true,1e-7,1.0-1e-7)
return K.mean(K.square(y_pred - y_true), axis=-1)
