3da641f3841d0cbb55543e691d4a2be1ca4102b7,deepctr/layers.py,CrossNet,call,#CrossNet#Any#,232

Before Change

        if K.ndim(inputs) !=2 :
            raise ValueError("Unexpected inputs dimensions %d, expect to be 2 dimensions"% (K.ndim(inputs)))

        x = inputs
        dim = x.get_shape()[-1]
        x_0 = K.reshape(x,[-1,dim, 1])
        x_l = x_0
        for i in range(self.layer_num):
            dot_ = tf.matmul(x_0, tf.transpose(x_l, [0, 2, 1]))  // K.dot(x_0,K.transpose(x_l))
            dot_ = K.dot(dot_, self.kernels[i])
            //x_l = K.bias_add(dot_+ x_l,self.bias[i]) // K.bias_add(dot_, self.bias)
            x_l = dot_ + x_l + self.bias[i]//K.reshape(self.bias[i],[1,dim,1])
        x_l = K.reshape(x_l, [-1, dim])
        return x_l

    def get_config(self,):

After Change

            xl_w = tf.tensordot(tf.transpose(x_l,[0,2,1]),self.kernels[i],axes=(-1,0))
            dot_ = tf.matmul(x_0,xl_w)
            x_l = dot_ + x_l + self.bias[i]
        x_l = tf.squeeze(x_l,axis=2)
        return x_l

    def get_config(self,):

Instances