utils.py

import tensorflow as tf
import numpy as np
from tensorflow.python.framework import ops
# from tflearn.initializations import trunAAAcated_normal
# from tflearn.activations import relu

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1,dtype=tf.float32)
    return tf.Variable(initial, dtype=tf.float32) # 设置默认的变量w

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape,dtype=tf.float32)
    return tf.Variable(initial, dtype=tf.float32)##设置默认的变量b

def a_layer(x,units):
    W = weight_variable([x.get_shape().as_list()[1],units])
    b = bias_variable([units])
    tf.add_to_collection('l2_reg', tf.contrib.layers.l2_regularizer(1.0)(W))
    return tf.nn.relu(tf.matmul(x, W) + b)
# 从特征中选择适合的特征，然后通过relu输出，最终和相似性进行相乘，相加上从drug_disease中得到的特征，还有本身的w

def bi_layer(x0,x1,sym,dim_pred):
    if sym == False:
        W0p = weight_variable([x0.get_shape().as_list()[1],dim_pred])
        W1p = weight_variable([x1.get_shape().as_list()[1],dim_pred])
        tf.add_to_collection('l2_reg', tf.contrib.layers.l2_regularizer(1.0)(W0p))
        tf.add_to_collection('l2_reg', tf.contrib.layers.l2_regularizer(1.0)(W1p))
        return tf.matmul(tf.matmul(x0, W0p), tf.matmul(x1, W1p),transpose_b=True)
    else:
        W0p = weight_variable([x0.get_shape().as_list()[1],dim_pred])
        tf.add_to_collection('l2_reg', tf.contrib.layers.l2_regularizer(1.0)(W0p))
        return tf.matmul(tf.matmul(x0, W0p), tf.matmul(x1, W0p),transpose_b=True)