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MLP_separate_1.py
executable file
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MLP_separate_1.py
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#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
@author: mashutian
@time: 2019-03-10 13:25
@desc:
"""
from __future__ import print_function
# import sys
# sys.path.append("..") #if you want to import python module from other folders,
# you need to append the system path
import tensorflow as tf
from numpy.random import RandomState
from tensorflow.contrib.layers.python.layers import batch_norm as batch_norm # for batch normalization
import numpy as np
class Config(object):
def __init__(self, args):
self.LAYER1_DIM = args.layer1_dim
self.LAYER2_DIM = args.layer2_dim
self.LAYER3_DIM = args.layer3_dim
self.LAYER4_DIM = args.layer4_dim
self.LEARNING_RATE = args.learning_rate
self.EPOCH = args.epoch
self.BATCH_SIZE = args.batch_size
class CitationRecNet(object):
def __init__(self, layer1_dim, layer2_dim, layer3_dim, layer4_dim, x_dim1, x_dim2,
y_dim, learning_rate, data_num):
# in order to generate same random sequences
tf.set_random_seed(1)
"""
input parameter
"""
# 否则传不到MLP() 里面，要不然MLP() function 得写成MLP(layer1_dim) 的形式
self.layer1_dim = layer1_dim
self.layer2_dim = layer2_dim
self.layer3_dim = layer3_dim
self.layer4_dim = layer4_dim
self.x_dim1 = x_dim1
self.x_dim2 = x_dim2
self.y_dim = y_dim
self.learning_rate = learning_rate
self.data_num = data_num
"""
input data
"""
# training data: record and label
self.dropout_keep = tf.placeholder(dtype=tf.float32, name='dropout_keep')
self.xa = tf.placeholder(tf.float32, shape=(None, self.x_dim1), name='xa-input')
self.xb = tf.placeholder(tf.float32, shape=(None, self.x_dim2), name='xb-input')
self.y = tf.placeholder(tf.float32, shape=(None, self.y_dim), name='y-input')
"""
batch norm
"""
# if self.is_batch_norm:
# update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# with tf.control_dependencies(update_ops):
# self.loss = -tf.reduce_mean(self.y * tf.log(tf.clip_by_value(self.y_pred, 1e-10, 1.0)))
# self.loss = self.loss + tf.add_n(tf.get_collection('loss')) #L2 regularization
# else:
# self.loss = -tf.reduce_mean(self.y * tf.log(tf.clip_by_value(self.y_pred, 1e-10, 1.0)))
# self.loss = self.loss + tf.add_n(tf.get_collection('loss')) #L2 regularization
"""
graph structure
"""
# predict data: label
self.y_pred = self.MLP()
self.y_pred_softmax = tf.nn.softmax(self.y_pred)
# print(self.y_pred_softmax)
# acc
self.acc = tf.equal(tf.argmax(self.y_pred_softmax, 1), tf.argmax(self.y, 1))
self.acc = tf.reduce_mean(tf.cast(self.acc, tf.float32))
"""
model training
"""
self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.y_pred, labels=self.y))
self.loss_metric = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.y_pred, labels=self.y))
# self.loss = -tf.reduce_mean(self.y * tf.log(tf.clip_by_value(self.y_pred_softmax, 1e-10, 1.0)))
# self.loss = tf.losses.mean_squared_error(self.y, self.y_pred_softmax)
# self.loss = tf.reduce_mean(tf.square(self.y - self.y_pred_softmax))
# loss_less = 10
# loss_more = 0.1
# self.loss = tf.reduce_sum(tf.where(tf.greater(self.y_pred_softmax, self.y),
# (self.y_pred_softmax-self.y) * loss_more, (self.y-self.y_pred_softmax) * loss_less))
# optimizer
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate, name='optimizer')
# self.optimizer = tf.train.RMSPropOptimizer(learning_rate=self.learning_rate,
# decay=0.9, momentum=0.0, epsilon=1e-5, name='optimizer')
# self.optimizer = tf.train.AdadeltaOptimizer(learning_rate=self.learning_rate, name='optimizer')
# self.optimizer = tf.train.AdagradOptimizer(learning_rate=self.learning_rate, name='optimizer')
# self.optimizer = tf.train.FtrlOptimizer(learning_rate=self.learning_rate, name='optimizer')
# self.optimizer = tf.train.GradientDescentOptimizer(learning_rate=self.learning_rate, name='optimizer')
# self.optimizer = tf.train.MomentumOptimizer(learning_rate=self.learning_rate, momentum=0.5, name='optimizer')
# self.optimizer = tf.train.ProximalAdagradOptimizer(learning_rate=self.learning_rate, name='optimizer')
# self.optimizer = tf.train.ProximalGradientDescentOptimizer(learning_rate=self.learning_rate, name='optimizer')
self.train_op = self.optimizer.minimize(self.loss, name='train_op')
def MLP(self):
# network parameter
# 问题：这里需要加self吗？比如下面这行
# [x_dim, self.layer1_dim]这两个
with tf.variable_scope("layer1"):
self.W11 = tf.get_variable("w11", initializer=tf.random_normal([self.x_dim1, self.layer1_dim], stddev=0.1),
dtype=tf.float32)
self.W12 = tf.get_variable("w12", initializer=tf.random_normal([self.x_dim2, self.layer1_dim], stddev=0.1),
dtype=tf.float32)
self.b11 = tf.get_variable("b11", initializer=tf.zeros([self.layer1_dim]), dtype=tf.float32)
self.b12 = tf.get_variable("b12", initializer=tf.zeros([self.layer1_dim]), dtype=tf.float32)
with tf.variable_scope("layer2"):
self.W21 = tf.get_variable("w21",
initializer=tf.random_normal([self.layer1_dim, self.layer2_dim], stddev=0.1),
dtype=tf.float32)
self.W22 = tf.get_variable("w22",
initializer=tf.random_normal([self.layer1_dim, self.layer2_dim], stddev=0.1),
dtype=tf.float32)
self.b21 = tf.get_variable("b21", initializer=tf.zeros([self.layer2_dim]), dtype=tf.float32)
self.b22 = tf.get_variable("b22", initializer=tf.zeros([self.layer2_dim]), dtype=tf.float32)
with tf.variable_scope("layer3"):
self.W3 = tf.get_variable("w3",
initializer=tf.random_normal([self.layer2_dim*2, self.layer3_dim], stddev=0.1),
dtype=tf.float32)
self.b3 = tf.get_variable("b3", initializer=tf.zeros([self.layer3_dim]), dtype=tf.float32)
with tf.variable_scope("layer4"):
self.W4 = tf.get_variable("w4",
initializer=tf.random_normal([self.layer3_dim, self.layer4_dim], stddev=0.1),
dtype=tf.float32)
self.b4 = tf.get_variable("b4", initializer=tf.zeros([self.layer4_dim]), dtype=tf.float32)
with tf.variable_scope("output"):
self.W5 = tf.get_variable("w_output",
initializer=tf.random_normal([self.layer3_dim, self.y_dim], stddev=0.1),
dtype=tf.float32)
# hidden1 = tf.nn.relu(tf.matmul(self.xa, self.W11) + tf.matmul(self.xb, self.W12) + self.b1)
hidden11 = tf.nn.sigmoid(tf.matmul(self.xa, self.W11) + self.b11)
hidden12 = tf.nn.sigmoid(tf.matmul(self.xb, self.W12) + self.b12)
hidden11_drop = tf.nn.dropout(hidden11, self.dropout_keep)
hidden12_drop = tf.nn.dropout(hidden12, self.dropout_keep)
hidden21 = tf.nn.selu(tf.matmul(hidden11_drop, self.W21) + self.b21)
hidden22 = tf.nn.selu(tf.matmul(hidden12_drop, self.W22) + self.b22)
hidden21_drop = tf.nn.dropout(hidden21, self.dropout_keep)
hidden22_drop = tf.nn.dropout(hidden22, self.dropout_keep)
# hidden3 = tf.nn.sigmoid(tf.matmul(hidden21_drop+hidden22_drop, self.W3) + self.b3)
hidden3 = tf.nn.sigmoid(tf.matmul(tf.concat([hidden21_drop, hidden22_drop], axis=1), self.W3) + self.b3)
hidden3_drop = tf.nn.dropout(hidden3, self.dropout_keep)
# hidden4 = tf.nn.relu(tf.matmul(hidden3_drop, self.W4) + self.b4)
# hidden4_drop = tf.nn.dropout(hidden4, self.dropout_keep)
y_pred = tf.matmul(hidden3_drop, self.W5) # + self.b4
return y_pred
def CNN(self):
pass