trainning.py

# -*- coding: utf-8 -*-
"""
Created on Sun May 19 19:28:18 2019

@author: 雷浩洁
训练模型
"""

import os
import numpy as np
import tensorflow as tf
import model
import get_batch_data as gbd
N_CLASSES=3
IMG_H=19
IMG_W=128
CAPACITY = 1000
MAX_STEP=4000

learning_rate=0.01

def run_trainning():
    """
    对神经网络进行训练
    """
    PATH = "Cleaned_5sRNA_test/"
    logs_train_dir="Net_model/"  # 保存训练得来的模型的文件夹
    row = 19
    column = 128
    vec_len = 8
    temp_batch_size = 64
    inputs, Labels= gbd.get_Data(PATH=PATH,
                                  row=row,
                                  column=column)

    train_X, train_Y, one_hot_train_Y = gbd.get_batch_data(inputs,Labels, batch_size=temp_batch_size)

    train_logits,train_v_length=model.interface(inputs=train_X,
                                                Y=one_hot_train_Y,
                                                batch_size=temp_batch_size,
                                                vec_len=vec_len,
                                                temp_batch_size=temp_batch_size)
    train_loss=model.loss(logits=train_logits,
                          v_length=train_v_length,
                          labels=train_Y,
                          Y=one_hot_train_Y,
                          temp_batch_size=temp_batch_size)
    train_op = model.trainning(train_loss,learning_rate)
    train_acc = model.evalution(train_logits,train_Y)
    
    sess = tf.Session()
    sess.run(tf.global_variables_initializer())
    sess.run(tf.local_variables_initializer())

    saver = tf.train.Saver()

    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess,coord = coord)

    try:
        for step in np.arange(MAX_STEP):
            if coord.should_stop():
                break
            _, tra_loss, tra_acc=sess.run([train_op, train_loss, train_acc])

            if step % 100 == 0:
                print("Step %d,train loss = %.2f,train accuracy = %.2f" %(step, tra_loss, tra_acc))
                print(train_X.shape)
            if step % 200 == 0:
                # 每两百轮保存一次训练数据
                checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt')
                saver.save(sess,
                           save_path=checkpoint_path,
                           global_step=step)

    except tf.errors.OutOfRangeError:
        print('Done Trainning')
    finally:
        coord.request_stop()
        
    coord.join(threads)
    sess.close()
    
if __name__ == "__main__":
    run_trainning()