Tensorflow實現CIFAR-10分類問題-詳解四cifar10_eval.py
阿新 • • 發佈:2019-01-29
最後我們採用cifar10_eval.py檔案來評估以下訓練模型在保留(hold-out samples)樣本下的表現力,其中保留樣本的容量為10000。為了驗證模型在訓練過程中的表現能力的變化情況,我們驗證了最近一些訓練過程中產生的checkpoint files。
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import math
import time
import numpy as np
import tensorflow as tf
import cifar10
parser = cifar10.parser
parser.add_argument('--eval_dir', type=str, default='/home/liu/NewDisk/LearnTensor/cifar10_eval',
help='Directory where to write event logs.')
parser.add_argument('--eval_data', type=str, default='test',
help='Either `test` or `train_eval`.' )
parser.add_argument('--checkpoint_dir', type=str, default='/home/liu/NewDisk/LearnTensor/cifar10_train',
help='Directory where to read model checkpoints.')
parser.add_argument('--eval_interval_secs', type=int, default=60*5,
help='How often to run the eval.')#設定每隔多長時間做一側評估
parser.add_argument('--num_examples', type=int, default=10000,
help='Number of examples to run.')
parser.add_argument('--run_once', type=bool, default=False,
help='Whether to run eval only once.')
def eval_once(saver, summary_writer, top_k_op, summary_op):
"""Run Eval once.
Args:
saver: Saver.
summary_writer: Summary writer.
top_k_op: Top K op.
summary_op: Summary op.
"""
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
# Assuming model_checkpoint_path looks something like:
# /my-favorite-path/cifar10_train/model.ckpt-0,
# extract global_step from it.
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
else:
print('No checkpoint file found')
return
# Start the queue runners.# 啟動很多執行緒,並把coordinator傳遞給每一個執行緒
coord = tf.train.Coordinator()
try:
threads = []#使用coord統一管理所有執行緒
for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
threads.extend(qr.create_threads(sess, coord=coord, daemon=True,
start=True))
num_iter = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size))
true_count = 0 # Counts the number of correct predictions.
total_sample_count = num_iter * FLAGS.batch_size
step = 0
while step < num_iter and not coord.should_stop():
predictions = sess.run([top_k_op])#計算num_iter個評估用例是否預測正確,應該到不了num_iter就會滿足coord.should_shop()然後退出
true_count += np.sum(predictions)#累加
step += 1
# Compute precision @ 1.
precision = true_count / total_sample_count
print('%s: precision @ 1 = %.3f' % (datetime.now(), precision))
summary = tf.Summary()
summary.ParseFromString(sess.run(summary_op))
summary.value.add(tag='Precision @ 1', simple_value=precision)
summary_writer.add_summary(summary, global_step)
except Exception as e: # pylint: disable=broad-except
coord.request_stop(e)
coord.request_stop()
coord.join(threads, stop_grace_period_secs=10)
def evaluate():
"""Eval CIFAR-10 for a number of steps."""
with tf.Graph().as_default() as g:
# Get images and labels for CIFAR-10.
eval_data = FLAGS.eval_data == 'test'
images, labels = cifar10.inputs(eval_data=eval_data)# 讀入評估圖片和標籤
# Build a Graph that computes the logits predictions from the
# inference model.
logits = cifar10.inference(images)
# Calculate predictions.
top_k_op = tf.nn.in_top_k(logits, labels, 1) #判定predictions的top k個預測結果是否包含targets,返回bool變數
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
cifar10.MOVING_AVERAGE_DECAY)#建立計算均值的物件
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.summary.merge_all()
# 建立一個event file,用於之後寫summary物件到FLAGS.eval_dir目錄下的檔案中
summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g)
# 每隔一定時間進行評估,只對當前訓練好的最新的模型進行評估。
while True:
eval_once(saver, summary_writer, top_k_op, summary_op)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def main(argv=None): # pylint: disable=unused-argument
cifar10.maybe_download_and_extract()
if tf.gfile.Exists(FLAGS.eval_dir):
tf.gfile.DeleteRecursively(FLAGS.eval_dir)
tf.gfile.MakeDirs(FLAGS.eval_dir)
evaluate()
if __name__ == '__main__':
FLAGS = parser.parse_args()
tf.app.run()
使用tensorboard實現變數的視覺化,具體操作:
開啟終端,輸入
source activate tensorflow
tensorboard –logdir /your_path/cifar10_train/
開啟相應網址即可…