tensorboard网络运行数据.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-1-9c121447261d>:5: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.one_hot on tensors.\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "Iter 0 Testing Accuracy: 0.8317\n",
      "Iter 1 Testing Accuracy: 0.8713\n",
      "Iter 2 Testing Accuracy: 0.8825\n",
      "Iter 3 Testing Accuracy: 0.8874\n",
      "Iter 4 Testing Accuracy: 0.8947\n",
      "Iter 5 Testing Accuracy: 0.8973\n",
      "Iter 6 Testing Accuracy: 0.9005\n",
      "Iter 7 Testing Accuracy: 0.9021\n",
      "Iter 8 Testing Accuracy: 0.9038\n",
      "Iter 9 Testing Accuracy: 0.9054\n",
      "Iter 10 Testing Accuracy: 0.906\n",
      "Iter 11 Testing Accuracy: 0.907\n",
      "Iter 12 Testing Accuracy: 0.9085\n",
      "Iter 13 Testing Accuracy: 0.9091\n",
      "Iter 14 Testing Accuracy: 0.9094\n",
      "Iter 15 Testing Accuracy: 0.9109\n",
      "Iter 16 Testing Accuracy: 0.9116\n",
      "Iter 17 Testing Accuracy: 0.9127\n",
      "Iter 18 Testing Accuracy: 0.9135\n",
      "Iter 19 Testing Accuracy: 0.9131\n",
      "Iter 20 Testing Accuracy: 0.9136\n",
      "Iter 21 Testing Accuracy: 0.9146\n",
      "Iter 22 Testing Accuracy: 0.9149\n",
      "Iter 23 Testing Accuracy: 0.9158\n",
      "Iter 24 Testing Accuracy: 0.9158\n",
      "Iter 25 Testing Accuracy: 0.916\n",
      "Iter 26 Testing Accuracy: 0.9171\n",
      "Iter 27 Testing Accuracy: 0.9175\n",
      "Iter 28 Testing Accuracy: 0.9179\n",
      "Iter 29 Testing Accuracy: 0.9178\n",
      "Iter 30 Testing Accuracy: 0.9177\n",
      "Iter 31 Testing Accuracy: 0.9176\n",
      "Iter 32 Testing Accuracy: 0.9171\n",
      "Iter 33 Testing Accuracy: 0.9183\n",
      "Iter 34 Testing Accuracy: 0.9182\n",
      "Iter 35 Testing Accuracy: 0.9186\n",
      "Iter 36 Testing Accuracy: 0.9187\n",
      "Iter 37 Testing Accuracy: 0.9196\n",
      "Iter 38 Testing Accuracy: 0.9199\n",
      "Iter 39 Testing Accuracy: 0.9193\n",
      "Iter 40 Testing Accuracy: 0.9193\n",
      "Iter 41 Testing Accuracy: 0.92\n",
      "Iter 42 Testing Accuracy: 0.9203\n",
      "Iter 43 Testing Accuracy: 0.9201\n",
      "Iter 44 Testing Accuracy: 0.9204\n",
      "Iter 45 Testing Accuracy: 0.921\n",
      "Iter 46 Testing Accuracy: 0.9211\n",
      "Iter 47 Testing Accuracy: 0.921\n",
      "Iter 48 Testing Accuracy: 0.9208\n",
      "Iter 49 Testing Accuracy: 0.9214\n",
      "Iter 50 Testing Accuracy: 0.9215\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "# 载入数据\n",
    "mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n",
    "\n",
    "# 批次的大小\n",
    "batch_size = 100\n",
    "n_batch = mnist.train.num_examples // batch_size\n",
    "\n",
    "# 参数概要\n",
    "def variable_summaries(var):\n",
    "    with tf.name_scope('summaries'):\n",
    "        mean = tf.reduce_mean(var)\n",
    "        tf.summary.scalar('mean', mean)  # 平均值\n",
    "        with tf.name_scope('stddev'):\n",
    "            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))\n",
    "        tf.summary.scalar('stddev', stddev) # 标准差\n",
    "        tf.summary.scalar('max', tf.reduce_max(var)) # 最大值\n",
    "        tf.summary.scalar('min', tf.reduce_min(var)) # 最小值\n",
    "        tf.summary.histogram('histogram', var) # 直方图\n",
    "\n",
    "# 可视化网络结构 \n",
    "with tf.name_scope('input'):\n",
    "    x = tf.placeholder(tf.float32, [None,784], name='x-input')\n",
    "    y = tf.placeholder(tf.float32, [None, 10], name='y-input')\n",
    "    \n",
    "with tf.name_scope('layer'):\n",
    "    # 创建一个简单的神经网络\n",
    "    with tf.name_scope('weight'):\n",
    "        W = tf.Variable(tf.zeros([784,10]), dtype=tf.float32, name='W')\n",
    "        variable_summaries(W)\n",
    "    with tf.name_scope('biases'):\n",
    "        b = tf.Variable(tf.zeros([1, 10]), dtype=tf.float32, name='b')\n",
    "        variable_summaries(b)\n",
    "    with tf.name_scope('wx_plus_b'):\n",
    "        wx_plus_b = tf.matmul(x, W) + b\n",
    "    with tf.name_scope('softmax'):\n",
    "        prediction = tf.nn.softmax(wx_plus_b)\n",
    "\n",
    "with tf.name_scope('loss'):\n",
    "    # 代价函数\n",
    "    loss = tf.reduce_mean(tf.square(y-prediction))\n",
    "    tf.summary.scalar('loss', loss)\n",
    "\n",
    "with tf.name_scope('train'):\n",
    "    # 梯度下降法\n",
    "    train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)\n",
    "\n",
    "# 初始化变量\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "with tf.name_scope('accuracy'):\n",
    "    with tf.name_scope('correct_prediction'):\n",
    "        # 得到一个布尔型列表，存放结果是否正确\n",
    "        correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(prediction,1)) #argmax 返回一维张量中最大值索引\n",
    "    with tf.name_scope('accuracy'):\n",
    "        # 求准确率\n",
    "        accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) # 把布尔值转换为浮点型求平均数\n",
    "        tf.summary.scalar('accuracy', accuracy)\n",
    "\n",
    "# 合并所有的sumary\n",
    "merged = tf.summary.merge_all()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    # 可视化网络结构 -- 2 存储图的结构\n",
    "    writer = tf.summary.FileWriter('logs/', sess.graph)\n",
    "    for epoch in range(51):\n",
    "        for batch in range(n_batch):\n",
    "            # 获得批次数据\n",
    "            batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "            summary, _ = sess.run([merged, train_step], feed_dict={x:batch_xs, y:batch_ys})\n",
    "        \n",
    "        # 将sumary写入sumary\n",
    "        writer.add_summary(summary, epoch)\n",
    "        acc = sess.run(accuracy, feed_dict={x:mnist.test.images,y:mnist.test.labels})\n",
    "        print(\"Iter \" + str(epoch) + \" Testing Accuracy: \" + str(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}