5-2tensorboard的网络结构.ipynb

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      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Iter 0 Testing Accuracy: 0.7948\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 = 128\n",
    "n_batch = mnist.train.num_examples // batch_size\n",
    "\n",
    "# 可视化网络结构 -- 1 命名空间 名字为input\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]))\n",
    "    with tf.name_scope('biases'):\n",
    "        b = tf.Variable(tf.zeros([1, 10]))\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",
    "\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",
    "\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(1):\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",
    "            sess.run(train_step, feed_dict={x:batch_xs, y:batch_ys})\n",
    "        acc = sess.run(accuracy, feed_dict={x:mnist.test.images,y:mnist.test.labels})\n",
    "        print(\"Iter \" + str(epoch) + \" Testing Accuracy: \" + str(acc))"
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