IT-Course/手写数字识别.ipynb

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    {
     "data": {
      "text/plain": [
       "'1.13.1'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.examples.tutorials.mnist.input_data as input_data\n",
    "\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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"
     ]
    }
   ],
   "source": [
    "MNIST=input_data.read_data_sets(\"MNIST_data\", one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9149\n"
     ]
    }
   ],
   "source": [
    "# 定义一个占位符x\n",
    "x = tf.placeholder(tf.float32, [None, 784])  # 张量的形状是[None, 784]，None表第一个维度任意\n",
    "\n",
    "# 定义变量W,b,是可以被修改的张量，用来存放机器学习模型参数\n",
    "W = tf.Variable(tf.zeros([784, 10]))\n",
    "b = tf.Variable(tf.zeros([10]))\n",
    "\n",
    "# 实现模型, y是预测分布\n",
    "y = tf.nn.softmax(tf.matmul(x, W) + b)\n",
    "\n",
    "# 训练模型，y_是实际分布\n",
    "y_ = tf.placeholder(\"float\", [None, 10])\n",
    "cross_entropy = -tf.reduce_sum(y_ * tf.log(y))  # 交叉嫡，cost function\n",
    "\n",
    "# 使用梯度下降来降低cost，学习速率为0.01\n",
    "train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)\n",
    "\n",
    "# 初始化已经创建的变量\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "# 在一个Session中启动模型，并初始化变量\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "\n",
    "# # 训练模型，运行1000次，每次随机抽取100个\n",
    "for i in range(1, 1000):\n",
    "    batch_xs, batch_ys = MNIST.train.next_batch(100)\n",
    "    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n",
    "\n",
    "# 验证正确率\n",
    "correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))\n",
    "print(sess.run(accuracy, feed_dict={x: MNIST.test.images, y_: MNIST.test.labels}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前迭代次数为0，训练正确率为0.8999999761581421\n",
      "当前迭代次数为200，训练正确率为0.9200000166893005\n",
      "当前迭代次数为400，训练正确率为0.9100000262260437\n",
      "当前迭代次数为600，训练正确率为0.9399999976158142\n",
      "当前迭代次数为800，训练正确率为0.8999999761581421\n",
      "当前迭代次数为1000，训练正确率为0.8799999952316284\n",
      "当前迭代次数为1200，训练正确率为0.9200000166893005\n",
      "当前迭代次数为1400，训练正确率为0.9100000262260437\n",
      "当前迭代次数为1600，训练正确率为0.9200000166893005\n",
      "当前迭代次数为1800，训练正确率为0.8399999737739563\n",
      "当前迭代次数为2000，训练正确率为0.8899999856948853\n",
      "当前迭代次数为2200，训练正确率为0.8700000047683716\n",
      "当前迭代次数为2400，训练正确率为0.8999999761581421\n",
      "当前迭代次数为2600，训练正确率为0.8899999856948853\n",
      "当前迭代次数为2800，训练正确率为0.8999999761581421\n",
      "当前迭代次数为3000，训练正确率为0.9100000262260437\n",
      "当前迭代次数为3200，训练正确率为0.949999988079071\n",
      "当前迭代次数为3400，训练正确率为0.9300000071525574\n",
      "当前迭代次数为3600，训练正确率为0.8999999761581421\n",
      "当前迭代次数为3800，训练正确率为0.9100000262260437\n",
      "当前迭代次数为4000，训练正确率为0.9700000286102295\n",
      "当前迭代次数为4200，训练正确率为0.8899999856948853\n",
      "当前迭代次数为4400，训练正确率为0.8999999761581421\n",
      "当前迭代次数为4600，训练正确率为0.9300000071525574\n",
      "当前迭代次数为4800，训练正确率为0.9399999976158142\n",
      "当前迭代次数为5000，训练正确率为0.8899999856948853\n",
      "当前迭代次数为5200，训练正确率为0.9100000262260437\n",
      "当前迭代次数为5400，训练正确率为0.9100000262260437\n",
      "当前迭代次数为5600，训练正确率为0.9100000262260437\n",
      "当前迭代次数为5800，训练正确率为0.9300000071525574\n",
      "当前迭代次数为6000，训练正确率为0.9200000166893005\n",
      "当前迭代次数为6200，训练正确率为0.8700000047683716\n",
      "当前迭代次数为6400，训练正确率为0.9200000166893005\n",
      "当前迭代次数为6600，训练正确率为0.9399999976158142\n",
      "当前迭代次数为6800，训练正确率为0.8999999761581421\n",
      "当前迭代次数为7000，训练正确率为0.9300000071525574\n",
      "当前迭代次数为7200，训练正确率为0.9300000071525574\n",
      "当前迭代次数为7400，训练正确率为0.9300000071525574\n",
      "当前迭代次数为7600，训练正确率为0.8799999952316284\n",
      "当前迭代次数为7800，训练正确率为0.9100000262260437\n",
      "当前迭代次数为8000，训练正确率为0.8999999761581421\n",
      "当前迭代次数为8200，训练正确率为0.9599999785423279\n",
      "当前迭代次数为8400，训练正确率为0.9300000071525574\n",
      "当前迭代次数为8600，训练正确率为0.8799999952316284\n",
      "当前迭代次数为8800，训练正确率为0.9200000166893005\n",
      "当前迭代次数为9000，训练正确率为0.8600000143051147\n",
      "当前迭代次数为9200，训练正确率为0.9700000286102295\n",
      "当前迭代次数为9400，训练正确率为0.9200000166893005\n",
      "当前迭代次数为9600，训练正确率为0.8899999856948853\n",
      "当前迭代次数为9800，训练正确率为0.8799999952316284\n"
     ]
    }
   ],
   "source": [
    " \n",
    "for i in range(10000):\n",
    "    batch = MNIST.train.next_batch(100)\n",
    "    if i % 200 == 0:\n",
    "        print(\"当前迭代次数为{}，训练正确率为{}\".format(i, accuracy.eval(feed_dict={x:batch[0], y_: batch[1]},session=sess)))\n",
    "        train_step.run(feed_dict={x: batch[0], y_: batch[1]},session=sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 测试正确率为 0.912\n"
     ]
    }
   ],
   "source": [
    "#测试阶段\n",
    "print(\" 测试正确率为 %g\"%accuracy.eval(feed_dict={x: MNIST.test.images, y_: MNIST.test.labels},session=sess)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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