4-2Dropout.ipynb

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   "cell_type": "code",
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   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "#载入数据集\n",
    "mnist = input_data.read_data_sets(\"C:\\Users\\51272\\Documents\\MNIST_data\",one_hot=True)\n",
    "#每个批次大小\n",
    "batch_size = 100\n",
    "#计算一共有多少个批次\n",
    "n_batch = mnist.train.num_examples\n",
    "#定义两个placeholder\n",
    "x = tf.placeholder(tf.float32,[None,784])\n",
    "y = tf.placeholder(tf.float32,[None,10])\n",
    "#创建一个简单的神经网络\n",
    "W1 = tf.Variable(tf.truncated_normal([784,2000],stddev=0.1))\n",
    "b1 = tf.Variable(tf.zeros([2000])+0.1)\n",
    "L1 = tf.nn.tanh(tf.matmul(x,W1)+b1)\n",
    "L1_drop = tf.nn.dropout(L1,keep_prob)\n",
    "\n",
    "W2 = tf.Variable(tf.truncated_normal([2000,2000],stddev=0.1))\n",
    "b2 = tf.Variable(tf.zeros([2000])+0.1)\n",
    "L2 = tf.nn.tanh(tf.matmul(L1_drop,W2)+b2)\n",
    "L2_drop = tf.nn.dropout(L12,keep_prob)\n",
    "                        \n",
    "W3 = tf.Variable(tf.truncated_normal([2000,1000],stddev=0.1))\n",
    "b3 = tf.Variable(tf.zeros([1000])+0.1)\n",
    "L3 = tf.nn.tanh(tf.matmul(L2_drop,W3)+b3)\n",
    "L3_drop = tf.nn.dropout(L3,keep_prob)\n",
    "\n",
    "W4 = tf.Variable(tf.truncated_normal([1000,10],stddev=0.1))\n",
    "b4 = tf.Variable(tf.zeros([10])+0.1)\n",
    "prediction = tf.nn.softmax(tf.matmul(L3_drop,W4)+b4)\n",
    "# W = tf.Variable(tf.zeros([784,10]))\n",
    "# b = tf.Variable(tf.zeros([10]))\n",
    "# prediction = tf.nn.softmax(tf.matmul(x,W)+b)\n",
    "\n",
    "#二次代价函数\n",
    "# loss = tf.reduce_mean(tf.square(y-prediction))\n",
    "#交叉熵代价函数\n",
    "loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))\n",
    "#梯度下降法\n",
    "train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)\n",
    "#初始化变量\n",
    "init = tf.global_variables_initializer()\n",
    "#结果存放在一个布尔型列表\n",
    "correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大值所在位置\n",
    "#求准确率\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    for epoch in range(21):\n",
    "        for batch in range(n_batch):\n",
    "            batch_xs,batch_ys = mnist.train.next_batch(batch_size)\n",
    "            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:0.7})\n",
    "        print(1)\n",
    "        test_acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})\n",
    "        train_acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})\n",
    "        print(\"Iter \" + str(epoch) + \",Testing Accuracy \" + str(test_acc) +\",Training Accuracy \" + str(train_acc))"
   ]
  }
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