tensorflow/tf_basic_cnn.ipynb

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   "source": [
    "http://studyai.site/2017/02/26/%E3%80%90Tensorflow%20r1.0%20%E6%96%87%E6%A1%A3%E7%BF%BB%E8%AF%91%E3%80%91%E6%B7%B1%E5%85%A5MNIST--%E4%B8%93%E5%AE%B6%E7%BA%A7/"
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  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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   },
   "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": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "import tensorflow as tf\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "source": [
    "sess = tf.InteractiveSession() # 不同session之间数据计算独立"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   "source": [
    "# 权重\n",
    "def weight_variable(shape):\n",
    "    initial = tf.truncated_normal(shape, stddev=0.1)# 初始权重，加入标准差为0.1的随机噪声\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "# 偏置\n",
    "def bias_variable(shape):\n",
    "    initial = tf.constant(0.1, shape=shape)\n",
    "    return tf.Variable(initial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
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   "source": [
    "# 卷积层\n",
    "def conv2d(x, W):\n",
    "    return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME') # strides:卷积核参数；[0:1]卷积核大小；[2]通道；[3]卷积核个数\n",
    "\n",
    "# MP层\n",
    "def max_pool_2x2(x):\n",
    "    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME') # [batch, height, width, channels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "y_ = tf.placeholder(tf.float32, [None, 10])\n",
    "x_image = tf.reshape(x, [-1,28,28,1]) # 28x28 images with channel 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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   "outputs": [],
   "source": [
    "# layer-1 conv\n",
    "W_conv1 = weight_variable([5,5,1,32]) # 5x5 kernel 32 counts\n",
    "b_conv1 = bias_variable([32])\n",
    "h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1)+b_conv1)\n",
    "h_pool1 = max_pool_2x2(h_conv1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'MaxPool:0' shape=(?, 14, 14, 32) dtype=float32>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_pool1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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   "outputs": [],
   "source": [
    "# layer-2 conv\n",
    "W_conv2 = weight_variable([5,5,32,64]) # 5x5 kernel 64 counts (32, since h_pool1 shape is 14x14x32)\n",
    "b_conv2 = bias_variable([64])\n",
    "h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2)+b_conv2)\n",
    "h_pool2 = max_pool_2x2(h_conv2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'MaxPool_2:0' shape=(?, 7, 7, 64) dtype=float32>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_pool2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
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   "source": [
    "# 将tensor变形为一维向量\n",
    "W_fc1 = weight_variable([7 * 7 * 64, 1024]) # 全连接层，1024神经元\n",
    "b_fc1 = bias_variable([1024])\n",
    "h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])\n",
    "h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "deletable": true,
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   "outputs": [],
   "source": [
    "# Dropout\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "deletable": true,
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   "outputs": [],
   "source": [
    "# Softmax\n",
    "W_fc2 = weight_variable([1024, 10])\n",
    "b_fc2 = bias_variable([10])\n",
    "y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true,
    "deletable": true,
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   "outputs": [],
   "source": [
    "# loss function\n",
    "cross_entropy = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n",
    "correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))# define acc function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 0, training accuracy 0.1\n",
      "step 1, training accuracy 0.1\n",
      "step 2, training accuracy 0.1\n",
      "step 3, training accuracy 0.1\n",
      "step 4, training accuracy 0.1\n",
      "step 5, training accuracy 0.1\n",
      "step 6, training accuracy 0.1\n",
      "step 7, training accuracy 0.1\n",
      "step 8, training accuracy 0.1\n",
      "step 9, training accuracy 0.1\n",
      "test accuracy 0.2455\n"
     ]
    }
   ],
   "source": [
    "# train\n",
    "sess.run(tf.global_variables_initializer())\n",
    "for i in range(10):\n",
    "    batch = mnist.train.next_batch(50)\n",
    "    if i%100 == 0:\n",
    "        train_accuracy = accuracy.eval(feed_dict={\n",
    "            x:batch[0], y_: batch[1], keep_prob: 1.0})\n",
    "    print(\"step %d, training accuracy %g\"%(i, train_accuracy))\n",
    "    train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})\n",
    "    \n",
    "# accuracy    \n",
    "print(\"test accuracy %g\"%accuracy.eval(feed_dict={\n",
    "    x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))"
   ]
  }
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