2_fullyconnected.ipynb

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    "深度学习\n",
    "=============\n",
    "\n",
    "任务 2\n",
    "------------\n",
    "\n",
    "之前在 `1_notmnist.ipynb` 中, 我们使用格式化的数据集创建了一个 pickle 用来训练、开发和测试 [notMNIST 数据集](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html)。\n",
    "\n",
    "这个任务的目的是利用 Tensorflow 进一步训练更深、更精确的模型。"
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   "source": [
    "# 这些是我们后面会使用的模块。确保在进一步操作之前你可以\n",
    "# 将所有模块导入。\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from six.moves import cPickle as pickle\n",
    "from six.moves import range"
   ]
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   "source": [
    "首先，重新加载由 `1_notmnist.ipynb` 所生成的数据。"
   ]
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     "text": [
      "Training set (200000, 28, 28) (200000,)\n",
      "Validation set (10000, 28, 28) (10000,)\n",
      "Test set (18724, 28, 28) (18724,)\n"
     ]
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   "source": [
    "pickle_file = 'notMNIST.pickle'\n",
    "\n",
    "with open(pickle_file, 'rb') as f:\n",
    "  save = pickle.load(f)\n",
    "  train_dataset = save['train_dataset']\n",
    "  train_labels = save['train_labels']\n",
    "  valid_dataset = save['valid_dataset']\n",
    "  valid_labels = save['valid_labels']\n",
    "  test_dataset = save['test_dataset']\n",
    "  test_labels = save['test_labels']\n",
    "  del save  # 提示帮助 gc 释放内存\n",
    "  print('Training set', train_dataset.shape, train_labels.shape)\n",
    "  print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
    "  print('Test set', test_dataset.shape, test_labels.shape)"
   ]
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    "重新格式化形成一个 shape 以更适用于我们所要训练的模型：\n",
    "- 数据格式化为一个 flat 矩阵，\n",
    "- 标签格式化为浮点 1-hot 编码。"
   ]
  },
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     "text": [
      "Training set (200000, 784) (200000, 10)\n",
      "Validation set (10000, 784) (10000, 10)\n",
      "Test set (18724, 784) (18724, 10)\n"
     ]
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   "source": [
    "image_size = 28\n",
    "num_labels = 10\n",
    "\n",
    "def reformat(dataset, labels):\n",
    "  dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)\n",
    "  # 将 0 映射到 [1.0, 0.0, 0.0 ...], 1 映射到 [0.0, 1.0, 0.0 ...]\n",
    "  labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n",
    "  return dataset, labels\n",
    "train_dataset, train_labels = reformat(train_dataset, train_labels)\n",
    "valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\n",
    "test_dataset, test_labels = reformat(test_dataset, test_labels)\n",
    "print('Training set', train_dataset.shape, train_labels.shape)\n",
    "print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
    "print('Test set', test_dataset.shape, test_labels.shape)"
   ]
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    "我们首先使用简单的梯度下降训练一个多项逻辑回归。\n",
    "\n",
    "TensorFlow 会这么做：\n",
    "* 首先描述下你是希望如何执行计算的：输入、变量和操作会是什么样。这由一个计算图的节点所创建完成。以上所述都包含在以下的块之中：\n",
    "\n",
    "      with graph.as_default():\n",
    "          ...\n",
    "\n",
    "* 然后，你可以通过调用 `session.run()` 根据自己的意愿执行多次图操作，为图返回的结果提供输出。这个运行时间操作均包含在以下块之中：\n",
    "\n",
    "      with tf.Session(graph=graph) as session:\n",
    "          ...\n",
    "\n",
    "把所有的数据加载到 Tensorflow 并根据我们的训练要求构建计算图："
   ]
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    "# 使用梯度下降训练，即便这样大小的数据也会成本太高。\n",
    "# 为了加速周转时间，将训练数据分为子集。\n",
    "train_subset = 10000\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "\n",
    "  # 输入数据。\n",
    "  # 加载训练、验证和测试数据为\n",
    "  # 附属于图的常量。\n",
    "  tf_train_dataset = tf.constant(train_dataset[:train_subset, :])\n",
    "  tf_train_labels = tf.constant(train_labels[:train_subset])\n",
    "  tf_valid_dataset = tf.constant(valid_dataset)\n",
    "  tf_test_dataset = tf.constant(test_dataset)\n",
    "  \n",
    "  # 变量。\n",
    "  # 这是我们需要训练的参数。 \n",
    "  # 使用（截断）正态分布为权重矩阵进行随机值赋值。\n",
    "  # 偏差随机赋值为零。\n",
    "  weights = tf.Variable(\n",
    "    tf.truncated_normal([image_size * image_size, num_labels]))\n",
    "  biases = tf.Variable(tf.zeros([num_labels]))\n",
    "  \n",
    "  # 训练计算。\n",
    "  # 我们将输入与权重矩阵相乘，然后加上偏差。\n",
    "  # 我们使用 softmax 和交叉熵进行计算（这是 Tensorflow 里的操作，因为\n",
    "  # 这十分常见，并能够进行优化）。\n",
    "  # 我们取所有训练样本的本次交叉熵计算的平均值：这是我们的损失。\n",
    "  logits = tf.matmul(tf_train_dataset, weights) + biases\n",
    "  loss = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))\n",
    "  \n",
    "  # 优化。\n",
    "  # 我们将使用梯度下降寻找最小的损失值。\n",
    "  optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
    "  \n",
    "  # 训练、验证和测试数据的预测。\n",
    "  # 这不是训练的一部分，但至此我们才能得到\n",
    "  # 我们训练的精确数据。\n",
    "  train_prediction = tf.nn.softmax(logits)\n",
    "  valid_prediction = tf.nn.softmax(\n",
    "    tf.matmul(tf_valid_dataset, weights) + biases)\n",
    "  test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)"
   ]
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   "source": [
    "让我们执行计算和迭代："
   ]
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     "text": [
      "Initialized\n",
      "Loss at step 0 : 17.2939\n",
      "Training accuracy: 10.8%\n",
      "Validation accuracy: 13.8%\n",
      "Loss at step 100 : 2.26903\n",
      "Training accuracy: 72.3%\n",
      "Validation accuracy: 71.6%\n",
      "Loss at step 200 : 1.84895\n",
      "Training accuracy: 74.9%\n",
      "Validation accuracy: 73.9%\n",
      "Loss at step 300 : 1.60701\n",
      "Training accuracy: 76.0%\n",
      "Validation accuracy: 74.5%\n",
      "Loss at step 400 : 1.43912\n",
      "Training accuracy: 76.8%\n",
      "Validation accuracy: 74.8%\n",
      "Loss at step 500 : 1.31349\n",
      "Training accuracy: 77.5%\n",
      "Validation accuracy: 75.0%\n",
      "Loss at step 600 : 1.21501\n",
      "Training accuracy: 78.1%\n",
      "Validation accuracy: 75.4%\n",
      "Loss at step 700 : 1.13515\n",
      "Training accuracy: 78.6%\n",
      "Validation accuracy: 75.4%\n",
      "Loss at step 800 : 1.0687\n",
      "Training accuracy: 79.2%\n",
      "Validation accuracy: 75.6%\n",
      "Test accuracy: 82.9%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 801\n",
    "\n",
    "def accuracy(predictions, labels):\n",
    "  return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n",
    "          / predictions.shape[0])\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  # 这是一个一次性的操作，确保参数已按照我们图所描述的进行初始化操作\n",
    "  # 权重矩阵随机赋值，\n",
    "  # 偏差为零。\n",
    "  tf.global_variables_initializer().run()\n",
    "  print('Initialized')\n",
    "  for step in range(num_steps):\n",
    "    # 执行计算。我们使用 .run() 需要执行优化，\n",
    "    # 然后获取 numpy 数组形式的损失值和\n",
    "    # 训练预测值。\n",
    "    _, l, predictions = session.run([optimizer, loss, train_prediction])\n",
    "    if (step % 100 == 0):\n",
    "      print('Loss at step %d: %f' % (step, l))\n",
    "      print('Training accuracy: %.1f%%' % accuracy(\n",
    "        predictions, train_labels[:train_subset, :]))\n",
    "      # 对 valid_prediction 调用 .eval() 基本上如同调用 run()，\n",
    "      # 但只是得到一个 numpy 数组。注意它会重新计算所有\n",
    "      # 与其存在依赖关系的图。\n",
    "      print('Validation accuracy: %.1f%%' % accuracy(\n",
    "        valid_prediction.eval(), valid_labels))\n",
    "  print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))"
   ]
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   "source": [
    "现在，让我们转向随机梯度下降训练，它更加快速。\n",
    "\n",
    "图基本是相似的，除了所有训练数据都在一个常量节点上，我们创建一个 `Placeholder` 节点，在每次调用 `session.run()` 时传输实际数据。"
   ]
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    "batch_size = 128\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "\n",
    "  # 输入数据。对于训练数据，我们使用一个 placeholder ，\n",
    "  # 在运行时，传输一个训练的 minibatch 。\n",
    "  tf_train_dataset = tf.placeholder(tf.float32,\n",
    "                                    shape=(batch_size, image_size * image_size))\n",
    "  tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
    "  tf_valid_dataset = tf.constant(valid_dataset)\n",
    "  tf_test_dataset = tf.constant(test_dataset)\n",
    "  \n",
    "  # 变量。\n",
    "  weights = tf.Variable(\n",
    "    tf.truncated_normal([image_size * image_size, num_labels]))\n",
    "  biases = tf.Variable(tf.zeros([num_labels]))\n",
    "  \n",
    "  # 训练计算。\n",
    "  logits = tf.matmul(tf_train_dataset, weights) + biases\n",
    "  loss = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))\n",
    "  \n",
    "  # 优化。\n",
    "  optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
    "  \n",
    "  # 训练、验证和测试数据的预测。\n",
    "  train_prediction = tf.nn.softmax(logits)\n",
    "  valid_prediction = tf.nn.softmax(\n",
    "    tf.matmul(tf_valid_dataset, weights) + biases)\n",
    "  test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)"
   ]
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   "source": [
    "让我们运行起来："
   ]
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   "outputs": [
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     "text": [
      "Initialized\n",
      "Minibatch loss at step 0 : 16.8091\n",
      "Minibatch accuracy: 12.5%\n",
      "Validation accuracy: 14.0%\n",
      "Minibatch loss at step 500 : 1.75256\n",
      "Minibatch accuracy: 77.3%\n",
      "Validation accuracy: 75.0%\n",
      "Minibatch loss at step 1000 : 1.32283\n",
      "Minibatch accuracy: 77.3%\n",
      "Validation accuracy: 76.6%\n",
      "Minibatch loss at step 1500 : 0.944533\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 76.5%\n",
      "Minibatch loss at step 2000 : 1.03795\n",
      "Minibatch accuracy: 78.9%\n",
      "Validation accuracy: 77.8%\n",
      "Minibatch loss at step 2500 : 1.10219\n",
      "Minibatch accuracy: 80.5%\n",
      "Validation accuracy: 78.0%\n",
      "Minibatch loss at step 3000 : 0.758874\n",
      "Minibatch accuracy: 82.8%\n",
      "Validation accuracy: 78.8%\n",
      "Test accuracy: 86.1%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 3001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  tf.global_variables_initializer().run()\n",
    "  print(\"Initialized\")\n",
    "  for step in range(num_steps):\n",
    "    # 在已经过随机化处理的训练数据里选取一个 offset。\n",
    "    # 注意：每次迭代时我们可以使用更优的随机化处理数据。\n",
    "    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
    "    # 生成一个 minibatch。\n",
    "    batch_data = train_dataset[offset:(offset + batch_size), :]\n",
    "    batch_labels = train_labels[offset:(offset + batch_size), :]\n",
    "    # 准备一个字典指定 session 从哪里获取 minibatch。\n",
    "    # 字典的键指定图的 placeholder 节点，\n",
    "    # 值是由 numpy 数组所 feed 的数据。\n",
    "    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
    "    _, l, predictions = session.run(\n",
    "      [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
    "    if (step % 500 == 0):\n",
    "      print(\"Minibatch loss at step %d: %f\" % (step, l))\n",
    "      print(\"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels))\n",
    "      print(\"Validation accuracy: %.1f%%\" % accuracy(\n",
    "        valid_prediction.eval(), valid_labels))\n",
    "  print(\"Test accuracy: %.1f%%\" % accuracy(test_prediction.eval(), test_labels))"
   ]
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   "source": [
    "---\n",
    "问题\n",
    "-------\n",
    "\n",
    "使用 SGD 将逻辑回归示例变为带有一层激活函数隐层和 1024 个节点的神经网络 [nn.relu()](https://www.tensorflow.org/versions/r0.7/api_docs/python/nn.html#relu)。这个模型可以改进你的验证集/测试集准确度。\n",
    "\n",
    "---"
   ]
  }
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