3_regularization.ipynb

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    "深度学习\n",
    "=============\n",
    "\n",
    "作业 3\n",
    "------------\n",
    "\n",
    "在之前的 `2_fullyconnected.ipynb`作业中, you trained a logistic regression and a neural network model.你训练了一个逻辑回归模型和一个神经网络模型。\n",
    "\n",
    "这次作业的目标是让你探索熟悉正则化技术。"
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   "source": [
    "#这些都是我们将来会使用到的模块，确保你在进行下一步之前可以导入它们。\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from six.moves import cPickle as pickle"
   ]
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   "source": [
    "首先加载我们之前在`1_notmnist.ipynb`作业中生成的数据。"
   ]
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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  # hint to help gc free up memory\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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    "重构数据的形状，让它更适合我们马上会用到的模型:\n",
    "- 将数据重构为平面二维矩阵，\n",
    "- 将标签 1-hot encoding 重构，并保存为浮点数类型."
   ]
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      "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",
    "  # Map 1 to [0.0, 1.0, 0.0 ...], 2 to [0.0, 0.0, 1.0 ...]\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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    "def accuracy(predictions, labels):\n",
    "  return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n",
    "          / predictions.shape[0])"
   ]
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   "source": [
    "---\n",
    "问题 1\n",
    "---------\n",
    "\n",
    "用L2归一化来优化你的逻辑回归模型和神经网络模型，并适当调整你的模型。 请记住L2归一化是把权重的标准值作为惩罚加入到损失函数里。 在 TensorFlow中，你可以使用`nn.l2_loss(t)`来计算一个张量(tensor)`t`的L2损失。 正确的归一化应该能够提升你的 验证/测试 准确性。\n",
    "\n",
    "---"
   ]
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   "source": [
    "---\n",
    "问题 2\n",
    "---------\n",
    "让我们演示一个过度拟合的极端情况。 限制你的训练数据阶段的训练次数，让它只训练少数几个批次(batches)。看看发生了什么？\n",
    "\n",
    "---"
   ]
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   "source": [
    "---\n",
    "问题 3\n",
    "---------\n",
    "\n",
    "将丢弃函数(Dropout)加入你的神经网络的隐藏层。请记住：丢弃函数只应该在训练阶段加入，而不应该在测试/验证阶段加入，否则你的测试/验证结果也将是随机的。TensorFlow 提供了`nn.dropout()`来实现丢弃，但是请你确保你只在训练阶段使用它。\n",
    "\n",
    "加入丢弃函数后我们的极端过度拟合的例子出现了什么变化？\n",
    "\n",
    "---"
   ]
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   "source": [
    "---\n",
    "问题 4\n",
    "---------\n",
    "\n",
    "请尽你最大的努力来让你的模型获得更好的表现，你可以使用多层模型！目前为止已知的使用深度网络的解法中测试准确率最高为 [97.1%](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html?showComment=1391023266211#c8758720086795711595).\n",
    "\n",
    "\n",
    "增加更多的神经网络层是一个你可以探索的方向。\n",
    "\n",
    "另一个方向是学习率衰减：\n",
    "\n",
    "    global_step = tf.Variable(0)  # count the number of steps taken.\n",
    "    learning_rate = tf.train.exponential_decay(0.5, global_step, ...)\n",
    "    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)\n",
    " \n",
    " ---\n"
   ]
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