tensorflow初学之SGD

在开始本任务之前，确保已经完成了之前的notMNIST的步骤，点击查看notMNIST

提示：训练随机梯度下降（SGD）花费的时间应给明显少于简单的梯度下降（GD）.

1.检查包

首先，检查本次学习要用到的包，确保都已经正确导入，输入以下代码，点击“run cell”，运行不报错即可

#在学习之前确保以下包已经正确导入
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range

2.导入pickle

导入之前生成的notMNIST.pickle文件（链接在本文开头）

pickle_file = 'notMNIST.pickle'

with open(pickle_file, 'rb') as f:
  save = pickle.load(f)
  train_dataset = save['train_dataset']
  train_labels = save['train_labels']
  valid_dataset = save['valid_dataset']
  valid_labels = save['valid_labels']
  test_dataset = save['test_dataset']
  test_labels = save['test_labels']
  del save  # hint to help gc free up memory
  print('Training set', train_dataset.shape, train_labels.shape)
  print('Validation set', valid_dataset.shape, valid_labels.shape)
  print('Test set', test_dataset.shape, test_labels.shape)

结果如下：

Training set (200000, 28, 28) (200000,)
Validation set (10000, 28, 28) (10000,)
Test set (18724, 28, 28) (18724,)

3.数据处理

将数据重新处理为更适合接下来模型学习的格式：

数据作为平面矩阵；标签用One-Hot编码处理.

image_size = 28
num_labels = 10

def reformat(dataset, labels):
  dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)
  # Map 0 to [1.0, 0.0, 0.0 ...], 1 to [0.0, 1.0, 0.0 ...]
  labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)
  return dataset, labels
train_dataset, train_labels = reformat(train_dataset, train_labels)
valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)
test_dataset, test_labels = reformat(test_dataset, test_labels)
print('Training set', train_dataset.shape, train_labels.shape)
print('Validation set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)

结果：

Training set (200000, 784) (200000, 10)
Validation set (10000, 784) (10000, 10)
Test set (10000, 784) (10000, 10)

4.简单的梯度下降（GD）

我们首先使用简单的梯度下降训练多项Logistic回归。

tensorflow工作流程：

首先描述你想要在输入，变量和操作上执行的计算方式，它们通过计算图的方式创建为一个节点。此描述全部包含在以下内容中：

with graph.as_default():

然后可以通过session.run()，在计算图上运行多次你想要的操作，从返回的图中提取提供输出。此运行时操作全部包含在以下块中：

with tf.Session(graph=graph) as session:

我们将所有数据加载到TensorFlow中，构建对应于我们培训的计算图：

# With gradient descent training, even this much data is prohibitive.
# Subset the training data for faster turnaround.
train_subset = 10000

graph = tf.Graph()
with graph.as_default():

  # Input data.
  # Load the training, validation and test data into constants that are
  # attached to the graph.
  tf_train_dataset = tf.constant(train_dataset[:train_subset, :])
  tf_train_labels = tf