下载数据集
>>> from tensorflow.examples.tutorials.mnist import input_data
>>> mnist = input_data.read_data_sets('./data/' ,one_hot=True)
显示
显示数据集样本数
>>> print "train data size:" ,mnist.train.num_examples
train data size: 55000
>>> print "validation data size:" ,mnist.validation.num_examples
validation data size: 5000
>>> print "test data size:" ,mnist.test.num_examples
test data size: 10000
显示标签
>>> mnist.train.labels[0]
array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.])
显示像素值
mnist.train.images[0]
数据传入神经网络
>>> BATCH_SZIE = 200
>>> xs , ys = mnist.train.next_batch(BATCH_SZIE)
>>> print "xs shape:" , xs.shape
xs shape: (200, 784)
>>> print "ys shape:" , ys.shape
ys shape: (200, 10)
手写数字识别常用函数
tf.get_collection(" ")
从集合中取出全部变量来生成一个列表
tf.add()
>>> x = tf.constant([[2,1],[2,3]])
>>> y = tf.constant([[0,-1],[4,10]])
>>> z = tf.add(x,y)
>>> with tf.Session() as sess:
... print sess.run(z)
...
[[ 2 0]
[ 6 13]]
tf.cast()
>>> A = tf.constant([[1,2,3],[2,3,4]])
>>> print A.dtype
<dtype: 'int32'>
>>> b = tf.cast(A, tf.float32)
>>> print b.dtype
<dtype: 'float32'>
tf.equal()
>>> A = [[1,3,5,7,9]]
>>> B = [[1,5,5,3,9]]
>>> with tf.Session() as sess:
... print sess.run(tf.equal(A,B))
...
[[ True False True False True]]
tf.reduce_mean(x,axis)
>>> x = [[1,1],[2,2]]
>>> x = tf.cast(x,tf.float32)
>>> with tf.Session() as sess:
... print sess.run(tf.reduce_mean(x))
... print sess.run(tf.reduce_mean(x,0))
... print sess.run(tf.reduce_mean(x,1))
...
1.5
[1.5 1.5]
[1. 2.]
os.path.join()
把字符串按路径命名规则拼接
字符串.split()
'./model/mnist_model-1001'.split('/')[-1].split('-')[-1]
两次拆分，第一次根据/拆分，取-1项也就是最后一项，然后再根据-取最后一项，结果为1001
