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segmentation fault (core dumped)
TensorFlow中出现段错误，import numpy，然后 import tensorflow as tf，什么原因？！
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tf.argmax()
argmax是一个非常有用的函数，它能给出某个tensor对象在某一维上的其数据最大值所在的索引值。由于标签向量是由0,1组成，因此最大值1所在的索引位置就是类别标签，比如tf.argmax(y,1)返回的是模型对于任一输入x预测到的标签值，而 tf.argmax(y_,1)代表正确的标签，我们可以用tf.equal来检测我们的预测是否真实标签匹配(索引位置一样表示匹配)。
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global_step = tf.Variable(0,name='global_step',trainable=False)
初始化为0，增加一次迭代，它就变化一次，可以用来改变学习率或者调解其他参数
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数据处理心得
想尽办法变成一个流来处理
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tf.one_hot()
import tensorflow as tf
indices = [0, 2, -1, 1]#索引
depth = 5#one_hot的维度
on_value = 5#索引位置的值，默认为1,数据类型需和off_value相同
off_value = 1#非索引位置的值，默认为0
axis = -1
x = tf.one_hot(indices,depth,on_value,off_value)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print(sess.run(x))
#####output
[[5 1 1 1 1]
[1 1 5 1 1]
[1 1 1 1 1]
[1 5 1 1 1]]
tf.pad(tensor, paddings, mode='CONSTANT', name=None)
# 't' is [[1, 2, 3], [4, 5, 6]].
# 'paddings' is [[1, 1,], [2, 2]].
# rank of 't' is 2.
pad(t, paddings, "CONSTANT") ==> [[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 2, 3, 0, 0],
[0, 0, 4, 5, 6, 0, 0],
[0, 0, 0, 0, 0, 0, 0]]
pad(t, paddings, "REFLECT") ==> [[6, 5, 4, 5, 6, 5, 4],
[3, 2, 1, 2, 3, 2, 1],
[6, 5, 4, 5, 6, 5, 4],
[3, 2, 1, 2, 3, 2, 1]]
pad(t, paddings, "SYMMETRIC") ==> [[2, 1, 1, 2, 3, 3, 2],
[2, 1, 1, 2, 3, 3, 2],
[5, 4, 4, 5, 6, 6, 5],
[5, 4, 4, 5, 6, 6, 5]]
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Tensorboard
tensorboard --logdir=/tmp --port=8008
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tf.Tensor.get_shape()
return a tuple of input tensor shape
import tensorflow as tf
T=tf.constant([[[11,12,13,14]],[[15,16,17,18]]])
#start a interactive session
sess=tf.InteractiveSession()
print("Our tensor is",T.eval())
print("Shape of the tensor is ",T.get_shape())
print("T[0] is ",T[0].eval()) #equivalent to T[0,:,:]
print("T[1,0] is",T[1,0].eval()) #equivalent to T[1,0,:]
print("T[1,0,0] is ",T[1,0,0].eval())
print("T[1,0,:] is",T[1,0,:].eval())
output:
Our tensor is [[[11 12 13 14]]
[[15 16 17 18]]]
Shape of the tensor is (2, 1, 4)
T[0] is [[11 12 13 14]]
T[1,0] is [15 16 17 18]
T[1,0,0] is 15
T[0,0,:] is [11 12 13 14]
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tf.reshape(tensor,shape,name=None)
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tf.slice(input,begin,size,name=None)
