tensorflow几个函数讲解
tensorflow几个函数讲解
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多看tensorflow API
1、tf.clip_by_value(a,1e-10,1.0)
将a的值限制在1e-10—1.0之间
#coding:utf-8
import tensorflow as tf
a = tf.constant([[1.0,2.0]])
b = tf.clip_by_value(a,1e-10,1.0)
with tf.Session() as sess:
print sess.run(b.eval())
2、tf.truncated_normal（）
生成截断正态分布
3、a = tf.equal(x,y)
对x,y中每一个数（元素）进行比较，返回布尔值列表
4、tf.constant()
创建常量
5、tf.placeholder()
创建placeholder，用于被喂数据
x = tf.placeholder(tf.float32,shape=(1,2),name="input")
例：
import tensorflow as tf
w1 = tf.Variable(tf.random_normal([2,3],stddev=1))
w2 = tf.Variable(tf.random_normal([3,1],stddev=1))
x = tf.placeholder(tf.float32,shape=(1,2),name="input")
a = tf.matmul(x,w1)
y = tf.matmul(a,w2)
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
feed_dict = {x:[[0.7,0.9]]}
print sess.run(y,feed_dict)
6、tf.random_normal_initializer()
生成正态分布
7、tf.Session()
启动会话
import tensorflow as tf
w1 = tf.Variable(tf.random_normal([3,3],stddev=1,seed=1))
w2 = tf.Variable(w1.initialized_value())#使用W1初始化w2
x = tf.constant([[0.7,0.9,0.2]])
a = tf.matmul(x,w1)
y = tf.matmul(a,w2)
with tf.Session() as sess:
#sess.run(w1.initializer)
#sess.run(w2.initializer)
init_op = tf.global_variables_initializer()
sess.run(init_op)#初始化所有的变量
print w1.eval()#得到张量的值
print sess.run(y)
8、tf.transpose()
调换维度
x=[[1 2 3]
[4 5 6]]
tf.transpose(x, perm=[1, 0]) ==> [[1 4]
[2 5]
[3 6]]
9、tf.cast()
转换数据类型
# tensor `a` is [1.8, 2.2], dtype=tf.float
tf.cast(a, tf.int32) ==> [1, 2] # dtype=tf.int32
#将a变为tf.int32类型10、tf.argmax()
返回某一个轴上最大值的索引
A = [[1,2,3,4,5]]
B = [[1,2,3], [4,5,6]]
with tf.Session() as sess:
print(sess.run(tf.argmax(A, 1))) ==>[4]
print(sess.run(tf.argmax(B, 1))) ==>[2 2]11、tf.expand_dims()
在维度上增加[1]
# 't' is a tensor of shape [2]
shape(expand_dims(t, 0)) ==> [1, 2]
shape(expand_dims(t, 1)) ==> [2, 1]
shape(expand_dims(t, -1)) ==> [2, 1]
# 't2' is a tensor of shape [2, 3, 5]
shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]12、tf.concat(concat_dim, values, name=’concat’)
连接张量
t1 = [[1, 2, 3], [4, 5, 6]]
t2 = [[7, 8, 9], [10, 11, 12]]
tf.concat(0, [t1, t2]) ==> [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
tf.concat(1, [t1, t2]) ==> [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]]
# tensor t3 with shape [2, 3]
# tensor t4 with shape [2, 3]
tf.shape(tf.concat(0, [t3, t4])) ==> [4, 3]
tf.shape(tf.concat(1, [t3, t4])) ==> [2, 6]13、
vocab_processor=tensorflow.contrib.learn.preprocessing.VocabularyProcessor(max_document_length)
x = np.array(list(vocab_processor.fit_transform(x_text)))用于从语料中学习字典,并且返回索引,x就是索引,如下,两个9说明那里是同一个字
[[ 1 2 3 4 0 0 0]
[ 5 6 7 8 9 10 0]
[11 12 12 13 14 15 9]
[16 17 14 15 18 19 0]]
14、tf.nn.embedding_lookup（params,ids）
在params中查找与ids对应的表示
a = np.asarray([[0.1, 0.2, 0.3], [1.1, 1.2, 1.3], [2.1, 2.2, 2.3], [3.1, 3.2, 3.3], [4.1, 4.2, 4.3]])
idx1 = tf.Variable([0, 2, 3, 1], tf.int32)
idx2 = tf.Variable([[0, 2, 3, 1], [4, 0, 2, 2]], tf.int32)
out1 = tf.nn.embedding_lookup(a, idx1)
out2 = tf.nn.embedding_lookup(a, idx2)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
print "\n这是out1结果",sess.run(out1)
print out1
print "\n"
print "\n这是out2结果",sess.run(out2)
print out215、tf.shape( )
感觉print tf.shape(a)并不好用，不如直接 print a，里面会包含shape，还有type