一直在等待，一直会等待 TensorFlow常见API--1

最新推荐文章于 2025-02-18 03:18:57 发布

AnthongDai

最新推荐文章于 2025-02-18 03:18:57 发布

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分类专栏： TensorFlow API 笔记

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tf.placeholder(dtype, shape=None,name=None)

张量的占位符号，执行的时候一定要通过数据进行赋值，否则会出错。
注意：
Important: This tensor will produce an error if evaluated. Its value must be fed using the feed_dict optional argument to Session.run(), Tensor.eval(), or Operation.run()
dtype: 元素类型
shape: 张量的形状，默认情况下为任意形状
name: 该操作名称
返回值：将用于数据填充的句柄

x = tf.placeholder(tf.float32, shape=(1024, 1024))
y = tf.matmul(x, x)

with tf.Session() as sess:
  print(sess.run(y))  # ERROR: will fail because x was not fed.

  rand_array = np.random.rand(1024, 1024)
  print(sess.run(y, feed_dict={x: rand_array}))  # Will succeed.

tf.reshape(tensor,shape,name=None)

返回一个给定张量的按照给定的形状的新张量
tensor: 给定张量
shape: 给定张量的形状
name: 该操作名称

# tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]
# tensor 't' has shape [9]
reshape(t, [3, 3]) ==> [[1, 2, 3],
                        [4, 5, 6],
                        [7, 8, 9]]

# tensor 't' is [[[1, 1], [2, 2]],
#                [[3, 3], [4, 4]]]
# tensor 't' has shape [2, 2, 2]
reshape(t, [2, 4]) ==> [[1, 1, 2, 2],
                        [3, 3, 4, 4]]

# tensor 't' is [[[1, 1, 1],
#                 [2, 2, 2]],
#                [[3, 3, 3],
#                 [4, 4, 4]],
#                [[5, 5, 5],
#                 [6, 6, 6]]]
# tensor 't' has shape [3, 2, 3]
# pass '[-1]' to flatten 't'
reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]

# -1 can also be used to infer the shape

# -1 is inferred to be 9:
reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]
# -1 is inferred to be 2:
reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]
# -1 is inferred to be 3:
reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],
                              [2, 2, 2],
                              [3, 3, 3]],
                             [[4, 4, 4],
                              [5, 5, 5],
                              [6, 6, 6]]]

# tensor 't' is [7]
# shape `[]` reshapes to a scalar
reshape(t, []) ==> 7

tf.argmax(input,axis=None,name=None,dimension=None,output_type=tf.int64

)
返回给定坐标轴方向下最大值的索引
input: 给定张量
axis: 给定坐标轴，0表示按列，1表示按行
name: 该操作名称
dimension：和axis功能一样，默认axis取值优先
output_type：输出数据的类型，tf.int32, tf.int64

import tensorflow as tf

a=tf.get_variable(name='a',
                  shape=[3,4],
                  dtype=tf.float32,
                  initializer=tf.random_uniform_initializer(minval=-1,maxval=1))
b=tf.argmax(input=a,axis=0)
c=tf.argmax(input=a,dimension=1)   #此处用dimesion或用axis是一样的
sess = tf.InteractiveSession()
sess.run(tf.initialize_all_variables())
print(sess.run(a))
#[[ 0.04261756 -0.34297419 -0.87816691 -0.15430689]
# [ 0.18663144  0.86972666 -0.06103253  0.38307118]
# [ 0.84588599 -0.45432305 -0.39736366  0.38526249]]
print(sess.run(b))
#[2 1 1 2]
print(sess.run(c))
#[0 1 0]

代码用例来自：https://blog.youkuaiyun.com/liyaoqing/article/details/54020202

tf.get_variable(name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None)

Gets an existing variable with these parameters or create a new one
给定参数获取一个存在的变量或者创建一个新的变量
name: 该操作名称
shape: 给定张量的形状
dtype：张量的数据类型
initializer: 张量的初始化器。要么为初始化对象，要么为张量

def foo():
  with tf.variable_scope("foo", reuse=tf.AUTO_REUSE):
    v = tf.get_variable("v", [1])
  return v

v1 = foo()  # Creates v.
v2 = foo()  # Gets the same, existing v.
assert v1 == v2