tf.variable

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import tensorflow as tf# Create a variable.w = tf.Variable(, name=)# Use...v = tf.Variable([1, 2])init = tf.compat.v1.global_variables_initializer()with tf.compat.v1.Session()...import tensorflow as tfx = tf.Variable(5)y = tf.Variable(10)z = tf.Variable(10)# The followings will...x = tf.Variable(5)print(x.experimental_ref().deref())==> tf.Variable 'Variable:0' shape=() dtype=int32...v = tf.Variable([1, 2])init = tf.compat.v1.global_variables_initializer()with tf.compat.v1.Session()

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tf.Variable()函数

tf.Variable(initializer,name),参数initializer是初始化参数，name是可自定义的变量名称，用法如下： import tensorflow as tf v1...=tf.Variable(tf.random_normal(shape=[4,3],mean=0,stddev=1),name='v1') v2=tf.Variable(tf.constant(2),name...(tf.zeros([3, 3, 3]), name="v1") v2 = tf.Variable(tf.ones([10, 5]), name="v2") # 填充单值矩阵 v3 = tf.Variable...name="weights") biases = tf.Variable(tf.zeros([200]), name="biases") ... # Add an op to initialize the...当然也可以这样写：encoding:UTF-8 import tensorflow as tf这句话是导入tensorflow 模块 state = tf.Variable(0 , name='counter

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Tensorflow教程： tf.Variable() 和tf.get_variable()

一、简介 tf.Variable() 1tf.Variable(initial_value=None, trainable=True, collections=None, validate_shape...使用tf.get_variable()时，系统不会处理冲突，而会报错 1import tensorflow as tf 2w_1 = tf.Variable(3,name="w_1") 3w_2 = tf.Variable...(1.0, name="w2") 9 10 print(w1 is w1_p, w2 is w2_p) 11 #输出 12 #True False 由于tf.Variable() 每次都在创建新对象...(tf.random_normal([100, 50]), name='h1_weights') 12b1 = tf.Variable(tf.zeros([50]), name='h1_biases')...([50, 10]), name='h2_weights') 16b2 = tf.Variable(tf.zeros([10]), name='2_biases') 17logits = tf.matmul

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tensorflow之tf.placeholder 与 tf.Variable区别对比

案例： tf.Variable weights = tf.Variable( tf.truncated_normal([IMAGE_PIXELS, hidden1_units],.../math.sqrt(float(IMAGE_PIXELS)), name='weights')) biases = tf.Variable(tf.zeros ([hidden1_units]), name

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tensorflow之tf.placeholder 与 tf.Variable区别对比

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Tensorflow教程(十三) tf.Variable() 和tf.get_variable()1 简介2 区别3 实例

1 简介 tf.Variable() tf.Variable(initial_value=None, trainable=True, collections=None, validate_shape=True...使用tf.get_variable()时，系统不会处理冲突，而会报错 import tensorflow as tf w_1 = tf.Variable(3,name="w_1") w_2 = tf.Variable...(1.0, name="w2") print(w1 is w1_p, w2 is w2_p) #输出 #True False 由于tf.Variable() 每次都在创建新对象，所有reuse=True...(tf.random_normal([100, 50]), name='h1_weights') b1 = tf.Variable(tf.zeros([50]), name='h1_biases...([50, 10]), name='h2_weights') b2 = tf.Variable(tf.zeros([10]), name='2_biases') logits = tf.matmul

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tf.variable和tf.get_Variable以及tf.name_scope和tf.variable_scope的区别

==因此，tensorflow中用tf.Variable(),tf.get_variable(),tf.Variable_scope(),tf.name_scope()几个函数来实现：== ---- 一...、tf.Variable(),tf.get_variable()的作用与区别： tf.Variable()和tf.get_variable()都是用于在一个name_scope下面获取或创建一个变量的两种方式...，区别在于： tf.Variable()会自动检测命名冲突并自行处理，但tf.get_variable()则遇到重名的变量创建且变量名没有设置为共享变量时，则会报错。...tf.Variable()用于创建一个新变量，在同一个name_scope下面，可以创建相同名字的变量，底层实现会自动引入别名机制，两次调用产生了其实是两个不同的变量。...(name='var2', initial_value=[2], dtype=tf.float32) var4 = tf.Variable(name='var2', initial_value=

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tensor张量

0阶张量 # 0阶张量import tensorflow as tfmammal = tf.Variable("maoli", tf.string)ignition = tf.Variable(20,...tf.int16)floating = tf.Variable(3.14159265359, tf.float64)its_complicated = tf.Variable(12.3 - 4.85j,...=string, numpy=b'maoli'>, tf.Variable 'Variable:0' shape=() dtype=int32, numpy=20>, tf.Variable 'Variable...=(12.3-4.85j)>] 1阶张量 # 1阶张量mystr = tf.Variable(["Hello", "World"], tf.string)cool_numbers = tf.Variable...)linear_squares = tf.Variable([[4], [9], [16], [25]], tf.int32) squarish_squares = tf.Variable([ [4,

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tensorflow: bn层

tf.Variable 'my/BatchNorm/moving_mean:0' shape=(1,) dtype=float32_ref> : [-77.38012695] tf.Variable...tf.Variable 'my/BatchNorm/moving_mean:0' shape=(1,) dtype=float32_ref> : [ 76.91065216] tf.Variable...tf.Variable 'my/BatchNorm/moving_mean:0' shape=(1,) dtype=float32_ref> : [ 17.47742271] tf.Variable...tf.Variable 'my/BatchNorm/moving_mean:0' shape=(1,) dtype=float32_ref> : [ 12.54585934] tf.Variable...tf.Variable 'my/BatchNorm/moving_mean:0' shape=(1,) dtype=float32_ref> : [ 12.54585934] tf.Variable

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tensorflow运作方式

定义变量，初始化,一般初始化随机值，或者常值 weights = tf.Variable(tf.random_normal([784, 200],stddev=0.35),...name='weights') from tensorflow.python.framework import ops ops.reset_default_graph() biases = tf.Variable...(tf.random_normal([784, 200], stddev=0.35), name='weights') w2 =tf.Variable...(weights.initialized_value(), name='w2') w_twice = tf.Variable(weights.initialized_value()*0.2,name...(tf.zeros([784,200]),name='weights') w_2 = tf.Variable(weightss, name='w2') w_t = tf.Variable

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tensorflow: bn层的 decay参数项

tf.Variable 'my/BatchNorm/moving_mean:0' shape=(1,) dtype=float32_ref> : [ 0.]...tf.Variable 'my/BatchNorm/moving_mean:0' shape=(1,) dtype=float32_ref> : [ 114.78817749] tf.Variable...tf.Variable 'my/BatchNorm/beta:0' shape=(1,) dtype=float32_ref> : [ 0.]...tf.Variable 'my/BatchNorm/moving_mean:0' shape=(1,) dtype=float32_ref> : [ 114.78817749] tf.Variable...tf.Variable 'my/BatchNorm/moving_mean:0' shape=(1,) dtype=float32_ref> : [ 114.78817749] tf.Variable

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TensorFlow修炼之道（2）——变量（Variable）

使用 tf.Variable 时，如果检测到命名冲突，系统会自动处理。...'w1:0' shape=(2, 5) dtype=float32_ref>, tf.Variable 'w1_1:0' shape=(2, 5) dtype=float32_ref>, tf.Variable...: [tf.Variable 'w1:0' shape=(2, 5) dtype=float32_ref>, tf.Variable 'w1_1:0' shape=(2, 5) dtype=float32..._ref>, tf.Variable 'scope/b:0' shape=(2, 5) dtype=float32_ref>, tf.Variable 'v:0' shape=(1,) dtype=...float32_ref>] my_col: [tf.Variable 'w1:0' shape=(2, 5) dtype=float32_ref>, tf.Variable 'scope/b:0

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深度学习|tensorflow张量运算

X = tf.Variable([[1.,1.,1.]])...b = tf.Variable([[ 0.1,0.2]]) XW =tf.Variable([[-1.3,0.4]]) Sum =XW+b with tf.Session() as sess:...X = tf.Variable([[0.4,0.2,0.4]]) W = tf.Variable([[-0.5,-0.2 ], [-0.3, 0.4 ],...W = tf.Variable(tf.random_normal([3, 2])) b = tf.Variable(tf.random_normal([1, 2])) X = tf.Variable([...W = tf.Variable(tf.random_normal([3, 2])) b = tf.Variable(tf.random_normal([1, 2])) X = tf.placeholder

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tensorflow中的上采样(unpool)和反卷积(conv2d_transpose)

(tf.random_normal([3, 3, 3, 16])), "conv2": tf.Variable(tf.random_normal([3, 3, 16, 32])),..."conv3": tf.Variable(tf.random_normal([3, 3, 32, 32])), "deconv2": tf.Variable(tf.random_normal...([3, 3, 16, 32])), "deconv1": tf.Variable(tf.random_normal([3, 3, 1, 16])) } biases = { "conv1...": tf.Variable(tf.random_normal([16])), "conv2": tf.Variable(tf.random_normal([32])), "...conv3": tf.Variable(tf.random_normal([32])), "deconv2": tf.Variable(tf.random_normal([16])),

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tf24: GANs—生成明星脸

(tf.constant(0.0, shape=[2 * IMAGE_SIZE * IMAGE_SIZE]), name='Generator/b_1'), 'beta_1': tf.Variable..., "b_3": tf.Variable(tf.constant(0.0, shape=[128]), name='Generator/b_3'), 'beta_3': tf.Variable(tf.constant.../b_4'), 'beta_4': tf.Variable(tf.constant(0.0, shape=[64]), name='Generator/beta_4'), 'gamma_4': tf.Variable..._3": tf.Variable(tf.constant(0.0, shape=[128]), name='Discriminator/b_3'), 'beta_3': tf.Variable(tf.constant.../W_5'), "b_5": tf.Variable(tf.constant(0.0, shape=[32]), name='Discriminator/b_5'), 'beta_5': tf.Variable

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深度学习之 TensorFlow（五）：mnist 的 Alexnet 实现

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Tensorflow|通过Variable及assign()感悟一点设计之道

01 Variable a = tf.Variable(2, name="scalar") # create variable a with scalar value b = tf.Variable(...[2, 3], name="vector") # create variable b as a vector c = tf.Variable([[0, 1], [2, 3]], name="matrix...# create variable c as a 2x2 matrix # create variable W as 784 x 10 tensor, filled with zeros W = tf.Variable...Just like this: W = tf.Variable(10) assign_op = W.assign(100) with tf.Session() as sess: sess.run(...Go on: # create a variable whose original value is 2 my_var = tf.Variable(2, name="my_var") # assign

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Python人工智能 | 十五.无监督学习Autoencoder原理及聚类可视化案例详解

tf.Variable(tf.random_normal([n_hidden_1])), 'encoder_b2': tf.Variable(tf.random_normal([n_hidden..._2])), 'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])), 'decoder_b2': tf.Variable...'encoder_h4': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_4])), 'decoder_h1': tf.Variable...'decoder_b2': tf.Variable(tf.random_normal([n_hidden_2])), 'decoder_b3': tf.Variable(...'decoder_b2': tf.Variable(tf.random_normal([n_hidden_2])), 'decoder_b3': tf.Variable(

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tf.name_scope()和tf.variable_scope()

tf.variable_scope可以让变量有相同的命名，包括tf.get_variable得到的变量，还有tf.Variable的变量 tf.name_scope可以让变量有相同的命名，只是限于tf.Variable...tf.variable_scope('V1'): a1 = tf.get_variable(name='a1', shape=[1], initializer=tf.constant_initializer(1)) a2 = tf.Variable...tf.name_scope('V1'): a1 = tf.get_variable(name='a1', shape=[1], initializer=tf.constant_initializer(1)) a2 = tf.Variable...('V1'): # a1 = tf.get_variable(name='a1', shape=[1], initializer=tf.constant_initializer(1)) a2 = tf.Variable...('V2'): # a3 = tf.get_variable(name='a1', shape=[1], initializer=tf.constant_initializer(1)) a4 = tf.Variable

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tf.get_collection()

'conv1/weights:0' shape=(3, 3, 3, 96) dtype=float32_ref> tf.Variable 'conv1/biases:0' shape=(96...,) dtype=float32_ref> tf.Variable 'conv2/weights:0' shape=(3, 3, 96, 64) dtype=float32_ref>...tf.Variable 'conv2/biases:0' shape=(64,) dtype=float32_ref> tf.Variable 'local3/weights:0' shape...tf.Variable 'local4/weights:0' shape=(384, 192) dtype=float32_ref> tf.Variable 'local4/biases:...0' shape=(192,) dtype=float32_ref> tf.Variable 'softmax_linear/softmax_linear:0' shape=(192, 10

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点击加载更多

tf.Variable

tf.Variable()函数

Tensorflow教程： tf.Variable() 和tf.get_variable()

tensorflow之tf.placeholder 与 tf.Variable区别对比

tensorflow之tf.placeholder 与 tf.Variable区别对比

Tensorflow教程(十三) tf.Variable() 和tf.get_variable()1 简介2 区别3 实例

tf.variable和tf.get_Variable以及tf.name_scope和tf.variable_scope的区别

tensor张量

tensorflow: bn层

tensorflow运作方式

tensorflow: bn层的 decay参数项

TensorFlow修炼之道（2）——变量（Variable）

深度学习|tensorflow张量运算

tensorflow中的上采样(unpool)和反卷积(conv2d_transpose)

tf24: GANs—生成明星脸

深度学习之 TensorFlow（五）：mnist 的 Alexnet 实现

Tensorflow|通过Variable及assign()感悟一点设计之道

Python人工智能 | 十五.无监督学习Autoencoder原理及聚类可视化案例详解

tf.name_scope()和tf.variable_scope()

tf.get_collection()

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