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
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()
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
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
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
==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=
, <tf.Variable 'block1_conv2/bias:0' shape=(64,) dtype=float32_ref , <tf.Variable 'block2_conv1/kernel..._ref , <tf.Variable 'block2_conv2/kernel:0' shape=(3, 3, 128, 128) dtype=float32_ref , <tf.Variable '...<tf.Variable 'block3_conv2/kernel:0' shape=(3, 3, 256, 256) dtype=float32_ref , <tf.Variable 'block3_...float32_ref , <tf.Variable 'block4_conv2/bias:0' shape=(512,) dtype=float32_ref , <tf.Variable 'block4...ref , <tf.Variable 'block5_conv1/bias:0' shape=(512,) dtype=float32_ref , <tf.Variable 'block5_conv2/
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'>, , ] 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,
tf.Variable(tf.random_normal([3,3,512,512])), 'wc11':tf.Variable(tf.random_normal([3,3,512,512])),...([64])), 'bc3':tf.Variable(tf.zeros([128])), 'bc4':tf.Variable(tf.zeros([128])), 'bc5':tf.Variable...':tf.Variable(tf.zeros([256])), 'bc9':tf.Variable(tf.zeros([512])), 'bc10':tf.Variable(tf.zeros([...512])), 'bc11':tf.Variable(tf.zeros([512])), 'bc12':tf.Variable(tf.zeros([512])), 'bc13':tf.Variable...':tf.Variable(tf.zeros([256])), 'bd1':tf.Variable(tf.zeros([4096])), 'bd2':tf.Variable(tf.zeros
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
: [ 0.]... : [ 114.78817749] : [ 0.]... : [ 114.78817749] : [ 114.78817749] <tf.Variable
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
: [-77.38012695] : [ 76.91065216] : [ 17.47742271] : [ 12.54585934] : [ 12.54585934] <tf.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...: [, , , ] my_col: [, <tf.Variable 'scope/b:0
(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
(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])),
wc3': tf.Variable(tf.random_normal([3, 3, 256, 384])), 'wc4': tf.Variable(tf.random_normal([3, 3,...384, 384])), 'wc5': tf.Variable(tf.random_normal([3, 3, 384, 256])), 'wd1': tf.Variable(tf.random_normal...([1024, n_classes])) } biases = { 'bc1': tf.Variable(tf.random_normal([96])), 'bc2': tf.Variable...([384])), 'bc5': tf.Variable(tf.random_normal([256])), 'bd1': tf.Variable(tf.random_normal([4096...])), 'bd2': tf.Variable(tf.random_normal([1024])), 'out': tf.Variable(tf.random_normal([n_classes
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(
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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