tf.global_variables_initializer()_为什么多次运行tf.global_variables_initializer()不会改变结果？_试图在Tensorflow中使用未初始化的值变量(使用的是sess.run(tf.global_variables_initializer())！) - 腾讯云开发者社区

一、tf.global_variables_initializer()tf.global_variables_initializer()添加节点用于初始化所有的变量(GraphKeys.VARIABLES...示例代码如下：sess.run(tf.global_variables_initializer(),feed_dict={ learning_rate_dis: learning_rate_val_dis

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tf.initialize_all_variables()和tf.global_variables_initializer()的区别

注意对于 tf.initialize_all_variables() 接口，TensorFlow 文档有一个重要说明。

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tensorflow学习笔记（四十五）：sess.run(tf.global_variables_initializer()) 做了什么？

当我们训练自己的神经网络的时候，无一例外的就是都会加上一句 sess.run(tf.global_variables_initializer()) ，这行代码的官方解释是初始化模型的参数。...所以： sess.run(tf.global_variables_initializer()) 就是 run了所有global Variable 的 assign op，这就是初始化参数的本来面目。

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tensorflow（一）windows 10 64位安装tensorflow1.4与基本概念解读tf.global_variables_initializer

optimizer = tf.train.GradientDescentOptimizer(0.5) train = optimizer.minimize(loss) # 初始化变量 init = tf.global_variables_initializer...() #官网是tf.initialize_all_variables() 该函数将不再使用，在 2017年#3月2号以后；用 tf.global_variables_initializer() 替代 #

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tensorflow（一）windows 10 64位安装tensorflow1.4与基本概念解读tf.global_variables_initializer

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tensorflow: variable初始化

全部变量初始化 tf.global_variables_initializer() 将在其创建时查看全局图并自动将依赖关系添加到图中的每个 tf.initializer。...tf.Session.run(tf.global_variables_initializer()) 等同于： with tf.Session() as sess: init = tf.global_variables_initializer...() sess.run(init) 等同于： init = tf.global_variables_initializer() sess = tf.Session() sess.run(init...) tf.global_variables_initializer() == tf.initialize_all_variables() 但是在 2017年3月2号以后, tf.initialize_all_variables...取而代之的是　tf.global_variables_initializer() 来自TensorFlow 文档的重要说明： tf.initialize_all_variables(): THIS

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tf.matmul()和tf.multipy()的区别

., 6.]])b=np.float32(np.random.randn(3,2))#c=tf.matmul(a,b)c=tf.multiply(a,b)init=tf.global_variables_initializer..., 5, 6]],dtype=np.float32)b=np.float32(np.random.randn(3,2))c=tf.matmul(a,b)#c=tf.multiply(a,b)init=tf.global_variables_initializer...tf.constant([[1, 2, 3],[4, 5, 6]])b=np.float32(np.random.randn(3,2))c=tf.matmul(a,b)#c=tf.multiply(a,b)init=tf.global_variables_initializer...tf.constant([[1, 2, 3],[4, 5, 6]])b=np.float32(np.random.randn(2,3))#c=tf.matmul(a,b)c=tf.multiply(a,b)init=tf.global_variables_initializer...np.float32)b=tf.constant([[1, 2, 3],[4, 5, 6]], dtype=np.int32)#c=tf.matmul(a,b)c=tf.multiply(a,b)init=tf.global_variables_initializer

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深度学习|tensorflow张量运算

XW =tf.matmul(X,W ) with tf.Session() as sess: init = tf.global_variables_initializer...tf.Variable([[ 0.1,0.2]]) XW =tf.Variable([[-1.3,0.4]]) Sum =XW+b with tf.Session() as sess: init = tf.global_variables_initializer...) XWb =tf.matmul(X,W)+b y=tf.nn.relu(tf.matmul(X,W)+b) with tf.Session() as sess: init = tf.global_variables_initializer...= tf.Variable([[0.4,0.2,0.4]]) y=tf.nn.relu(tf.matmul(X,W)+b) with tf.Session() as sess: init = tf.global_variables_initializer...tf.placeholder("float", [None,3]) y=tf.nn.relu(tf.matmul(X,W)+b) with tf.Session() as sess: init = tf.global_variables_initializer

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tensorflow运作方式

import ops ops.reset_default_graph() biases = tf.Variable(tf.zeros([200]), name='biases') init_op = tf.global_variables_initializer...w2') w_twice = tf.Variable(weights.initialized_value()*0.2,name='w_twice') # 保存变量 init_op = tf.global_variables_initializer...w2') w_twice = tf.Variable(weights.initialized_value()*0.2,name='w_twice') # 保存变量 init_op = tf.global_variables_initializer...784,200]), name='my_wt') #weightss = tf.Variable(tf.zeros([784,200]),name='my_weight') init_op = tf.global_variables_initializer

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tensorflow学习笔记（四十一）：control dependencies

tf.control_dependencies([opt]): updated_weight = tf.identity(weight) with tf.Session() as sess: tf.global_variables_initializer...tf.control_dependencies([ema_op]): ema_val = ema.average(update) with tf.Session() as sess: tf.global_variables_initializer...ema_op]): ema_val = tf.identity(ema.average(update)) #一个identity搞定 with tf.Session() as sess: tf.global_variables_initializer...ema_op]): w1 = tf.Variable(2.0) ema_val = ema.average(update) with tf.Session() as sess: tf.global_variables_initializer

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TensorFlow架构与设计：变量初始化

按照习惯用法，使用tf.global_variables_initializer()将所有全局变量的初始化器汇总，并对其进行初始化。...init = tf.global_variables_initializer()with tf.Session() as sess: sess.run(init) 按照既有经验，其计算图大致如下图所示...变量初始化模型初始化过程然后，通过调用tf.global_variables_initializer()将变量的所有初始化器进行汇总，然后启动Session运行该OP。...init = tf.global_variables_initializer() 事实上，搜集所有全局变量的初始化器的OP是一个NoOp，即不存在输入，也不存在输出。...初始化依赖同样地，可以通过调用tf.global_variables_initializer()将变量的所有初始化器进行汇总，然后启动Session完成所有变量的初始化。

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tensorflow编程: Layers (contrib)

convolution, deconvolution = func(input_x, 1, 1) with tf.Session() as sess: sess.run(tf.global_variables_initializer...tf.nn.dropout(x=input_x, keep_prob=keep_prob) with tf.Session() as sess: sess.run(tf.global_variables_initializer...fully_connected(inputs=input_x, num_outputs=1) with tf.Session() as sess: sess.run(tf.global_variables_initializer...tf.exp(logits), dim, keep_dims=True)) return my_softmax with tf.Session() as sess: sess.run(tf.global_variables_initializer...tf.constant_initializer() : tf.global_variables_initializer

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tensorflow学习笔记（三十）：tf.gradients 与 tf.stop_gradient() 与高阶导数

res = tf.matmul(w1, [[2],[1]]) grads = tf.gradients(res,[w1,w2]) with tf.Session() as sess: tf.global_variables_initializer...]]) res = tf.matmul(w1, [[2],[1]]) grads = tf.gradients(res,[w1]) with tf.Session() as sess: tf.global_variables_initializer...with tf.Session() as sess: tf.global_variables_initializer().run() print(sess.run(grads)) [array...b) e = tf.add(c_stoped, d) gradients = tf.gradients(e, xs=[a, b]) with tf.Session() as sess: tf.global_variables_initializer...opt.apply_gradients(zip(gradients, tf.trainable_variables())) print(gradients) with tf.Session() as sess: tf.global_variables_initializer

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TensorFlow入门1-minist

tf.log(y)) train_step = tf.train.GradientDescentOptimizer( 0.01).minimize(cross_entroy) init = tf.global_variables_initializer...feed_dict = {x:batch_xs, y_:batch_ys}) tf.argmax函数用法 testArgmax=tf.argmax([[12,34,3],[78,21,45]],0) init = tf.global_variables_initializer...testArgmax) 输出（第二个参数为0，取出每一列最大值的索引） array([1, 0, 1]) testArgmax=tf.argmax([[12,34,3],[78,21,45]],1) init = tf.global_variables_initializer...testArgmax1, testArgmax2) testcast = tf.cast(accurate, 'float') accuracy = tf.reduce_mean(testcast) init = tf.global_variables_initializer

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tensorflow: 对variable_scope进行reuse的两种方法

with tf.Session() as sess: sess.run(tf.global_variables_initializer())...output = func(input_x, 1, 1) with tf.Session() as sess: sess.run(tf.global_variables_initializer...=0)) ### 改动部分 ### with tf.Session() as sess: sess.run(tf.global_variables_initializer...=0)) ### 改动部分 ### with tf.Session() as sess: sess.run(tf.global_variables_initializer

7.9K11 5

TensorFlow实现模型断点训练,checkpoint模型载入方式

/log/' + "model_savemodel.cpkt-" + str(20000)) sess.run(tf.global_variables_initializer()) ckpt = tf.train.get_checkpoint_state.../log/' + "model_savemodel.cpkt-" + str(111000)) sess.run(tf.global_variables_initializer()) 载入模型后，会继续端点处的变量继续训练...model-9400 import tensorflow as tf graph=tf.get_default_graph() # 获取当前图 sess=tf.Session() sess.run(tf.global_variables_initializer...model-9200 import tensorflow as tf graph=tf.get_default_graph() # 获取当前图 sess=tf.Session() sess.run(tf.global_variables_initializer

1.9K3 1

tf.InteractiveSession()与tf.Session()

., 6.]]) b=np.float32(np.random.randn(3,2)) c=tf.matmul(a,b) init=tf.global_variables_initializer() sess...a=tf.constant([[1., 2., 3.],[4., 5., 6.]]) b=np.float32(np.random.randn(3,2)) c=tf.matmul(a,b) init=tf.global_variables_initializer...a=tf.constant([[1., 2., 3.],[4., 5., 6.]]) b=np.float32(np.random.randn(3,2)) c=tf.matmul(a,b) init=tf.global_variables_initializer

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tensorflow变量命名空间与可视化

--------' if var1 is var2: print sess.run(var2) print var1.name, var2.name sess.run(tf.global_variables_initializer...var2: print sess.run(var2) print var1.name, var2.name print '-----------' sess.run(tf.global_variables_initializer...tf.random_uniform([3]),name="input2") output = tf.add_n([input1,input2],name = "add") sess.run(tf.global_variables_initializer...with tf.name_scope('add1'): output = tf.add_n([input1,input2],name = "add") sess.run(tf.global_variables_initializer

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TensorFlow打印输出tensor的值

tf.InteractiveSession() ones.initializer.run() print(inter_sess.run(ones)) # 方法2 inter_sess=tf.InteractiveSession() tf.global_variables_initializer...print(inter_sess.run(ones)) # 方法3 Session + global_variables_initializer sess=tf.Session() sess.run(tf.global_variables_initializer...sess.run(ones)) # 方法4 with Session + global_variables_initializer with tf.Session() as sess: sess.run(tf.global_variables_initializer

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tensorflow学习笔记(三十四)：Saver(保存与加载模型)

":v1, "variable_2": v2}) # Use the saver object normally after that. with tf.Session() as sess: tf.global_variables_initializer...tf.train.Saver([v1, v2]) # Use the saver object normally after that. with tf.Session() as sess: tf.global_variables_initializer...":v1, "variable_2": v2}) # Use the saver object normally after that. with tf.Session() as sess: tf.global_variables_initializer...":v2, "variable_2": v1}) # Use the saver object normally after that. with tf.Session() as sess: tf.global_variables_initializer

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tf.global_variables_initializer()与tf.local_variables_initializer()的区别

tf.initialize_all_variables()和tf.global_variables_initializer()的区别

tensorflow学习笔记（四十五）：sess.run(tf.global_variables_initializer()) 做了什么？

tensorflow（一）windows 10 64位安装tensorflow1.4与基本概念解读tf.global_variables_initializer

tensorflow（一）windows 10 64位安装tensorflow1.4与基本概念解读tf.global_variables_initializer

tensorflow: variable初始化

tf.matmul()和tf.multipy()的区别

深度学习|tensorflow张量运算

tensorflow运作方式

tensorflow学习笔记（四十一）：control dependencies

TensorFlow架构与设计：变量初始化

tensorflow编程: Layers (contrib)

tensorflow学习笔记（三十）：tf.gradients 与 tf.stop_gradient() 与高阶导数

TensorFlow入门1-minist

tensorflow: 对variable_scope进行reuse的两种方法

TensorFlow实现模型断点训练,checkpoint模型载入方式

tf.InteractiveSession()与tf.Session()

tensorflow变量命名空间与可视化

TensorFlow打印输出tensor的值

tensorflow学习笔记(三十四)：Saver(保存与加载模型)

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