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tf46：再议tf.estimator之便利

tf.nn.relu, use_bias=True, trainable=True, bias_initializer=tf.zeros_initializer(), kernel_initializer=tf.contrib.layers.xavier_initializer...tf.nn.relu, use_bias=True, trainable=True, bias_initializer=tf.zeros_initializer(), kernel_initializer=tf.contrib.layers.xavier_initializer...tf.nn.relu, use_bias=True, trainable=True, bias_initializer=tf.zeros_initializer(), kernel_initializer=tf.contrib.layers.xavier_initializer...tf.nn.relu, use_bias=True, trainable=True, bias_initializer=tf.zeros_initializer(), kernel_initializer=tf.contrib.layers.xavier_initializer...tf.nn.relu, use_bias=True, trainable=True, bias_initializer=tf.zeros_initializer(), kernel_initializer=tf.contrib.layers.xavier_initializer

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【tensorflow速成】Tensorflow图像分类从模型自定义到测试

name="conv1", filters=12,kernel_size=[3,3], strides=(2,2), activation=tf.nn.relu,kernel_initializer=tf.contrib.layers.xavier_initializer...(),bias_initializer=tf.contrib.layers.xavier_initializer()) bn1 = tf.layers.batch_normalization...(),bias_initializer=tf.contrib.layers.xavier_initializer()) bn2 = tf.layers.batch_normalization...(),bias_initializer=tf.contrib.layers.xavier_initializer()) bn3 = tf.layers.batch_normalization...(),bias_initializer=tf.contrib.layers.xavier_initializer()) x 即输入，name 是网络名字，filters 是卷积核数量，kernel_size

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SELU︱在keras、tensorflow中使用SELU激活函数

how-to-do-xavier-initialization-on-tensorflow W1 = tf.get_variable("W1", shape=[784, 512], initializer=tf.contrib.layers.xavier_initializer...keep_prob=keep_prob) W2 = tf.get_variable("W2", shape=[512, 512], initializer=tf.contrib.layers.xavier_initializer...keep_prob=keep_prob) W3 = tf.get_variable("W3", shape=[512, 512], initializer=tf.contrib.layers.xavier_initializer...keep_prob=keep_prob) W4 = tf.get_variable("W4", shape=[512, 512], initializer=tf.contrib.layers.xavier_initializer...keep_prob=keep_prob) W5 = tf.get_variable("W5", shape=[512, 10], initializer=tf.contrib.layers.xavier_initializer

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TensorFlow强化学习入门（3）——构建仿真环境来进行强化学习

W1_m) + B1_m) # 第二层 W2_m = tf.get_variable("W2_m", shape=[num_hidden_m, num_hidden_m], initializer=tf.contrib.layers.xavier_initializer...第三层（输出层） # 注意这里有三个单独的输出层 W_obs_m = tf.get_variable("W_obs_m", shape=[num_hidden_m, 4], initializer=tf.contrib.layers.xavier_initializer...]), name="B_obs_m") W_reward_m = tf.get_variable("W_reward_m", shape=[num_hidden_m, 1], initializer=tf.contrib.layers.xavier_initializer...1]), name="B_reward_m") W_done_m = tf.get_variable("W_done_m", shape=[num_hidden_m, 1], initializer=tf.contrib.layers.xavier_initializer...) # 第一层 W1_p = tf.get_variable("W1", shape=[dimen,num_hidden_p], initializer=tf.contrib.layers.xavier_initializer

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Convolutional Neural Networks: Application

tf.placeholder('float',[None, n_y]) ### END CODE HERE ### return X, Y Initialize parameters 使用tf.contrib.layers.xavier_initializer...CODE HERE ### (approx. 2 lines of code) W1 = tf.get_variable("W1", [4, 4, 3, 8], initializer = tf.contrib.layers.xavier_initializer...(seed = 0)) W2 = tf.get_variable("W2", [2, 2, 8, 16], initializer = tf.contrib.layers.xavier_initializer

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tensorflow编程: Layers (contrib)

tf.contrib.layers.l2_regularizer tf.contrib.layers.l2_regularizer (scale, scope=None) Initializers tf.contrib.layers.xavier_initializer...tf.contrib.layers.xavier_initializer (uniform=True, seed=None, dtype=tf.float32) # coding=utf-8 import...tf.get_variable(name="weights", shape=[2, 2], initializer=tf.contrib.layers.xavier_initializer

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【DL笔记5】一文上手TensorFlow，并搭建神经网络实现手写数字识别

tf.get_variable(, , ) 我更常用后一种方法，因为可以直接指定initializer来赋值，比如我们常用的Xavier-initializer，就可以直接调用tf.contrib.layers.xavier_initializer...dtype=tf.float32,shape=[None,10],name='Y') # 定义各个参数： W1 = tf.get_variable('W1',[784,128],initializer=tf.contrib.layers.xavier_initializer...tf.get_variable('b1',[128],initializer=tf.zeros_initializer()) W2 = tf.get_variable('W2',[128,64],initializer=tf.contrib.layers.xavier_initializer...tf.get_variable('b2',[64],initializer=tf.zeros_initializer()) W3 = tf.get_variable('W3',[64,10],initializer=tf.contrib.layers.xavier_initializer

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CNN模型-ResNet、MobileNet、DenseNet、ShuffleNet、EfficientNet

stride=1, padding='SAME', activation_fn=None, normalizer_fn=None, weights_initializer=tf.contrib.layers.xavier_initializer...stride=1, padding='SAME', activation_fn=None, normalizer_fn=None, weights_initializer=tf.contrib.layers.xavier_initializer...stride=1, padding='SAME', activation_fn=None, normalizer_fn=None, weights_initializer=tf.contrib.layers.xavier_initializer...padding = 'SAME', activation_fn = None, normalizer_fn = None, weights_initializer = tf.contrib.layers.xavier_initializer...stride=1, padding='SAME', activation_fn=None, normalizer_fn=None, weights_initializer=tf.contrib.layers.xavier_initializer

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Convolution_model_Application_v1a

dtype=float32) 1.2 - Initialize parameters You will initialize weights/filters \(W1\) and \(W2\) using tf.contrib.layers.xavier_initializer...l2reg = tf.contrib.layers.l2_regularizer(0.001); W1 = tf.get_variable("W1", [4,4,3,8], initializer=tf.contrib.layers.xavier_initializer...(seed = 0),regularizer=l2reg) W2 = tf.get_variable("W2", [2,2,8,16], initializer=tf.contrib.layers.xavier_initializer...(seed = 0),regularizer=l2reg) # W1 = tf.get_variable("W1", [4,4,3,8], initializer=tf.contrib.layers.xavier_initializer...(seed = 0)) # W2 = tf.get_variable("W2", [2,2,8,16], initializer=tf.contrib.layers.xavier_initializer

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初步了解TensorFlow

def initialize_parameters(): # 初始化权重和偏置值 W1 = tf.get_variable("W1", [25,12288], initializer = tf.contrib.layers.xavier_initializer...[25,1], initializer = tf.zeros_initializer()) W2 = tf.get_variable("W2", [12,25], initializer = tf.contrib.layers.xavier_initializer...[12,1], initializer = tf.zeros_initializer()) W3 = tf.get_variable("W3", [6,12], initializer = tf.contrib.layers.xavier_initializer

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02.改善深层神经网络：超参数调试、正则化以及优化 W3. 超参数调试、Batch Norm和程序框架（作业：TensorFlow教程+数字手势预测）

初始化参数用 Xavier 初始化权重，0初始化偏置参考：深度学习中Xavier初始化 W1 = tf.get_variable("W1", [25,12288], initializer = tf.contrib.layers.xavier_initializer...START CODE HERE ### (approx. 6 lines of code) W1 = tf.get_variable('W1',[25,12288],initializer=tf.contrib.layers.xavier_initializer...tf.get_variable('b1',[25,1],initializer=tf.zeros_initializer()) W2 = tf.get_variable('W2',[12,25],initializer=tf.contrib.layers.xavier_initializer...tf.get_variable('b2',[12,1],initializer=tf.zeros_initializer()) W3 = tf.get_variable('W3',[6,12],initializer=tf.contrib.layers.xavier_initializer

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CosFace中的cos loss（Large Margin Cosine Loss）实现（tensorflow）

w = tf.get_variable("centers", [xs[1], num_cls], dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer

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BAT面试题13：请简要说说一个完整机器学习项目的流程

tf.get_variable("W1", shape=[input_size, hidden_layer_neurons], 8 initializer=tf.contrib.layers.xavier_initializer...tf.get_variable("W2", shape=[hidden_layer_neurons, hidden_layer_neurons], 18 initializer=tf.contrib.layers.xavier_initializer...tf.get_variable("W3", shape=[hidden_layer_neurons, hidden_layer_neurons], 28 initializer=tf.contrib.layers.xavier_initializer...tf.get_variable("W4", shape=[hidden_layer_neurons, output_size], 39 initializer=tf.contrib.layers.xavier_initializer

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卷积神经网络第一周作业 Convolution+model+-+Application+-+v1

dtype=float32) 1.2 - Initialize parameters You will initialize weights/filters W1W1W1 and W2W2W2 using tf.contrib.layers.xavier_initializer...START CODE HERE ### (approx. 2 lines of code) W1 = tf.get_variable("W1", [4, 4, 3, 8], initializer=tf.contrib.layers.xavier_initializer...(seed=0)) W2 = tf.get_variable("W2", [2, 2, 8, 16], initializer=tf.contrib.layers.xavier_initializer

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利用tensorflow训练简单的生成对抗网络GAN

tf.layers.conv2d(images, 128, 5, strides=2, padding='same', kernel_initializer= tf.contrib.layers.xavier_initializer...tf.layers.conv2d(drop1, 256, 5, strides=2, padding='same', kernel_initializer= tf.contrib.layers.xavier_initializer...tf.layers.conv2d(drop2, 512, 5, strides=2, padding='same', kernel_initializer= tf.contrib.layers.xavier_initializer

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TensorFlow强化学习入门（2）——基于策略的Agents

name="input_x") # 第一个权重层 W1 = tf.get_variable("W1", shape=[dimen, hidden_layer_neurons], initializer=tf.contrib.layers.xavier_initializer...observations, W1)) # 第二个权重层 W2 = tf.get_variable("W2", shape=[hidden_layer_neurons, 1], initializer=tf.contrib.layers.xavier_initializer

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权重初始化的几个方法

在 TensorFlow 中： W = tf.get_variable('W', [dims], tf.contrib.layers.xavier_initializer()) 还有一种是用下面这个式子乘以

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TensorFlow基础入门

作为一个示范，对于W1和b1您可以使用： W1 = tf.get_variable("W1", [25,12288], initializer = tf.contrib.layers.xavier_initializer...START CODE HERE ### (approx. 6 lines of code) W1 = tf.get_variable("W1", [25, 12288], initializer = tf.contrib.layers.xavier_initializer...25, 1], initializer = tf.zeros_initializer()) W2 = tf.get_variable("W2", [12, 25], initializer = tf.contrib.layers.xavier_initializer...[12, 1], initializer = tf.zeros_initializer()) W3 = tf.get_variable("W3", [6, 12], initializer = tf.contrib.layers.xavier_initializer

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改善深层神经网络 - 第二课第三周作业 TensorFlow Tutorial

example, to help you, for W1 and b1 you could use: W1 = tf.get_variable("W1", [25,12288], initializer = tf.contrib.layers.xavier_initializer...START CODE HERE ### (approx. 6 lines of code) W1 = tf.get_variable("W1", [25, 12288], initializer=tf.contrib.layers.xavier_initializer..., [25, 1], initializer=tf.zeros_initializer()) W2 = tf.get_variable("W2", [12, 25], initializer=tf.contrib.layers.xavier_initializer...", [12, 1], initializer=tf.zeros_initializer()) W3 = tf.get_variable("W3", [6, 12], initializer=tf.contrib.layers.xavier_initializer

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点击加载更多

tf46：再议tf.estimator之便利

【tensorflow速成】Tensorflow图像分类从模型自定义到测试

SELU︱在keras、tensorflow中使用SELU激活函数

TensorFlow强化学习入门（3）——构建仿真环境来进行强化学习

Convolutional Neural Networks: Application

tensorflow编程: Layers (contrib)

【DL笔记5】一文上手TensorFlow，并搭建神经网络实现手写数字识别

CNN模型-ResNet、MobileNet、DenseNet、ShuffleNet、EfficientNet

推荐系统遇上深度学习(二十七)--知识图谱与推荐系统结合之RippleNet模型原理及实现

Convolution_model_Application_v1a

初步了解TensorFlow

02.改善深层神经网络：超参数调试、正则化以及优化 W3. 超参数调试、Batch Norm和程序框架（作业：TensorFlow教程+数字手势预测）

CosFace中的cos loss（Large Margin Cosine Loss）实现（tensorflow）

BAT面试题13：请简要说说一个完整机器学习项目的流程

卷积神经网络第一周作业 Convolution+model+-+Application+-+v1

利用tensorflow训练简单的生成对抗网络GAN

TensorFlow强化学习入门（2）——基于策略的Agents

权重初始化的几个方法

TensorFlow基础入门

改善深层神经网络 - 第二课第三周作业 TensorFlow Tutorial

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