From e9b6ec32bd6248b4eadd382fee60d81f670a7d25 Mon Sep 17 00:00:00 2001 From: benjas <909336740@qq.com> Date: Fri, 18 Dec 2020 10:33:05 +0800 Subject: [PATCH] =?UTF-8?q?Update=20=E5=BF=AB=E6=89=8B=E7=94=A8=E6=88=B7?= =?UTF-8?q?=E6=B4=BB=E8=B7=83=E9=A2=84=E6=B5=8B.ipynb?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../快手用户活跃预测.ipynb | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/机器学习竞赛实战_优胜解决方案/快手短视频用户活跃度分析/快手用户活跃预测.ipynb b/机器学习竞赛实战_优胜解决方案/快手短视频用户活跃度分析/快手用户活跃预测.ipynb index f665094..3089015 100644 --- a/机器学习竞赛实战_优胜解决方案/快手短视频用户活跃度分析/快手用户活跃预测.ipynb +++ b/机器学习竞赛实战_优胜解决方案/快手短视频用户活跃度分析/快手用户活跃预测.ipynb @@ -49225,6 +49225,17 @@ } ], "source": [ + "\"\"\"\n", + "tf.placeholder：\n", + " 在神经网络构建graph的时候在模型中的占位，此时没有把数据传入模型，只会分配必要的内存。运行模型的时候通过feed_dict()函数向占位符喂入数据。\n", + " dtype：数据类型。常用的是tf.float32,tf.float64等数值类型\n", + " shape：数据形状。默认是None（一维），也可以是多维\n", + " name：名称\n", + "tf.get_variable：\n", + " 创建新的tensorflow变量\n", + " 第一列：名称\n", + " shape：变量的形状\n", + "\"\"\"\n", "n_features = 12\n", "n_hu = 8\n", "with tf.variable_scope('train'): # tf.variable_scope用来指定变量的作用域\n", @@ -49233,7 +49244,7 @@ " lr = tf.placeholder(tf.float32, [], name='learning_rate') # 定义学习率\n", " \n", " # 隐藏层到输出层的参数W, b，n_hu隐藏单元的个数\n", - " W_out = tf.get_variable('W_out', [n_hu, 1]) \n", + " W_out = tf.get_variable('W_out', [n_hu, 1])\n", " b_out = tf.get_variable('b_out', [1])\n", " \n", " # x(batch_size, seq_length, n_features)\n",