如何在 Kaggle Notebook 上安装 "Tree Ensemble Layer"
How to install the "Tree Ensemble Layer" on Kaggle Notebook
我想在 Kaggle Notebook 上尝试以下代码,但找不到安装 tf_trees 的方法。
from tensorflow import keras
from tf_trees import TEL
tree_layer = TEL(output_logits_dim=2, trees_num=10, depth=3)
model = keras.Sequential()
model.add(keras.layers.BatchNormalization())
model.add(tree_layer)
似乎无法使用 !pip install
安装 tf_trees
如果有人能提出解决方案,我将不胜感激。谢谢。
来源:https://github.com/google-research/google-research/tree/master/tf_trees
首先打开互联网支持,然后从 github:
克隆 google-research repo
!git clone https://github.com/google-research/google-research.git
然后我们需要 g++ 的编译和链接选项,所以 运行 以下代码片段:
import tensorflow as tf;
print(" ".join(tf.sysconfig.get_compile_flags()))
和
import tensorflow as tf;
print(" ".join(tf.sysconfig.get_link_flags()))
对于我的笔记本,我得到了以下标志:
-I/opt/conda/lib/python3.7/site-packages/tensorflow/include -D_GLIBCXX_USE_CXX11_ABI=0
-L/opt/conda/lib/python3.7/site-packages/tensorflow -l:libtensorflow_framework.so.2
之后只需将变量 ${TF_CFLAGS[@]}
和 ${TF_LFLAGS[@]}
替换为上述输出
!g++ -std=c++11 -shared google-research/tf_trees/neural_trees_ops.cc google-research/tf_trees/neural_trees_kernels.cc google-research/tf_trees/neural_trees_helpers.cc -o google-research/tf_trees/neural_trees_ops.so -fPIC -I/opt/conda/lib/python3.7/site-packages/tensorflow/include -D_GLIBCXX_USE_CXX11_ABI=0 -L/opt/conda/lib/python3.7/site-packages/tensorflow -l:libtensorflow_framework.so.2 -O2
最后我们需要添加系统路径
import sys
sys.path.insert(1, '/kaggle/working/google-research')
和运行您的代码段
from tensorflow import keras
from tf_trees import TEL
tree_layer = TEL(output_logits_dim=2, trees_num=10, depth=3)
model = keras.Sequential()
model.add(keras.layers.BatchNormalization())
model.add(tree_layer)
我想在 Kaggle Notebook 上尝试以下代码,但找不到安装 tf_trees 的方法。
from tensorflow import keras
from tf_trees import TEL
tree_layer = TEL(output_logits_dim=2, trees_num=10, depth=3)
model = keras.Sequential()
model.add(keras.layers.BatchNormalization())
model.add(tree_layer)
似乎无法使用 !pip install
安装 tf_trees如果有人能提出解决方案,我将不胜感激。谢谢。
来源:https://github.com/google-research/google-research/tree/master/tf_trees
首先打开互联网支持,然后从 github:
克隆 google-research repo!git clone https://github.com/google-research/google-research.git
然后我们需要 g++ 的编译和链接选项,所以 运行 以下代码片段:
import tensorflow as tf;
print(" ".join(tf.sysconfig.get_compile_flags()))
和
import tensorflow as tf;
print(" ".join(tf.sysconfig.get_link_flags()))
对于我的笔记本,我得到了以下标志:
-I/opt/conda/lib/python3.7/site-packages/tensorflow/include -D_GLIBCXX_USE_CXX11_ABI=0
-L/opt/conda/lib/python3.7/site-packages/tensorflow -l:libtensorflow_framework.so.2
之后只需将变量 ${TF_CFLAGS[@]}
和 ${TF_LFLAGS[@]}
替换为上述输出
!g++ -std=c++11 -shared google-research/tf_trees/neural_trees_ops.cc google-research/tf_trees/neural_trees_kernels.cc google-research/tf_trees/neural_trees_helpers.cc -o google-research/tf_trees/neural_trees_ops.so -fPIC -I/opt/conda/lib/python3.7/site-packages/tensorflow/include -D_GLIBCXX_USE_CXX11_ABI=0 -L/opt/conda/lib/python3.7/site-packages/tensorflow -l:libtensorflow_framework.so.2 -O2
最后我们需要添加系统路径
import sys
sys.path.insert(1, '/kaggle/working/google-research')
和运行您的代码段
from tensorflow import keras
from tf_trees import TEL
tree_layer = TEL(output_logits_dim=2, trees_num=10, depth=3)
model = keras.Sequential()
model.add(keras.layers.BatchNormalization())
model.add(tree_layer)