Tensorflow学习教程------创建图启动图

 

参考博客:https://www.cnblogs.com/cnugis/p/7635128.html
视频链接:https://www.bilibili.com/video/av20542427?from=search&seid=3218324014806772997

 

　Tensorflow作为目前最热门的机器学习框架之一，受到了工业界和学界的热门追捧。以下几章教程将记录本人学习tensorflow的一些过程。

　　在tensorflow这个框架里，可以讲是弱数据类型，也就是说不严格声明数据是什么类型，因为在整个过程中玩的都是向量，或者说矩阵和数组，所有的数据都被看做是一个tensor, 一个或者几个tensor经过一个op(operation)之后，产生新的tensor。首先将所有tensor和op都定义好，然后把这套tensor和op的组合放到默认的图里，用会话启动图，最后我们就看到结果了。以下是创建图和启动图的代码以及结果。

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#coding:utf-8 import tensorflow as tf m1 = tf.constant([[3,3]]) m2 = tf.constant([[2],[3]]) product = tf.matmul(m1,m2) print (product) #定义一个会话 启动默认图 sess = tf.Session() #调用sess的run方法来执行矩阵乘法 result = sess.run(product) print (result) sess.close()

　　结果如下

I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally
Tensor("MatMul:0", shape=(1, 1), dtype=int32)
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: 
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.582
pciBusID 0000:03:00.0
Total memory: 10.91GiB
Free memory: 10.24GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:03:00.0)
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