Tensorflow學習教程------建立圖啟動圖
阿新 • • 發佈：2018-11-14
Tensorflow作為目前最熱門的機器學習框架之一，受到了工業界和學界的熱門追捧。以下幾章教程將記錄本人學習tensorflow的一些過程。
在tensorflow這個框架裡，可以講是弱資料型別，也就是說不嚴格宣告資料是什麼型別，因為在整個過程中玩的都是向量，或者說矩陣和陣列，所有的資料都被看做是一個tensor, 一個或者幾個tensor經過一個op(operation)之後，產生新的tensor。首先將所有tensor和op都定義好，然後把這套tensor和op的組合放到預設的圖裡，用會話啟動圖，最後我們就看到結果了。以下是建立圖和啟動圖的程式碼以及結果。
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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)
#定義一個會話 啟動預設圖
sess
=
tf.Session()#呼叫sess的run方法來執行矩陣乘法
result
=
sess.run(product)
print
(result)
sess.close()
|
結果如下
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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