tensorflow

How can I get the number of CUDA cores in my GPU using Python and Numba?

由 女生的网名这么多〃 提交于 2021-01-19 09:12:28
问题 I would like to know how to obtain the total number of CUDA Cores in my GPU using Python, Numba and cudatoolkit. 回答1: Most of what you need can be found by combining the information in this answer along with the information in this answer. We'll use the first answer to indicate how to get the device compute capability and also the number of streaming multiprocessors. We'll use the second answer (converted to python) to use the compute capability to get the "core" count per SM, then multiply

Accuracy given from evaluating model not equal to sklearn classification_report accuracy

由 ￡可爱￡侵袭症＋ 提交于 2021-01-19 08:10:24
问题 I'm using sklearn classification_report for reporting test statistics. The accuracy given by this method is 42% while model evaluation gives 93% accuracy. Which one is the real accuracy and what's the reason of this difference? Model evaluation: results = model.evaluate(test_ds.values, test_lb.values) print(results) Output: 7397/7397 [==============================] - 0s 28us/sample - loss: 0.2309 - acc: 0.9305 Report Classification: import numpy as np from sklearn.metrics import

Accuracy given from evaluating model not equal to sklearn classification_report accuracy

由 梦想与她 提交于 2021-01-19 08:08:38
问题 I'm using sklearn classification_report for reporting test statistics. The accuracy given by this method is 42% while model evaluation gives 93% accuracy. Which one is the real accuracy and what's the reason of this difference? Model evaluation: results = model.evaluate(test_ds.values, test_lb.values) print(results) Output: 7397/7397 [==============================] - 0s 28us/sample - loss: 0.2309 - acc: 0.9305 Report Classification: import numpy as np from sklearn.metrics import

Accuracy given from evaluating model not equal to sklearn classification_report accuracy

由 狂风中的少年 提交于 2021-01-19 08:08:17
问题 I'm using sklearn classification_report for reporting test statistics. The accuracy given by this method is 42% while model evaluation gives 93% accuracy. Which one is the real accuracy and what's the reason of this difference? Model evaluation: results = model.evaluate(test_ds.values, test_lb.values) print(results) Output: 7397/7397 [==============================] - 0s 28us/sample - loss: 0.2309 - acc: 0.9305 Report Classification: import numpy as np from sklearn.metrics import

Difference between .pb and .h5

由 試著忘記壹切 提交于 2021-01-19 05:33:47
问题 What is the main difference between . pb format of tensorflow and .h5 format of keras to store models? Is there any reason to choose one over the other? 回答1: Different file formats with different characteristics, both used by tensorflow to save models ( .h5 specifically by keras ). .pb - protobuf It is a way to store some structured data (in this case a neural network),project is open source and currently overviewed by Google. Example person { name: "John Doe" email: "jdoe@example.com" }

Keras custom data generator giving dimension errors with multi input and multi output( functional api model)

由 夙愿已清 提交于 2021-01-18 04:53:32
问题 I have written a generator function with Keras, before returning X,y from __getitem__ I have double check the shapes of the X's and Y's and they are alright, but generator is giving dimension mismatch array and warnings. (Colab Code to reproduce: https://colab.research.google.com/drive/1bSJm44MMDCWDU8IrG2GXKBvXNHCuY70G?usp=sharing) My training and validation generators are pretty much same as class ValidGenerator(Sequence): def __init__(self, df, batch_size=64): self.batch_size = batch_size

ValueError: Error when checking target: expected dense_2 to have shape (1,) but got array with shape (14,)

由 自闭症网瘾萝莉.ら 提交于 2021-01-18 04:41:14
问题 I am trying to train a classification model in a distributed way. I am using TensorflowOnSpark library developed by Yahoo. The example I am using github link I am using dataset other than mnist which is used in example mentioned in the github link. This dataset I am using is of dimensions as follows after preprocessing (260000,28047) and also the classes(labels) range from 0:13. >>> import os >>> import tensorflow as tf >>> from tensorflow.python import keras >>> from tensorflow.python.keras

ValueError: Error when checking target: expected dense_2 to have shape (1,) but got array with shape (14,)

由 折月煮酒 提交于 2021-01-18 04:40:48
问题 I am trying to train a classification model in a distributed way. I am using TensorflowOnSpark library developed by Yahoo. The example I am using github link I am using dataset other than mnist which is used in example mentioned in the github link. This dataset I am using is of dimensions as follows after preprocessing (260000,28047) and also the classes(labels) range from 0:13. >>> import os >>> import tensorflow as tf >>> from tensorflow.python import keras >>> from tensorflow.python.keras

ValueError: Error when checking target: expected dense_2 to have shape (1,) but got array with shape (14,)

由 你说的曾经没有我的故事 提交于 2021-01-18 04:40:04
问题 I am trying to train a classification model in a distributed way. I am using TensorflowOnSpark library developed by Yahoo. The example I am using github link I am using dataset other than mnist which is used in example mentioned in the github link. This dataset I am using is of dimensions as follows after preprocessing (260000,28047) and also the classes(labels) range from 0:13. >>> import os >>> import tensorflow as tf >>> from tensorflow.python import keras >>> from tensorflow.python.keras

如何理解Axis？

由 余生颓废 提交于 2021-01-17 06:37:28
前言 只有光头才能变强。 回顾前面： 从零开始学TensorFlow【01-搭建环境、HelloWorld篇】 什么是TensorFlow？ TensorFlow读写数据 不知道大家最开始接触到axis的时候是怎么样的，反正我是挺难理解的..我们可以发现TensorFlow的很多API都有axis这个参数，如果我们对axis不了解，压根不知道API是怎么搞的。 一句话总结axis：axis可以方便我们 将数据进行不同维度的处理 。 一、理解axis 如果你像我一样，发现API中有axis这个参数，但不知道是什么意思。可能就会搜搜axis到底代表的什么意思。于是可能会类似搜到下面的信息： 使用0值表示沿着每一列或行标签\索引值向下执行方法( axis=0代表往跨行 ) 使用1值表示沿着每一行或者列标签模向执行对应的方法( axis=1代表跨列 ) 但我们又知道，我们的数组不单单只有二维的，还有三维、四维等等。 一旦维数超过二维，就无法用简单的行和列来表示了 。 所以，可以用我下面的方式进行理解： axis=0 将最开外头的括号去除，看成一个整体，在这个整体上进行运算 axis=1 将第二个括号去除，看成一个整体，在这个整体上进行运算 ...依次类推 话不多说，下面以例子说明~ 1.1二维数组之concat 首先，我们来看个 concat 的例子，concat第一个参数接收val
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