tensorflow | 易学教程

TypeError: The added layer must be an instance of class Layer. Found: <keras.engine.training.Model object at 0x7fa5bee17ac8>

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问题 I am try to train a model using Xception /Inception Model of keras library but I face value error Dataset which I use it from kaggle commuinity and Notebook which I refer Notebook But I am try to use different Model like Xception /Inception but silmilar idea not work for me with strategy.scope(): enet = keras.applications.inception_v3.InceptionV3( input_shape=(512, 512, 3), weights='imagenet', include_top=False ) model = tf.keras.Sequential([ enet, tf.keras.layers.GlobalAveragePooling2D(), tf

Keras returns binary results

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问题 I want to predict the kind of 2 diseases but I get results as binary (like 1.0 and 0.0). How can I get accuracy of these (like 0.7213)? Training code: from keras.models import Sequential from keras.layers import Conv2D from keras.layers import MaxPooling2D from keras.layers import Flatten from keras.layers import Dense # Intialising the CNN classifier = Sequential() # Step 1 - Convolution classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu')) # Step 2 - Pooling

少有人知的python数据科学库

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Python是门很神奇的语言，历经时间和实践检验，受到开发者和数据科学家一致好评，目前已经是全世界发展最好的编程语言之一。简单易用，完整而庞大的第三方库生态圈，使得Python成为编程小白和高级工程师的首选。在本文中，我们会分享不同于市面上的python数据科学库（如numpy、padnas、scikit-learn、matplotlib等），尽管这些库很棒，但是其他还有一些不为人知，但同样优秀的库需要我们去探索去学习。 1. Wget 从网络上获取数据被认为是数据科学家的必备基本技能，而Wget是一套非交互的基于命令行的文件下载库。ta支持HTTP、HTTPS和FTP协议，也支持使用IP代理。因为ta是非交互的，即使用户未登录，ta也可以在后台运行。所以下次如果你想从网络上下载一个页面，Wget可以帮到你哦。安装 pip isntall wget 用例 import wget url = 'http://www.futurecrew.com/skaven/song_files/mp3/razorback.mp3' filename = wget.download(url) Run and output 100% [................................................] 3841532 / 3841532 filename

tensorflow卷积神经网络-【老鱼学tensorflow】

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前面我们曾有篇文章中提到过关于用tensorflow训练手写28 28像素点的数字的识别，在那篇文章中我们把手写数字图像直接碾压成了一个784列的数据进行识别，但实际上，这个图像是28 28长宽结构的，我们这次使用CNN卷积神经网络来进行识别。卷积神经网络我的理解是部分模仿了人眼的功能。我们在看一个图像时不是一个像素点一个像素点去分辨的，我们的眼睛天然地具有大局观，我们看到某个图像时自动地会把其中的细节部分给聚合起来进行识别，相反，如果我们用个放大镜看到其中的各个像素点时反而不知道这是啥东西了。因此卷积神经网络就把每个像素点的图像进行一定程度上的模糊化，而怎么进行模糊化呢？它是通过选择一小片的区域范围，把这小片中的图像数据缩小其长宽，但增加其高度值。然后进行某种计算，最终达到有点类似模糊化图像的目的，但这个模糊化的图像中反而能够比较容易识别出相应的边界及形状。具体大家可以到网上搜索相关的理论知识，这里不细讲，只专注于如何在tensorflow中实现 CNN的功能。之前在tensorflow分类-【老鱼学tensorflow】中已经用一般的神经网络进行过手写数字的识别，我们在那个程序的基础上来进行，那篇文章的地址为： http://www.cnblogs.com/dreampursuer/p/8026866.html import tensorflow as tf #

How to solve tensor flow cpu dll not found error

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问题 I have install tensorflow v2.1.0 with python version 3.6.6 and pip version 20.0.2 . When i try to import tensorflow i got below error. C:\Users\Dexter>python Python 3.6.6 (v3.6.6:4cf1f54eb7, Jun 27 2018, 03:37:03) [MSC v.1900 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow Traceback (most recent call last): File "C:\Users\Dexter\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow_core\python\pywrap

How to solve tensor flow cpu dll not found error

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Tensorflow学习笔记6：解决tensorflow训练过程中GPU未调用问题

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Tensorflow学习笔记6：解决tensorflow训练过程中GPU未调用问题参考文章：（1）Tensorflow学习笔记6：解决tensorflow训练过程中GPU未调用问题（2）https://www.cnblogs.com/zheng1076/p/11239085.html 备忘一下。来源： oschina 链接： https://my.oschina.net/stackoom/blog/4935122

Softmax Cross Entropy implementation in Tensorflow Github Source Code

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问题 I am trying to implement a Softmax Cross-Entropy loss in python. So, I was looking at the implementation of Softmax Cross-Entropy loss in the GitHub Tensorflow repository. I am trying to understand it but I run into a loop of three functions and I don't understand which line of code in the function is computing the Loss? The function softmax_cross_entropy_with_logits_v2(labels, logits, axis=-1, name=None) returns the function softmax_cross_entropy_with_logits_v2_helper(labels=labels, logits

How to change Sequential model to Custom Class model

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问题 I'm learning tensorflow 2.0 from its older versions. I found tensorflow model is changed Sequential-base from Class-base. But I want to use Class-base model because it is easy to read for me. I want to try translate : https://www.tensorflow.org/beta/tutorials/keras/basic_text_classification_with_tfhub embedding = 'https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1' hub_layer = hub.KerasLayer(embedding, input_shape=[], dtype=tf.string, trainable=True) # hub_layer(train_example_batch[:3]

FailedPreconditionError: GetNext() failed because the iterator has not been initialized in Tensorflow 2.3

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问题 Why do I get dataset not initialized error in Tensorflow 2.3 where there is no longer a concept of session? import tensorflow as tf print(tf.__version__) bm = tf.estimator.BaselineRegressor( model_dir=None, label_dimension=1, weight_column=None, optimizer='Ftrl', ) def create_dummy_data(): d = tf.data.Dataset.from_generator(lambda: iter( ({'osName': b'Android OS', 'osVersion': b'5.1.1', 'deviceModel': b'dfjhgsdjhfshfjsgdjhf'}, 100), ... ), output_types=({ "osName": tf.string, "osVersion": tf