keras conv2d

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Use Xshell+xmanager+pycharm to build Pytorch remote debugging development environment

=Torch.utils.data.DataLoader (Datasets. MNIST ('.. /data', Train=false, transform=Transforms.compose ([Transforms. Totensor (), transforms. Normalize ((0.1307,), (0.3081,))]), Batch_size=args.batch_size, Shuffle=true, * *Kwargs)classNet (NN. Module):def __init__(self): Super (Net, self).__init__() Self.conv1= nn. conv2d (1, kernel_size=5) Self.conv2= nn. Conv2d (Ten, kernel_size=5) Self.conv2_drop=nn. DROPO

"Pytorch" The four-play _ through Lenet pytorch Neural Network _

"Pytorch" The four-play _ through Lenet pytorch Neural Network _# author:hellcat# Time:2018/2/11import Torch as Timport Torch.nn as Nnimport torch.nn.functional as Fclass LeNet (NN. Module): def __init__ (self): Super (Lenet,self). __init__ () Self.conv1 = nn. Conv2d (3, 6, 5) Self.conv2 = nn. conv2d (6,16,5) self.fc1 = nn. Linear (16*5*5,120) self.fc2 = nn. Linear (120,84) self.fc3 = nn. Linear (84,10) def

Ubuntu installation TensorFlow and its minist instances

, Batch_ys = mnist.tr Ain.next_batch (Sess.run) (Train_step, Feed_dict={x:batch_xs, Y_: Batch_ys}) # Test trained model CORRECT_PREDICTI On = Tf.equal (Tf.argmax (y, 1), Tf.argmax (Y_, 1)) accuracy = Tf.reduce_mean (Tf.cast (correct_prediction, tf.float32)) Prin T (Sess.run (accuracy, Feed_dict={x:mnist.test.imaGES, Y_: Mnist.test.labels})) Run result b convolutional network modified code (named testcon.py) From tensorflow.examples.tutorials.mnist import input_data import tensorflow as TF # imp

Share the 8 tools common to Python data analysis

Python is a common tool for data processing, can handle the order of magnitude from a few k to several T data, with high development efficiency and maintainability, but also has a strong commonality and cross-platform, here for you to share a few good data analysis tools, the need for friends can refer to the next Python is a common tool for data processing, which can handle data ranging from a few k to several T, with high development efficiency and maintainability, as well as a strong versati

Deep learning articles and code collections for text categorization

://github.com/richliao/textClassifier (Keras)Https://github.com/ematvey/hierarchical-attention-networks (TensorFlow)Https://github.com/EdGENetworks/attention-networks-for-classification (Pytorch)I'm a split line.[5] Recurrent convolutional neural Networks for Text classificationSiwei Lai et al.Chinese Academy of SciencesAAAI 2015https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9745/9552This article presents a cyclic convolution neural net

Build a deeplearning server

, the bigger the money, the better.Power problem: A video card power is close to 300W, four graphics card recommended power over 1500W, in order to expand later, the selection of 1600W power.Chassis heat Dissipation:Because of the size of the various components, a large chassis with good thermal dissipation is required, and the TT Thermaltake Core V51 chassis is selected, with 3 12cm fans as standard. In the future, if necessary, water-cooled equipment can be installed.The above is the main hard

Wide_and_deep_model_keras (error

# Coding: UTF-8 ''' Google wide Deep model ''' written in Keras import pandas as pdfrom Keras. models import sequentialfrom Keras. layers import dense, mergefrom sklearn. preprocessing import minmaxscaler # All data columns = ["Age", "workclass", "fnlwgt", "education", "education_num", "marital_status", "Occupation ", "relationship", "race", "gender", "capital_g

Regular expressions and the Python re module

expressions in programming languages, when we want to match \ The time needs to match 4 \, \\\\ match \, because the first programming language will transfer \\\\ to \ \, and then the second time will be transferred \ \. If you use the native string r of Python to write the regular, you can write less two \, that is, R ' \ \ ' matches \,r ' \\d ' match ' \d ', R ' \d ' matches the numberUse of the 1.2 re module#first compile the regular expression into the pattern objectPattern = Re.Compile('

Using the PIP installation package in a multi-python environment

2.7 and 3.5 Two versions of Python were installed on the notebook, and failed to create process error occurred while installing Keras with the 3.5 version of PIP. Here's how to fix it:1. Since I have configured both 2.7 and 3.5 paths in the environment variable, I can execute python3 directly at the command line to start the 3.5 version of Python;2. Start the PIP via Python3, enter the python3-m pip install Keras

Build a scientific computing environment

Environment Construction Download Anaconda Change Anaconda Package management source to Tsinghua Mirror source CMD input: Conda Config--add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ Conda config--set show_channel_urls Yes change pip source to Tsinghua Mirror C:/USER/PIP, new file Pip.ini, as follows: [Global] Index-url = Https://pypi.tuna.tsinghua.edu.cn/simple Add Jupyter kernel Use Anaconda Navigator Virtual Environment Python2 and install common third party package

importerror:failed to import Pydot. You are must install Pydot and Graphviz for ' pydotprint ' to work.

When I used the Keras visualization model, I met the above error with the following error message: Traceback (most recent): File "harrison_feature_model.py", line The solution is: Pip install pydot-ng pip install GraphvizAnd then it's solved, my system for Ubuntu 16.04 Or: sudo pip3 install pydot sudo pip3 install graphviz sudo apt-get install Graphviz The solution below is also Ubuntu 16.04, but it's Python3 Reference Documents[1].

Deep learning tool: TensorFlow system architecture and high performance programming __deep

November 9, 2015 Google Open source of the artificial intelligence platform TensorFlow, but also become the 2015 's most popular open source projects. After 12 iterations from v0.1 to v0.12, Google released its version of TensorFlow 1.0 on February 15, 2017, and hosted the first TensorFlow Dev Summit conference in Mountain View, California, USA. TensorFlow 1.0 and Dev Summit (2017) Review Compared with previous versions, the features of TensorFlow 1.0 are mainly reflected in the following aspect

Summary of LSTM model theory __NLP

), which has already appeared, 8. Summary Two key issues: 1. Why has the memory function. This is the problem solved in the RNN, because there is a recursive effect, the state of the hidden layer at the moment to participate in the calculation of this moment, the explicit point of the statement is the selection and decision-making reference to the last state. 2. Why lstm remember the long time. Because the specially designed structure has the characteristics of CEC, error up a last state when

[Deep-learning-with-python] Machine learning basics

. We should use a multi-parameter and not less-than-fit network model. The tradeoff between too much capacity and too little capacity.Unfortunately, there is no effective rule or method to determine the size of the model parameters. You must constantly try to find the optimal parameter size on the validation set. a general approach to determining the size of a model: start with a relatively simple model, gradually increase or decrease the number of neurons or the number of network layers until t

Advanced Study Preparation _ depth study

Summary In order to follow the in-depth study of the introductory, usually see the relevant sites and videos, here back up. A comparison of 5 depth learning frameworks Share a comparison video about the most popular 5 depth learning frameworks (Scikit Learn,tensorflow,theano,keras, and Caffe): http://weibo.com/p/ 23044464933dbb5463a1b0cef9ebcb4207b869. Iterate through each of the pros and cons, as well as some sample code, to make a definitive concl

win10-anaconda2-theano-cuda7.5-vs2013

. There is absolutely no need, and will cause the Spyder to start when the window, kernel died, and so on, this is my test, engaged a day ... "" When installing anaconda, do not install Python version 3.5, the total display GPU is not available. And do not install Spyder3 series, that is, more than Anaconda4.2.0. Instead, Python chooses 2.7,spyder to select the 2 series, which is the Anaconda4.1.1 version and below. What is the reason? Because Spyder3 always does not call the Ipythonw.exe interp

Python and R data analysis/mining tools Mutual Search

Sklearn.cluster.Birch Unknown K-medoids Clustering Pyclust. Kmedoids (Reliability unknown) Cluster.pam Association Rules category Python R Apriori algorithm Apriori (Unknown reliability, py3 not supported), Pyfim (Reliability unknown, PIP installation not available) Arules::apriori Fp-growth algorithm Fp-growth (Unknown reliability, py3 not supported), Pyfim (Reliability u

Deep Learning Basics Series (vi) | Selection of weight initialization

function, |a|>1, it means that the curve is getting smoother, Z-values tend to be closer to 1 or 0, which can also cause gradients to disappear.What if we can give a suitable value to W when we initialize the weights in each layer of the network, can we reduce the possibility of this gradient explosion or gradient disappearing? Let's see how to choose.One, random distribution weightsIn Keras, whose function is: k.random_uniform_variable (), let's tak

SciPy incorrectly installing an issue that cannot be found by the report DLL

The problem is as follows:E:\project\dl\python\keras>python keras_sample.pyUsing Theano backend.Traceback (most recent):File "keras_sample.py", line 8, From Keras.preprocessing.image import ImagedatageneratorFile "D:\Program files\python_3.5\lib\site-packages\keras\preprocessing\image.py", line 9, From scipy import NdimageFile "D:\Program files\python_3.5\lib\site-packages\scipy\ndimage\__init__.py", line 1

Course Four (convolutional neural Networks), second week (Deep convolutional models:case studies)--0.learning goals

Learning Goals Understand multiple foundational papers of convolutional neural networks Analyze the dimensionality reduction of a volume in a very deep network Understand and Implement a residual network Build a deep neural network using Keras Implement a skip-connection in your network Clone a repository from GitHub and use transfer learning Learning Goalsunderstanding of multi-basis papers in convolutional neural ne

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