tensorpack | A Neural Net Training Interface on TensorFlow , with focus | Machine Learning library
kandi X-RAY | tensorpack Summary
kandi X-RAY | tensorpack Summary
Tensorpack is a neural network training interface based on TensorFlow v1.
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Top functions reviewed by kandi - BETA
- Creates a 2D convolution .
- Creates a convolution layer .
- Basic batch normalization
- Convert weights to a layer .
- Wrap a layer .
- finalize the data structures
- Resize image .
- Collect environment information .
- Setup Keras trainer .
- Finds the full path of the library .
tensorpack Key Features
tensorpack Examples and Code Snippets
# File: imagenet_utils.py
import multiprocessing
import numpy as np
import os
from abc import abstractmethod
import cv2
import tqdm
from tensorpack import tfv1 as tf
from tensorpack import ModelDesc
from tensorpack.dataflow import (
AugmentImag
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# Author: Patrick Wieschollek
import tensorflow as tf
from tensorpack import ModelDesc, argscope, enable_argscope_for_module
enable_argscope_for_module(tf.layers)
# FlowNet2 follows the convention
# -*- coding: utf-8 -*-
# File:
from tensorpack import tfv1 as tf
from tensorpack import ModelDesc
from tensorpack.models import GlobalAvgPooling, l2_regularizer, regularize_cost
from tensorpack.tfutils import optimizer
from tensorpack.tfutils.summa
Community Discussions
Trending Discussions on tensorpack
QUESTION
Got the DLC-GPU.yaml from here: https://github.com/DeepLabCut/DeepLabCut/blob/master/conda-environments/DLC-GPU.yaml
...ANSWER
Answered 2020-Sep-24 at 21:01matplotlib.animation
requires ffmpeg
for saving movies and ImageMagick
for saving animated gifs.
See https://matplotlib.org/users/installing.html#install-requirements
Install them with your system package manager:
QUESTION
I tried to do git rebase, and in the process lost a file. Not a very important file, but I'd like to understand what happened.
I wanted to merge branch 'master' into branch 'learning' using rebase command.
Here is a summary of my actions:
Did git rebase master, got a conflict, resolved it, tried to continue rebase, failed again with more conflicts, added/commited an untracked file, failed rebase again, tried rebase --skip, failed, aborted rebase, finally did git merge, which was successful.
The untracked file (test.py) I committed during rebase is now gone.
Here is the detailed record of my actions:
...ANSWER
Answered 2020-Mar-06 at 08:29First, that does not merge master
into learning
: a git rebase master
will replay learning
on top of master
.
Second, if at any point you added/committed test.py, you can find it back in the reflog. See "Query git reflog for all commits to a specific file".
QUESTION
I am trying to build a Django app that would use Keras models to make recommendations. Right now I'm trying to use one custom container that would hold both Django and Keras. Here's the Dockerfile I've written.
...ANSWER
Answered 2019-Jan-02 at 22:56It looks like tensorflow only publishes wheels (and only up to 3.6), and Alpine linux is not manylinux1
-compatible due to its use of musl
instead of glibc
. Because of this, pip
cannot find a suitable installation candidate and fails. Your best options are probably to build from source or change your base image.
QUESTION
importing logger of tensorpack gives following error:
...ANSWER
Answered 2018-Jan-24 at 12:10there is lines in the source tensorpack/tensorpack/dataflow/parallel.
although I dont know what's the purpose of those lines, but I know I missed the pyarrow
package, pip install pyarrow
solved the problem
QUESTION
I've a question regarding the training performance of Keras vs other TF Wrappers like Tensorpack.
During a Q&A session, the author of Keras stated that the package comes with no performance cost:
Keras provides a simpler, quicker way to build and train models in TensorFlow, at no performance cost since the models are still being run by the same TensorFlow engine.
On the other hand though, the Tensorpack github claims that it's 2 times faster than Keras, even with a tiny CNN:
Tensorpack trainer is almost always faster than feed_dict based wrappers. Even on a tiny CNN example, the training runs 2x faster than the equivalent Keras code.
So now I wonder the following: Who is right? A factor of two would be very significant for me in training.
...ANSWER
Answered 2018-Mar-05 at 07:55I'm the author of tensorpack. The reasons for major performance improvements are summarized in the Input Pipeline page in the documentation: http://tensorpack.readthedocs.io/en/latest/tutorial/input-source.html These arguments are for single GPU training case. For multi GPU training we have more performance improvements to match Google's benchmark code.
The answer by @jdehesa is not right, we never use TFRecord to read data (although it is capable of doing so, I don't see much advantage). You can read everything by pure Python and get this speed.
Also, the 2x speedup might be a bit outdated, I know Keras has been making improvements on reducing framework overhead.
QUESTION
What is the way to schedule hyper-parameters in TensorFlow?
Namely, for the sake of reproducibility I would like to implement a ResNet (you name one) using suggested learning rate schedule {0: 0.1, 1: 1., 100: 0.01, 150: 0.001}, or enable the weight decay only after first few initial epoch.
For example, tensorpack provides an optionas follows:
...ANSWER
Answered 2017-Dec-20 at 01:10Ok, it wasn't that hard
QUESTION
I'm trying to install tensorpack on Ubuntu 16.04 LTS.
...ANSWER
Answered 2017-Apr-03 at 12:04Tensorpack worked for me, but the per-requisite were
Using a Virtual Environment
virtualenv tensorpack
Secondly updating pip
pip install --upgrade pip
Lastly not using "sudo"
pip install tensorpack
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install tensorpack
You can use tensorpack like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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