No project description provided
Project description
DFFML Models for Tensorflow Library
About
DFFML models backed by Tensorflow.
Demo
This demo was taken before the below steps were updated, the actual accuracy may vary as this video shows accuracy being assessed against the training data. You should try it for yourself and see!
Install
virtualenv -p python3.7 .venv
. .venv/bin/activate
python3.7 -m pip install --user -U dffml[tensorflow]
Usage
wget http://download.tensorflow.org/data/iris_training.csv
wget http://download.tensorflow.org/data/iris_test.csv
head iris_training.csv
sed -i 's/.*setosa,versicolor,virginica/SepalLength,SepalWidth,PetalLength,PetalWidth,classification/g' *.csv
head iris_training.csv
dffml train \
-model dnn \
-sources csv=iris_training.csv \
-classifications 0 1 2 \
-features \
def:SepalLength:float:1 \
def:SepalWidth:float:1 \
def:PetalLength:float:1 \
def:PetalWidth:float:1 \
-num_epochs 3000 \
-steps 20000 \
-log debug
dffml accuracy \
-model dnn \
-sources csv=iris_test.csv \
-classifications 0 1 2 \
-features \
def:SepalLength:float:1 \
def:SepalWidth:float:1 \
def:PetalLength:float:1 \
def:PetalWidth:float:1 \
-log critical
dffml predict all \
-model dnn \
-sources csv=iris_test.csv \
-classifications 0 1 2 \
-features \
def:SepalLength:float:1 \
def:SepalWidth:float:1 \
def:PetalLength:float:1 \
def:PetalWidth:float:1 \
-caching \
-log critical \
> results.json
head -n 33 results.json
License
DFFML Tensorflow Models are distributed under the terms of the MIT License.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file dffml-model-tensorflow-0.1.3.tar.gz
.
File metadata
- Download URL: dffml-model-tensorflow-0.1.3.tar.gz
- Upload date:
- Size: 6.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.1
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | a36efdaa5bede9afa176ce354ce8332217d257859427485a7737233cfb38e5f4 |
|
MD5 | baef6e9e407b630c246adaa461055e18 |
|
BLAKE2b-256 | ffa5787caf732c051b58004142154a66fd5bdbd706f1cf0c8003322dab6dc401 |