A Clojure library wrap deep java library(DJL.ai)
clj-djl is available as a Maven artifact from Clojars.
With Leiningen/Boot:
[clj-djl "0.1.6"]With Clojure CLI/deps.edn:
clj-djl/clj-djl {:mvn/version "0.1.6"}Latest version please refer to:
To list all available engine:
git clone https://github.com/kimim/clj-djl
cd clj-djl
lein runlein testdeps.edn (clj cli)
{:deps
{clj-djl/clj-djl {:mvn/version "0.1.6"}
clj-djl/dataframe {:mvn/version "0.1.2"}}}project.clj (for leiningen)
(defproject clj-d2l/house-prices "0.0.1"
:dependencies [[clj-djl "0.1.6"]
[clj-djl/dataframe "0.1.2"]]
:source-paths ["src"])house_prices.clj:
(ns clj-d2l.house-prices
(:require
[clj-djl.ndarray :as nd]
[clj-djl.model :as m]
[clj-djl.nn :as nn]
[clj-djl.training :as t]
[clj-djl.training.dataset :as ds]
[clj-djl.training.loss :as loss]
[clj-djl.training.optimizer :as optimizer]
[clj-djl.training.tracker :as tracker]
[clj-djl.training.listener :as listener]
[clj-djl.nn.parameter :as param]
[clj-djl.dataframe :as df]
[clj-djl.dataframe.column-filters :as cf]
[clj-djl.dataframe.functional :as dfn]))
(def train-ds-url
"http://d2l-data.s3-accelerate.amazonaws.com/kaggle_house_pred_train.csv")
(def test-ds-url
"http://d2l-data.s3-accelerate.amazonaws.com/kaggle_house_pred_test.csv")
(def train-data (df/->dataframe train-ds-url))
(def test-data (df/->dataframe test-ds-url))
(df/select-by-index train-data (range 4) [0 1 2 -2 -1])
;; http://d2l-data.s3-accelerate.amazonaws.com/kaggle_house_pred_train.csv [4 5]:
;; | Id | MSSubClass | MSZoning | SaleCondition | SalePrice |
;; |----|------------|----------|---------------|-----------|
;; | 1 | 60 | RL | Normal | 208500 |
;; | 2 | 20 | RL | Normal | 181500 |
;; | 3 | 60 | RL | Normal | 223500 |
;; | 4 | 70 | RL | Abnorml | 140000 |
(def all-features (-> train-data
(df/drop-columns ["SalePrice"])
(df/concat test-data)
(df/drop-columns ["Id"])
(df/replace-missing cf/numeric 0)
(df/replace-missing cf/categorical "None")
(df/update-columns cf/numeric
#(dfn// (dfn/- % (dfn/mean %))
(dfn/standard-deviation %)))
(df/categorical->one-hot cf/categorical)))
(def n-train (df/row-count train-data))
(def n-test (df/row-count test-data))
(def train-features (df/head all-features n-train))
(def test-features (df/tail all-features n-test))
(def train-labels (-> train-data
(df/select-columns ["SalePrice"])
(df/update-columns cf/numeric
#(dfn// % (dfn/mean %)))))
(def ndm (nd/new-base-manager))
(def train-nd (df/->ndarray ndm train-features))
(def test-nd (df/->ndarray ndm test-features))
(def label-nd (df/->ndarray ndm train-labels))
(defn do-train [nepochs learning-rate weight-decay batchsize]
(let [train-dataset
(ds/array-dataset {:data (nd/to-type train-nd :float32 false)
:labels (nd/to-type label-nd :float32 false)
:batchsize batchsize
:shuffle false})
net (nn/sequential {:blocks (nn/linear {:units 1})
:initializer (nn/normal-initializer)
:parameter param/weight})
cfg (t/training-config {:loss (loss/l2-loss)
:optimizer (optimizer/sgd
{:tracker (tracker/fixed learning-rate)})
:evaluator (t/accuracy)
:listeners (listener/logging)})]
(with-open [model (m/model {:name "mlp" :block net})
trainer (t/trainer model cfg)]
(t/initialize trainer (nd/shape 1 310))
(t/set-metrics trainer (t/metrics))
(t/fit trainer nepochs train-dataset)
(t/get-result trainer))))
(do-train 5 0.05 0 64)
;; => {:epochs 5,
;; :train-accuracy 0.87876713,
;; :train-loss 0.017618015,
;; :validate-accuracy ##NaN,
;; :validate-loss ##NaN}
Copyright © 2020 kimim
This project is licensed under the Apache-2.0 License.