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Last active Jan 14, 2020
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libpython-clj-mxnet
(ns gigasquid.mxnet
(:require [libpython-clj.require :refer [require-python]]
[libpython-clj.python :as py]
[clojure.string :as string]))
(require-python '(mxnet mxnet.ndarray mxnet.module mxnet.io))
(require-python '(mxnet.test_utils))
(require-python '(mxnet.initializer))
(require-python '(mxnet.metric))
(require-python '(mxnet.symbol))
;;; get the mnist data and format it
(def mnist (mxnet.test_utils/get_mnist))
(def train-x (mxnet.ndarray/array (py/$a (py/get-item mnist "train_data") "reshape" -1 784)))
(def train-y (mxnet.ndarray/array (py/get-item mnist "train_label")))
(def test-x (mxnet.ndarray/array (py/$a (py/get-item mnist "test_data") "reshape" -1 784)))
(def test-y (mxnet.ndarray/array (py/get-item mnist "test_label")))
(def batch-size 100)
(def train-dataset (mxnet.io/NDArrayIter :data train-x
:label train-y
:batch_size batch-size
:shuffle true))
(def test-dataset (mxnet.io/NDArrayIter :data test-x
:label test-y
:batch_size batch-size))
(def data-shapes (py/get-attr train-dataset "provide_data"))
(def label-shapes (py/get-attr train-dataset "provide_label"))
data-shapes ;=> [DataDesc[data,(10, 784),<class 'numpy.float32'>,NCHW]]
label-shapes ;=> [DataDesc[softmax_label,(10,),<class 'numpy.float32'>,NCHW]]
;;;; Setting up the model and initializing it
(def data (mxnet.symbol/Variable "data"))
(def net (-> (mxnet.symbol/Variable "data")
(mxnet.symbol/FullyConnected :name "fc1" :num_hidden 128)
(mxnet.symbol/Activation :name "relu1" :act_type "relu")
(mxnet.symbol/FullyConnected :name "fc2" :num_hidden 64)
(mxnet.symbol/Activation :name "relu2" :act_type "relu")
(mxnet.symbol/FullyConnected :name "fc3" :num_hidden 10)
(mxnet.symbol/SoftmaxOutput :name "softmax")))
(def model (py/call-kw mxnet.module/Module [] {:symbol net :context (mxnet/cpu)}))
(py/$a model bind :data_shapes data-shapes :label_shapes label-shapes)
(py/$a model init_params)
(py/$a model init_optimizer :optimizer "adam")
(def acc-metric (mxnet.metric/Accuracy))
(defn end-of-data-error? [e]
(string/includes? (.getMessage e) "StopIteration"))
(defn reset [iter]
(py/$a iter reset))
(defn next-batch [iter]
(try (py/$a iter next)
(catch Exception e
(when-not (end-of-data-error? e)
(throw e)))))
(defn get-metric [metric]
(py/$a metric get))
(defn train-epoch [model dataset metric]
(reset dataset)
(loop [batch (next-batch dataset)
i 0]
(if batch
(do
(py/$a model forward batch :is_train true)
(py/$a model backward)
(py/$a model update)
(py/$a model update_metric metric (py/get-attr batch "label"))
(when (zero? (mod i 100)) (println "i-" i " Training Accuracy " (py/$a metric get)))
(recur (next-batch dataset) (inc i)))
(println "Final Training Accuracy " (get-metric metric)))))
(defn test-accuracy [model dataset metric]
(reset dataset)
(loop [batch (next-batch dataset)
i 0]
(if batch
(do
(py/$a model forward batch :is_train true)
(py/$a model update_metric metric (py/get-attr batch "label"))
(when (zero? (mod i 100)) (println "i-" i " Test Accuracy " (py/$a metric get)))
(recur (next-batch dataset) (inc i)))
(println "Final Test Accuracy " (get-metric metric)))))
(comment
;;;training
(dotimes [i 3]
(println "========= Epoch " i " ============")
(train-epoch model train-dataset acc-metric))
(get-metric acc-metric) ;=> ('accuracy', 0.9483555555555555)
;;;; test
(test-accuracy model test-dataset acc-metric)
(get-metric acc-metric) ;=> ('accuracy', 0.9492052631578948)
)
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