validating train - kamangir/bolt#689

image_classifier/__init__.py

@@ -1,5 +1,5 @@
 name = "image_classifier"
 
-version = "1.1.43"
+version = "1.1.44"
 
 description = "fashion-mnist + hugging-face + awesome-bash-cli"

image_classifier/__main__.py

@@ -1,26 +1,8 @@
 import argparse
-import cv2
-from functools import reduce
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-import os.path
-import tensorflow as tf
-from tqdm import *
-import re
-import time
 from . import *
-from abcli import objects
-from abcli import cache
+from .classes import Image_Classifier
+from .funcs import *
 from abcli import file
-from abcli.tasks import host
-from abcli import graphics
-from abcli.options import Options
-from abcli import path
-from abcli.storage import instance as storage
-from abcli import string
-from abcli.plugins import tags
-
 import abcli.logging
 import logging
 
@@ -136,33 +118,18 @@ args = parser.parse_args()
 
 success = False
 if args.task == "describe":
-    image_classifier().load(args.model_path)
+    Image_Classifier().load(args.model_path)
     success = True
 elif args.task == "eval":
     success = eval(args.input_path, args.output_path)
-elif args.task == "ingest":
-    success = ingest(
-        args.include,
-        args.output_path,
-        {
-            "count": args.count,
-            "exclude": args.exclude,
-            "negative": args.negative,
-            "non_empty": args.non_empty,
-            "positive": args.positive,
-            "test_size": args.test_size,
-        },
-    )
 elif args.task == "predict":
-    classifier = image_classifier()
+    classifier = Image_Classifier()
 
     if classifier.load(args.model_path):
-        success, test_images = file.load(
-            "{}/test_images.pyndarray".format(args.data_path)
-        )
+        success, test_images = file.load(f"{args.data_path}/test_images.pyndarray")
 
     if success:
-        logger.info("test_images: {}".format(string.pretty_size_of_matrix(test_images)))
+        logger.info(f"test_images: {string.pretty_size_of_matrix(test_images)}")
 
         _, test_labels = file.load(
             "{}/test_labels.pyndarray".format(args.data_path),
@@ -172,23 +139,26 @@ elif args.task == "predict":
 
         test_images = test_images / 255.0
 
-        success = classifier.predict(test_images, test_labels, args.output_path)
+        success = classifier.predict(
+            test_images,
+            test_labels,
+            args.output_path,
+        )
 elif args.task == "preprocess":
     success = preprocess(
         args.output_path,
-        {
-            "objects": args.objects,
-            "infer_annotation": args.infer_annotation,
-            "purpose": args.purpose,
-            "window_size": args.window_size,
-        },
+        objects=args.objects,
+        infer_annotation=args.infer_annotation,
+        purpose=args.purpose,
+        window_size=args.window_size,
     )
 elif args.task == "train":
-    classifier = image_classifier()
-    success = classifier.train(
+    success = Image_Classifier.train(
         args.data_path,
         args.model_path,
-        {"color": args.color, "convnet": args.convnet, "epochs": args.epochs},
+        color=args.color,
+        convnet=args.convnet,
+        epochs=args.epochs,
     )
 else:
     logger.error(f"-{name}: {args.task}: command not found.")