validating fashion_mnist train - kamangir/bolt#689

image_classifier/__init__.py

@@ -1,5 +1,5 @@
 name = "image_classifier"
 
-version = "1.1.49"
+version = "1.1.51"
 
 description = "fashion-mnist + hugging-face + awesome-bash-cli"

image_classifier/__main__.py

@@ -129,7 +129,7 @@ elif args.task == "predict":
         success, test_images = file.load(f"{args.data_path}/test_images.pyndarray")
 
     if success:
-        logger.info(f"test_images: {string.pretty_size_of_matrix(test_images)}")
+        logger.info(f"test_images: {string.pretty_shape_of_matrix(test_images)}")
 
         _, test_labels = file.load(
             "{}/test_labels.pyndarray".format(args.data_path),

image_classifier/classes.py

@@ -67,8 +67,8 @@ class Image_Classifier(object):
     def predict(self, test_images, test_labels, output_path="", page_count=-1):
         logger.info(
             "image_classifier.predict({},{}){}".format(
-                string.pretty_size_of_matrix(test_images),
-                string.pretty_size_of_matrix(test_labels),
+                string.pretty_shape_of_matrix(test_images),
+                string.pretty_shape_of_matrix(test_labels),
                 "-> {}".format(output_path) if output_path else "",
             )
         )
@@ -332,7 +332,7 @@ class Image_Classifier(object):
             "train_images,train_labels,test_images,test_labels".split(","),
             [train_images, train_labels, test_images, test_labels],
         ):
-            logger.info("{}: {}".format(name, string.pretty_size_of_matrix(thing)))
+            logger.info("{}: {}".format(name, string.pretty_shape_of_matrix(thing)))
         logger.info(
             f"{len(classifier.class_names)} class(es): {', '.join(classifier.class_names)}"
         )

image_classifier/fashion_mnist/ingest.py

@@ -31,7 +31,7 @@ def ingest(output_path):
         [train_images, train_labels, test_images, test_labels],
     ):
         if file.save(os.path.join(output_path, f"{name}.pyndarray"), thing):
-            logger.info(f"ingested {name}: {string.pretty_size_of_matrix(thing)}")
+            logger.info(f"ingested {name}: {string.pretty_shape_of_matrix(thing)}")
         else:
             success = False
 

image_classifier/funcs.py

@@ -19,7 +19,7 @@ def eval(input_path, output_path):
     if success:
         logger.info(
             "groundtruth: {} - {}".format(
-                string.pretty_size_of_matrix(ground_truth),
+                string.pretty_shape_of_matrix(ground_truth),
                 ",".join([str(value) for value in ground_truth[:10]] + ["..."]),
             )
         )
@@ -29,7 +29,7 @@ def eval(input_path, output_path):
         predictions = np.argmax(predictions, axis=1).astype(np.uint8)
         logger.info(
             "predictions: {} - {}".format(
-                string.pretty_size_of_matrix(predictions),
+                string.pretty_shape_of_matrix(predictions),
                 ",".join([str(value) for value in predictions[:10]] + ["..."]),
             )
         )
@@ -129,7 +129,7 @@ def preprocess(
             error_count += 1
     logger.info(
         "tensor: {}{}".format(
-            string.pretty_size_of_matrix(tensor),
+            string.pretty_shape_of_matrix(tensor),
             " {} error(s)".format(error_count) if error_count else "",
         )
     )
@@ -164,17 +164,19 @@ def preprocess(
             )
         )
         logger.info(
-            "tensor_train: {}".format(string.pretty_size_of_matrix(tensor_train))
+            "tensor_train: {}".format(string.pretty_shape_of_matrix(tensor_train))
+        )
+        logger.info(
+            "tensor_test: {}".format(string.pretty_shape_of_matrix(tensor_test))
         )
-        logger.info("tensor_test: {}".format(string.pretty_size_of_matrix(tensor_test)))
         logger.info(
             "annotations_train: {}".format(
-                string.pretty_size_of_matrix(annotations_train)
+                string.pretty_shape_of_matrix(annotations_train)
             )
         )
         logger.info(
             "annotations_test: {}".format(
-                string.pretty_size_of_matrix(annotations_test)
+                string.pretty_shape_of_matrix(annotations_test)
             )
         )