+basic image recognition

Python/ai/basic_image_classification/is-black.py

@@ -0,0 +1,48 @@
+#!/bin/python3
+from sys import argv
+from tensorflow import keras
+
+my_activation = ( # declared like this to ease commenting/uncommenting
+	#'sigmoid'
+	# performs like absolute trash
+	# requires ~x4 more epochs than relu
+
+	#'relu'
+	# has the tendency to produce such probabilities:
+	#  white.png - 0.00% black : 100.00% white 
+	#  black.png - 51.10% black : 48.90% white
+	# requires roughly 50 epochs and slight luck
+
+	'tanh'
+	# easily adjusts under 10 epochs
+	# produces reasonable divided probabilites
+)
+
+HEIGHT, WIDTH = 20, 20
+
+dataset = keras.utils.image_dataset_from_directory(
+	"dataset/",
+	image_size=(HEIGHT, WIDTH),
+)
+
+model = keras.Sequential([
+    keras.layers.Flatten(),
+    keras.layers.Dense(8, activation=my_activation),
+    keras.layers.Dense(8, activation=my_activation),
+    keras.layers.Dense(1, activation='sigmoid')
+])
+
+model.compile(
+	'adam',
+    loss='binary_crossentropy',
+    metrics=['accuracy']
+)
+
+model.fit(dataset, epochs=10)
+
+img = keras.preprocessing.image.load_img(argv[1], target_size=(HEIGHT, WIDTH))
+img = keras.utils.img_to_array(img)
+img = keras.ops.expand_dims(img, 0)
+
+score = model.predict(img)[0][0]
+print(f"{100 * (1 - score):.2f}% black : {100 * score:.2f}% white")

Python/ai/basic_image_classification/mkdata.sh

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+#!/bin/bash
+mkdir dataset
+mkdir dataset/white
+mkdir dataset/black
+convert -size 100x100 xc:white dataset/white/white.png
+convert -size 100x100 xc:black dataset/black/black.png