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ML/TensorFlow/Basics/tutorial18-customdata-images/3_single_folder.py

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+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+import tensorflow as tf
+import pandas as pd
+from tensorflow import keras
+from tensorflow.keras import layers
+import pathlib  # pathlib is in standard library
+
+batch_size = 2
+img_height = 28
+img_width = 28
+
+directory = "data/mnist_images_only/"
+ds_train = tf.data.Dataset.list_files(str(pathlib.Path(directory + "*.jpg")))
+
+
+def process_path(file_path):
+    image = tf.io.read_file(file_path)
+    image = tf.image.decode_jpeg(image, channels=1)
+    label = tf.strings.split(file_path, "\\")
+    label = tf.strings.substr(label, pos=0, len=1)[2]
+    label = tf.strings.to_number(label, out_type=tf.int64)
+    return image, label
+
+
+ds_train = ds_train.map(process_path).batch(batch_size)
+
+model = keras.Sequential(
+    [
+        layers.Input((28, 28, 1)),
+        layers.Conv2D(16, 3, padding="same"),
+        layers.Conv2D(32, 3, padding="same"),
+        layers.MaxPooling2D(),
+        layers.Flatten(),
+        layers.Dense(10),
+    ]
+)
+
+model.compile(
+    optimizer=keras.optimizers.Adam(),
+    loss=[keras.losses.SparseCategoricalCrossentropy(from_logits=True),],
+    metrics=["accuracy"],
+)
+
+model.fit(ds_train, epochs=10, verbose=2)