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ML/TensorFlow/Basics/tutorial18-customdata-images/2_csv_file.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
+
+directory = "data/mnist_images_csv/"
+df = pd.read_csv(directory + "train.csv")
+
+file_paths = df["file_name"].values
+labels = df["label"].values
+ds_train = tf.data.Dataset.from_tensor_slices((file_paths, labels))
+
+
+def read_image(image_file, label):
+    image = tf.io.read_file(directory + image_file)
+    image = tf.image.decode_image(image, channels=1, dtype=tf.float32)
+    return image, label
+
+
+def augment(image, label):
+    # data augmentation here
+    return image, label
+
+
+ds_train = ds_train.map(read_image).map(augment).batch(2)
+
+for epoch in range(10):
+    for x, y in ds_train:
+        # train here
+        pass
+
+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)