Initial commit

ML/TensorFlow/Basics/tutorial14-callbacks.py

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+import os
+import matplotlib.pyplot
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+import tensorflow_datasets as tfds
+
+physical_devices = tf.config.list_physical_devices("GPU")
+tf.config.experimental.set_memory_growth(physical_devices[0], True)
+
+(ds_train, ds_test), ds_info = tfds.load(
+    "mnist",
+    split=["train", "test"],
+    shuffle_files=True,
+    as_supervised=True,  # will return tuple (img, label) otherwise dict
+    with_info=True,  # able to get info about dataset
+)
+
+
+def normalize_img(image, label):
+    """Normalizes images"""
+    return tf.cast(image, tf.float32) / 255.0, label
+
+
+AUTOTUNE = tf.data.experimental.AUTOTUNE
+BATCH_SIZE = 128
+
+# Setup for train dataset
+ds_train = ds_train.map(normalize_img, num_parallel_calls=AUTOTUNE)
+ds_train = ds_train.cache()
+ds_train = ds_train.shuffle(ds_info.splits["train"].num_examples)
+ds_train = ds_train.batch(BATCH_SIZE)
+ds_train = ds_train.prefetch(AUTOTUNE)
+
+model = keras.Sequential(
+    [
+        keras.Input((28, 28, 1)),
+        layers.Conv2D(32, 3, activation="relu"),
+        layers.Flatten(),
+        tf.keras.layers.Dense(10, activation="softmax"),
+    ]
+)
+
+save_callback = keras.callbacks.ModelCheckpoint(
+    "checkpoint/", save_weights_only=True, monitor="train_acc", save_best_only=False,
+)
+
+lr_scheduler = keras.callbacks.ReduceLROnPlateau(
+    monitor="loss", factor=0.1, patience=3, mode="max", verbose=1
+)
+
+
+class OurOwnCallback(keras.callbacks.Callback):
+    def on_epoch_end(self, epoch, logs=None):
+        if logs.get("accuracy") > 1:
+            print("Accuracy over 70%, quitting training")
+            self.model.stop_training = True
+
+
+model.compile(
+    optimizer=keras.optimizers.Adam(0.01),
+    loss=keras.losses.SparseCategoricalCrossentropy(),
+    metrics=["accuracy"],
+)
+
+model.fit(
+    ds_train,
+    epochs=10,
+    callbacks=[save_callback, lr_scheduler, OurOwnCallback()],
+    verbose=2,
+)