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ML/TensorFlow/Basics/tutorial16-customloops.py

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+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.datasets import mnist
+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,
+    with_info=True,
+)
+
+
+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)
+
+# Setup for test Dataset
+ds_test = ds_train.map(normalize_img, num_parallel_calls=AUTOTUNE)
+ds_test = ds_train.batch(128)
+ds_test = ds_train.prefetch(AUTOTUNE)
+
+model = keras.Sequential(
+    [
+        keras.Input((28, 28, 1)),
+        layers.Conv2D(32, 3, activation="relu"),
+        layers.Flatten(),
+        layers.Dense(10, activation="softmax"),
+    ]
+)
+
+num_epochs = 5
+optimizer = keras.optimizers.Adam()
+loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+acc_metric = keras.metrics.SparseCategoricalAccuracy()
+
+# Training Loop
+for epoch in range(num_epochs):
+    print(f"\nStart of Training Epoch {epoch}")
+    for batch_idx, (x_batch, y_batch) in enumerate(ds_train):
+        with tf.GradientTape() as tape:
+            y_pred = model(x_batch, training=True)
+            loss = loss_fn(y_batch, y_pred)
+
+        gradients = tape.gradient(loss, model.trainable_weights)
+        optimizer.apply_gradients(zip(gradients, model.trainable_weights))
+        acc_metric.update_state(y_batch, y_pred)
+
+    train_acc = acc_metric.result()
+    print(f"Accuracy over epoch {train_acc}")
+    acc_metric.reset_states()
+
+# Test Loop
+for batch_idx, (x_batch, y_batch) in enumerate(ds_test):
+    y_pred = model(x_batch, training=True)
+    acc_metric.update_state(y_batch, y_pred)
+
+train_acc = acc_metric.result()
+print(f"Accuracy over Test Set: {train_acc}")
+acc_metric.reset_states()