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ML/TensorFlow/Basics/tutorial3-neuralnetwork.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
+
+physical_devices = tf.config.list_physical_devices("GPU")
+tf.config.experimental.set_memory_growth(physical_devices[0], True)
+
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+x_train = x_train.reshape(-1, 28 * 28).astype("float32") / 255.0
+x_test = x_test.reshape(-1, 28 * 28).astype("float32") / 255.0
+
+# Sequential API (Very convenient, not very flexible)
+model = keras.Sequential(
+    [
+        keras.Input(shape=(28 * 28)),
+        layers.Dense(512, activation="relu"),
+        layers.Dense(256, activation="relu"),
+        layers.Dense(10),
+    ]
+)
+
+model = keras.Sequential()
+model.add(keras.Input(shape=(784)))
+model.add(layers.Dense(512, activation="relu"))
+model.add(layers.Dense(256, activation="relu", name="my_layer"))
+model.add(layers.Dense(10))
+
+# Functional API (A bit more flexible)
+inputs = keras.Input(shape=(784))
+x = layers.Dense(512, activation="relu", name="first_layer")(inputs)
+x = layers.Dense(256, activation="relu", name="second_layer")(x)
+outputs = layers.Dense(10, activation="softmax")(x)
+model = keras.Model(inputs=inputs, outputs=outputs)
+
+model.compile(
+    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=False),
+    optimizer=keras.optimizers.Adam(lr=0.001),
+    metrics=["accuracy"],
+)
+
+model.fit(x_train, y_train, batch_size=32, epochs=5, verbose=2)
+model.evaluate(x_test, y_test, batch_size=32, verbose=2)