tutorial3-neuralnetwork.py

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)