002_m1_deep_learning

m1_deep_learning.py

import tensorflow as tf
from tensorflow.keras import datasets, layers, models

(train_images, train_labels), (test_images, test_labels) = datasets.fashion_mnist.load_data()
train_images, test_images = train_images / 255.0, test_images / 255.0

# ONLY ON THE MAC
# from tensorflow.python.compiler.mlcompute import mlcompute
# mlcompute.set_mlc_device(device_name='gpu')

model = models.Sequential([
    layers.Flatten(input_shape=(28, 28)),
    layers.Dense(128, activation='relu'),
    layers.Dense(64, activation='relu'),
    layers.Dense(10)
])
model.compile(
    optimizer='adam',
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=['accuracy']
)
history = model.fit(
    train_images, 
    train_labels, 
    epochs=10, 
    validation_data=(test_images, test_labels)
)