Examples¶
Following are examples to train an Deep Learning model on MNIST Data to recognize digits in images using TensorFlow.
Using the Explicit Object¶
import tensorflow as tf
from codecarbon import EmissionsTracker
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential(
[
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation="relu"),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10),
]
)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
tracker = EmissionsTracker()
tracker.start()
model.fit(x_train, y_train, epochs=10)
emissions: float = tracker.stop()
print(emissions)
Using the Decorator¶
import tensorflow as tf
from codecarbon import track_emissions
@track_emissions(project_name="mnist")
def train_model():
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential(
[
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation="relu"),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10),
]
)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
model.fit(x_train, y_train, epochs=10)
return model
if __name__ == "__main__":
model = train_model()