Monitor_ML_runs_live.py

import neptune
from tensorflow import keras

run = neptune.init_run(project="common/quickstarts", api_token=neptune.ANONYMOUS_API_TOKEN)

params = {
    "epoch_nr": 10,
    "batch_size": 256,
    "lr": 0.005,
    "momentum": 0.4,
    "use_nesterov": True,
    "unit_nr": 256,
    "dropout": 0.05,
}

mnist = keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

model = keras.models.Sequential(
    [
        keras.layers.Flatten(),
        keras.layers.Dense(params["unit_nr"], activation=keras.activations.relu),
        keras.layers.Dropout(params["dropout"]),
        keras.layers.Dense(10, activation=keras.activations.softmax),
    ]
)

optimizer = keras.optimizers.SGD(
    learning_rate=params["lr"],
    momentum=params["momentum"],
    nesterov=params["use_nesterov"],
)

model.compile(optimizer=optimizer, loss="sparse_categorical_crossentropy", metrics=["accuracy"])


# log metrics during training
class NeptuneLogger(keras.callbacks.Callback):
    def on_batch_end(self, batch, logs=None):
        if logs is None:
            logs = {}
        for log_name, log_value in logs.items():
            run[f"batch/{log_name}"].append(log_value)

    def on_epoch_end(self, epoch, logs=None):
        if logs is None:
            logs = {}
        for log_name, log_value in logs.items():
            run[f"epoch/{log_name}"].append(log_value)


model.fit(
    x_train,
    y_train,
    epochs=params["epoch_nr"],
    batch_size=params["batch_size"],
    callbacks=[NeptuneLogger()],
)