Mobile machine learning made easy with the Fritz CLI. https://fritz.ai

fritz_cli_tutorial.py

import fritz
import fritz.train

# Fritz needs to be configured first. Calling the fritz.Configure() method will
# read the credentials we setup for the CLI earlier.
fritz.configure()

# Create the callback

# Start by defining a training configuration and storing it as metadata
metadata = {
    "learning_rate": 0.001,
    "batch_size": 256
}

# Specify model ids so new versions of our model get associated with the
# original checkpoing uploads from above.

model_ids = {
    fritz.frameworks.KERAS: "<YOUR KERAS MODEL ID>",
    fritz.frameworks.CORE_ML: "<YOUR CORE ML MODEL ID>",
    fritz.frameworks.TENSORFLOW_LITE: "<YOUR TENSORFLOW LITE MODEL ID>"
}

# Specify our conversion functions so keras checkpoints
# get turned into Core ML and TFLite models each time
converters = {
    fritz.frameworks.CORE_ML: convert_to_coreml,
    fritz.frameworks.TENSORFLOW_LITE: convert_to_tflite
}

fritz_callback = fritz.train.FritzSnapshotCallback(
    model_ids_by_framework=model_ids,
    converters_by_framework=converters,
    output_file_name="mnist.h5",
    metadata=metadata,
    period=10 # Save after every 10 epochs and at the end of training
)