Dataset Integration

This section covers the integration of the mnist dataset into Tensorleap. We'll later use this dataset with a classification model.

Dataset Script

Below is the full dataset script to be used in the integration. More information about the structure of this script can be found under Dataset Script.

from typing import List

import numpy as np
from sklearn.model_selection import train_test_split
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical

# Tensorleap imports
from code_loader import leap_binder
from code_loader.contract.datasetclasses import PreprocessResponse
from code_loader.contract.enums import Metric, DatasetMetadataType

# Preprocess Function
def preprocess_func() -> List[PreprocessResponse]:
    (data_X, data_Y), (test_X, test_Y) = mnist.load_data()

    data_X = np.expand_dims(data_X, axis=-1)  # Reshape :,28,28 -> :,28,28,1
    data_X = data_X / 255                     # Normalize to [0,1]
    data_Y = to_categorical(data_Y)           # Hot Vector
    test_X = np.expand_dims(test_X, axis=-1)  # Reshape :,28,28 -> :,28,28,1
    test_X = test_X / 255                     # Normalize to [0,1]
    test_Y = to_categorical(test_Y)           # Hot Vector

    train_X, val_X, train_Y, val_Y = train_test_split(data_X, data_Y, test_size=0.2, random_state=42)

    # Generate a PreprocessResponse for each data slice, to later be read by the encoders.
    # The length of each data slice is provided, along with the data dictionary.
    # In this example we pass `images` and `labels` that later are encoded into the inputs and outputs 
    train = PreprocessResponse(length=len(train_X), data={'images': train_X, 'labels': train_Y})
    val = PreprocessResponse(length=len(val_X), data={'images': val_X, 'labels': val_Y})
    test = PreprocessResponse(length=len(test_X), data={'images': test_X, 'labels': test_Y})

    response = [train, val, test]
    return response

# Input encoder fetches the image with the index `idx` from the `images` array set in
# the PreprocessResponse data. Returns a numpy array containing the sample's image. 
def input_encoder(idx: int, preprocess: PreprocessResponse) -> np.ndarray:

# Ground truth encoder fetches the label with the index `idx` from the `labels` array set in
# the PreprocessResponse's data. Returns a numpy array containing a hot vector label correlated with the sample.
def gt_encoder(idx: int, preprocess: PreprocessResponse) -> np.ndarray:

# Metadata functions allow to add extra data for a later use in analysis.
# This metadata adds the int digit of each sample (not a hot vector).
def metadata_label(idx: int, preprocess: PreprocessResponse) -> int:
    one_hot_digit = gt_encoder(idx, preprocess)
    digit = one_hot_digit.argmax()
    digit_int = int(digit)
    return digit_int

LABELS = ['0','1','2','3','4','5','6','7','8','9']
# Dataset binding functions to bind the functions above to the `Dataset Instance`.
leap_binder.set_input(function=input_encoder, name='image')
leap_binder.set_ground_truth(function=gt_encoder, name='classes')
leap_binder.set_metadata(function=metadata_label,, name='label')
leap_binder.add_prediction(name='classes', labels=LABELS)

For more information, see Binding Functions.

Add a Dataset Instance

Add a Dataset Instance Using the UI

To add a new Dataset Instance:

  1. In the Dataset Editor, enter these properties:

    • Dataset Name: mnist

    • Script: copy and paste the script from the Dataset Script above

  2. Click Save.

After saving the mnist dataset, the platform will automatically parse the dataset script. This process evaluates the script and ensures that all its functions, including the ability to successfully read the data, are working as expected.

Upon successful parsing, the details of the MNIST dataset will be displayed on the right. In case of unsuccessful parsing, errors will be shown instead.

Up Next - Model Integration

The purpose of this section was to help you define a dataset script and create a dataset instance in Tensorleap.

Now that the mnist dataset has been integrated into Tensorleap, we can use it with a classification model. That's what we'll do in the next section, where we'll build a classification model.

When ready, move on to Model Integration.

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