Multi-Modal and Multi-Task

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!pip install autokeras

import numpy as np

import autokeras as ak

In this tutorial we are making use of the AutoModel API to show how to handle multi-modal data and multi-task.

Multi-modal data means each data instance has multiple forms of information. For example, a photo can be saved as a image. Besides the image, it may also have when and where it was taken as its attributes, which can be represented as numerical data.

What is multi-task?

Multi-task here we refer to we want to predict multiple targets with the same input features. For example, we not only want to classify an image according to its content, but we also want to regress its quality as a float number between 0 and 1.

The following diagram shows an example of multi-modal and multi-task neural network model.

graph TD id1(ImageInput) --> id3(Some Neural Network Model) id2(Input) --> id3 id3 --> id4(ClassificationHead) id3 --> id5(RegressionHead)

It has two inputs the images and the numerical input data. Each image is associated with a set of attributes in the numerical input data. From these data, we are trying to predict the classification label and the regression value at the same time.

Data Preparation

To illustrate our idea, we generate some random image and numerical data as the multi-modal data.

num_instances = 10
# Generate image data.
image_data = np.random.rand(num_instances, 32, 32, 3).astype(np.float32)
# Generate numerical data.
numerical_data = np.random.rand(num_instances, 20).astype(np.float32)

We also generate some multi-task targets for classification and regression.

# Generate regression targets.
regression_target = np.random.rand(num_instances, 1).astype(np.float32)
# Generate classification labels of five classes.
classification_target = np.random.randint(5, size=num_instances)

Build and Train the Model

Then we initialize the multi-modal and multi-task model with AutoModel. Since this is just a demo, we use small amount of max_trials and epochs.

# Initialize the multi with multiple inputs and outputs.
model = ak.AutoModel(
    inputs=[ak.ImageInput(), ak.Input()],
    outputs=[
        ak.RegressionHead(metrics=["mae"]),
        ak.ClassificationHead(
            loss="categorical_crossentropy", metrics=["accuracy"]
        ),
    ],
    overwrite=True,
    max_trials=2,
)
# Fit the model with prepared data.
model.fit(
    [image_data, numerical_data],
    [regression_target, classification_target],
    epochs=1,
    batch_size=3,
)

Validation Data

By default, AutoKeras use the last 20% of training data as validation data. As shown in the example below, you can use validation_split to specify the percentage.

model.fit(
    [image_data, numerical_data],
    [regression_target, classification_target],
    # Split the training data and use the last 15% as validation data.
    validation_split=0.15,
    epochs=1,
    batch_size=3,
)

You can also use your own validation set instead of splitting it from the training data with validation_data.

split = 5

image_val = image_data[split:]
numerical_val = numerical_data[split:]
regression_val = regression_target[split:]
classification_val = classification_target[split:]

image_data = image_data[:split]
numerical_data = numerical_data[:split]
regression_target = regression_target[:split]
classification_target = classification_target[:split]

model.fit(
    [image_data, numerical_data],
    [regression_target, classification_target],
    # Use your own validation set.
    validation_data=(
        [image_val, numerical_val],
        [regression_val, classification_val],
    ),
    epochs=1,
    batch_size=3,
)

Customized Search Space

You can customize your search space. The following figure shows the search space we want to define.

graph LR id1(ImageInput) --> id2(Normalization) id2 --> id3(Image Augmentation) id3 --> id4(Convolutional) id3 --> id5(ResNet V2) id4 --> id6(Merge) id5 --> id6 id7(Input) --> id9(DenseBlock) id6 --> id10(Merge) id9 --> id10 id10 --> id11(Classification Head) id10 --> id12(Regression Head)

input_node1 = ak.ImageInput()
output_node = ak.Normalization()(input_node1)
output_node = ak.ImageAugmentation()(output_node)
output_node1 = ak.ConvBlock()(output_node)
output_node2 = ak.ResNetBlock(version="v2")(output_node)
output_node1 = ak.Merge()([output_node1, output_node2])

input_node2 = ak.Input()
output_node2 = ak.DenseBlock()(input_node2)

output_node = ak.Merge()([output_node1, output_node2])
output_node1 = ak.ClassificationHead()(output_node)
output_node2 = ak.RegressionHead()(output_node)

auto_model = ak.AutoModel(
    inputs=[input_node1, input_node2],
    outputs=[output_node1, output_node2],
    overwrite=True,
    max_trials=2,
)

image_data = np.random.rand(num_instances, 32, 32, 3).astype(np.float32)
numerical_data = np.random.rand(num_instances, 20).astype(np.float32)
regression_target = np.random.rand(num_instances, 1).astype(np.float32)
classification_target = np.random.randint(5, size=num_instances)

auto_model.fit(
    [image_data, numerical_data],
    [classification_target, regression_target],
    batch_size=3,
    epochs=1,
)

Data Format

You can refer to the documentation of ImageInput, Input, TextInput, RegressionHead, ClassificationHead, for the format of different types of data. You can also refer to the Data Format section of the tutorials of Image Classification, Text Classification,

Reference

AutoModel, ImageInput, Input, DenseBlock, RegressionHead, ClassificationHead, CategoricalToNumerical.