How to use fit_generator with multiple inputs

How to Use `fit_generator` with Multiple Inputs

In deep learning, it’s often necessary to train models on multiple input data sources. This might involve combining images, text, or other features for a richer representation of the problem. Keras’s `fit_generator` function provides a powerful tool for training models with multiple inputs, but it requires some specific handling. This guide will walk you through the process.

Understanding the Challenge

The challenge arises because `fit_generator` typically expects a single input for each batch. When working with multiple inputs, you need to structure your data and model architecture to accommodate this.

Steps to Using `fit_generator` with Multiple Inputs

1. Prepare Your Data Generator

• Create a function that yields batches of data
• The function should yield a tuple of input arrays, where each element in the tuple corresponds to one of your input sources.
• The shape of each input array should match the expected input shape of your model layers.
• Optionally, yield the target labels as the last element of the tuple.

2. Define Your Model with Multiple Inputs

• Use the Keras functional API to construct a model with separate input layers for each input source.
• Connect the input layers to the appropriate model layers based on your architecture.
• Merge the outputs of the input branches using a suitable merging technique (e.g., concatenate, average, or dot product).
• Add the rest of your model layers after the merging point.
• Specify the final output layer.

3. Compile the Model

• Choose an optimizer and loss function appropriate for your task.
• Set up any additional metrics you want to track.

4. Train the Model Using `fit_generator`

• Pass your data generator as the `generator` argument to `fit_generator`.
• Specify the number of epochs, batch size, and other training parameters.
• Use `steps_per_epoch` to specify the number of batches per epoch, based on the total number of samples and batch size.

Example Code

Data Generator

def data_generator(data_path, batch_size):
    while True:
        # Read data from your sources here (e.g., image files, text files)
        image_data = ...
        text_data = ...
        labels = ...
        yield ([image_data, text_data], labels)

Model Definition

from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, concatenate
from tensorflow.keras.models import Model

# Input layers for images and text
image_input = Input(shape=(128, 128, 3))
text_input = Input(shape=(100,))

# Image processing branch
image_branch = Conv2D(32, (3, 3), activation='relu')(image_input)
image_branch = MaxPooling2D(pool_size=(2, 2))(image_branch)
image_branch = Flatten()(image_branch)

# Text processing branch
text_branch = Dense(64, activation='relu')(text_input)

# Merge the branches
merged = concatenate([image_branch, text_branch])

# Final layers
x = Dense(64, activation='relu')(merged)
output = Dense(1, activation='sigmoid')(x)

# Create the model
model = Model(inputs=[image_input, text_input], outputs=output)

# Compile the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

Training

model.fit_generator(
    generator=data_generator('data_path', batch_size=32),
    steps_per_epoch=100,
    epochs=10
)

Key Points

• Data Generators are Essential: Using generators is crucial for handling large datasets efficiently and for data augmentation.
• Model Design Matters: The way you structure your model, including the merging technique, will affect performance.
• Experimentation is Key: Experiment with different model architectures, data augmentation strategies, and hyperparameters to optimize performance.

Conclusion

Using `fit_generator` with multiple inputs is a powerful technique for building complex deep learning models that learn from diverse data sources. By following these steps, you can effectively train models that leverage the strengths of multiple input modalities, leading to better predictions and deeper insights.