Does Batch Sizes Affect the Performance of the InceptionV3 Model for Image Classification?
InceptionV3 is a popular convolutional neural network architecture that was introduced by Google in 2014. It is designed to achieve high accuracy in image classification tasks while minimizing the computational cost of the network. InceptionV3 has been shown to outperform other state-of-the-art models on various benchmarks.
In the previous article, we implemented the InceptionV3 model using TensorFlow and Keras for image classification on the CIFAR-10 dataset. The CIFAR-10 dataset is a well-known benchmark for image classification, containing 60,000 images of 10 different classes, such as airplanes, automobiles, birds, cats, deer, dogs, frogs, horses, ships, and trucks.
In our implementation, we used a batch size of 32, which means that the model processes 32 images at a time during training. The batch size is an important hyperparameter in deep learning models that determines how many samples are processed before updating the weights of the model. A larger batch size can lead to faster convergence, but it also requires more memory and may lead to overfitting. On the other hand, a smaller batch size can reduce memory requirements but may slow down convergence.
What if we try to use different batch sizes on the previous model? Will that affect the performance of the Inception V3 model for image classification?
BATCH_SIZES = [16, 32, 64]
EPOCHS = 10
for batch_size in BATCH_SIZES:
# Load the Inceptionv3 model
model = InceptionV3(weights='imagenet', include_top=False, input_shape=IMG_SIZE + (3,))The code initializes a list of batch sizes [16, 32, 64] and sets the number of epochs to 10. Then, for each batch size in the list, the InceptionV3 model is loaded with pre-trained weights from the ImageNet dataset.
The InceptionV3 model is a deep convolutional neural network with 48 layers and is designed for image classification tasks. The weights in the pre-trained model have been learned from a large dataset of 1.2 million images and can help the model learn useful representations of features in images.
The include_top argument is set to False to remove the final classification layer, which allows us to add our own classification layer to the model. This is useful if we want to use the pre-trained features to solve a different image classification task.
The input_shape argument is set to the shape of the input images, which is IMG_SIZE concatenated with a tuple of (3,) to specify the 3 color channels. This ensures that the input images are correctly formatted to be fed into the model.
By initializing the model with pre-trained weights, we can speed up the training process and achieve higher accuracy. Additionally, using a range of batch sizes allows us to explore how the model performs with different batch sizes, which can affect the speed and accuracy of the model. Overall, this code sets up the InceptionV3 model for training on the CIFAR-10 dataset with different batch sizes to evaluate performance.
# Train the model
model.fit(train_data, epochs=EPOCHS, validation_data=test_data)This line of code shows the split data then specifying the number of epochs to train for the model.
# Evaluate the model
test_loss, test_acc = model.evaluate(test_data)
print(f'Batch size: {batch_size}, Test loss: {test_loss}, Test accuracy: {test_acc}')
test_acc_results.append(test_acc)To evaluate the model’s performance, this shows that after several runs and experiments as specified accordingly with the batch sizes from 16,32 and 64. Then we can now append the output from these in accordance with the test loss and test accuracy.
Upon, training the model with batch size of 16, we can see that the Test accuracy is 87.13% as follows with the 39.47% test loss.
Then proceed to the training of the model with batch size of 32, it shows that the test accuracy is around 87.61% and the test loss is 38.29%. It’s higher than the batch size 16 performance.
Lastly, the model is set to have a batch size 64 for evaluation of the performance of the model. It shows that the test accuracy increases at 87.87% with a test loss of 37.05%
Then we can now show the visualization of the difference of the performance of the model per increase of the Batch Sizes.
# Plot the results
plt.plot(BATCH_SIZES, test_acc_results, 'bo-')
plt.xlabel('Batch size')
plt.ylabel('Test accuracy')
plt.title('Impact of batch size on test accuracy')
plt.show()
It is possible that increasing the batch size will result in higher performance for Inceptionv3, but this is dependent on the particular dataset as well as the hardware that is available.
Raising the batch size enables the model to analyze more data during each iteration, which in turn can speed up the training process and improve the model’s overall performance.
After a certain point, increasing the batch size further may not provide significant improvements, or may even decrease performance due to the increased memory requirements. In general, increasing the batch size can improve the training speed and performance up to a certain point. After that point, however, increasing the batch size further may not improve the training speed and performance.
As a result, the recommended batch size for Inceptionv3 is going to vary depending on the particular dataset, the hardware that is going to be used, and the balance that needs to be struck between training speed and performance. It is generally a good idea to experiment with different batch sizes and evaluate the performance of the model in order to identify the ideal batch size for a particular use case.
Therefore, by increasing the batch sizes, it improves the performance of the model. It does affect how the model was able to generates better results and prediction.
