This paper evaluated the performance of MLP, CNN, and RNN models through five distinct tasks. The core of the machine not only introduced the experiments but also implemented a CNN model on the MNIST dataset using Keras with TensorFlow as the backend.
Keras has gained significant popularity in the data science and deep learning communities due to its strong support from major cloud platforms and deep learning frameworks. Currently, the official version of Keras supports Google’s TensorFlow, Microsoft’s CNTK, and the University of Montreal’s Theano. Additionally, AWS announced last year that Keras would support Apache MXNet. The recent release of MXNet 0.11 added Core ML and Keras v1.2. However, it appears that MXNet still only supports Keras v1.2.2 instead of the latest version 2.0.5.
Although Keras allows developers to use any of the supported backends, it's important to understand that Keras acts as a high-level API for deep learning libraries and does not provide all the low-level parameter tuning options available in each framework. If fine-tuning is required, it may be better to use the native framework directly. However, as more tools are integrated into Keras, this limitation is expected to decrease over time. For now, Keras remains an excellent tool for the early stages of deep learning development, offering powerful capabilities for data scientists and algorithm engineers to quickly build and test complex models.
The heart of the machine also tested Keras using TensorFlow as the backend. We found the model easy to build, making it accessible even for beginners to grasp the network architecture. Compared to building a convolutional neural network directly with TensorFlow, using Keras as a high-level API significantly simplifies the process. In the future, we will upload the Keras implementation of the CNN code and comments to the heart of the machine GitHub project. The following figure shows how we initialized and trained the model using TensorFlow as the backend.
[Image: Comparative analysis of four frameworks: TensorFlow, MXNet, CNTK, Theano]
The following is the architecture of the entire convolutional network:
[Image: Comparative analysis of four frameworks: TensorFlow, MXNet, CNTK, Theano]
The above code clearly defines the layers used in the network. The Sequential model represents a linear stack of layers. After defining the model, we can add different layers from the input to build the entire network. The architecture starts with a 2D convolutional layer (Conv2D), with a kernel size of 3x3 and ReLU activation. The first parameter, 32, refers to the number of filters. The network also includes a MaxPooling2D layer with a pool size of (2,2), which reduces the spatial dimensions. A Dropout layer is used to randomly deactivate 25% of the neurons during training, and a Dense layer represents the fully connected layer. Finally, a Flatten layer is used to convert multi-dimensional inputs into a one-dimensional format, commonly used when transitioning from convolutional layers to dense layers. For more detailed code and explanations, you can check the GitHub repository of the heart of the machine.
Below is the specifics of the Jasmeet Bhatia assessment.
**Keras Backend Framework Performance Testing**
Keras enables developers to quickly evaluate the relative performance of different deep learning frameworks as backends. A configuration file in Keras determines which framework to use, allowing identical models to run on various backends such as TensorFlow, CNTK, and Theano. For MXNet, since it only supports Keras v1.2.2, some minor code adjustments are necessary. While models can be fine-tuned for better performance on specific frameworks, Keras still provides a valuable opportunity to compare the basic performance of these libraries.
Previous articles have compared the performance of Keras backends, but those comparisons were based on older versions, mainly focusing on TensorFlow and Theano. This article expands the comparison to include the latest versions of Keras and deep learning frameworks.
All performance tests were conducted on an Azure NC6 VM equipped with an Nvidia Tesla K80 GPU, running the Azure DSVM (Data Science Virtual Machine) image on Ubuntu. Alongside other data science tools, Keras, TensorFlow, Theano, and MXNet were pre-installed. All packages were updated to their latest versions, except for MXNet, which only supports Keras v1.2.2.
**Configuration**
Due to the varying dependencies among deep learning frameworks, our tests were conducted across three different configurations.
[Image: Comparative analysis of four frameworks: TensorFlow, MXNet, CNTK, Theano]
[Image: Comparative analysis of four frameworks: TensorFlow, MXNet, CNTK, Theano]
**Performance Testing**
To compare the performance of different deep learning frameworks, we used five different test models. To ensure fairness, all models were taken from the Keras/examples repository on GitHub.
Model source code: https://github.com/fchollet/keras/tree/master/examples
Test code: https://github.com/jasmeetsb/deep-learning-keras-projects
Note: Two tests involving MXNet were excluded because MXNet does not support the latest Keras version, requiring extensive code modifications. In the remaining tests, MXNet as a backend also needed some minor adjustments, mainly due to function name changes in newer Keras versions.
**Test 1: CIFAR-10 & CNN**
Type of model: Convolutional Neural Network (CNN)
Dataset/Task: CIFAR-10 Small Image Dataset
Goal: Classify images into 10 categories
TensorFlow showed slightly faster training per epoch than MXNet. In terms of accuracy and convergence speed, CNTK led in the first 25 epochs, but after 50 epochs, the other frameworks reached similar accuracy levels, while CNTK showed a slight drop.
[Image: Comparative analysis of four frameworks: TensorFlow, MXNet, CNTK, Theano]
[Image: Comparative analysis of four frameworks: TensorFlow, MXNet, CNTK, Theano]
**Test 2: MNIST & CNN**
Type of model: CNN
Dataset/Task: MNIST Handwritten Digital Dataset
Goal: Classify images into 10 types of handwritten numbers
In this test, TensorFlow had a clear advantage in training time, while all frameworks showed similar accuracy and convergence speed.
[Image: Comparative analysis of four frameworks: TensorFlow, MXNet, CNTK, Theano]
[Image: Comparative analysis of four frameworks: TensorFlow, MXNet, CNTK, Theano]
**Test 3: MNIST & MLP**
Type of model: Multilayer Perceptron / Deep Neural Network
Dataset/Task: MNIST Handwritten Digital Dataset
Goal: Classify images into 10 types of handwritten numbers
In a standard neural network test using the MNIST dataset, CNTK, TensorFlow, and Theano achieved similar results (2.5 – 2.7 s/epoch), while MXNet completed the task in just 1.4s/epoch and showed a slight edge in accuracy and convergence speed.
[Image: Comparative analysis of four frameworks: TensorFlow, MXNet, CNTK, Theano]
[Image: Comparative analysis of four frameworks: TensorFlow, MXNet, CNTK, Theano]
**Test 4: MNIST & RNN**
Type of model: Recurrent Neural Network (RNN)
Dataset/Task: MNIST Handwritten Digital Dataset
Goal: Classify images into 10 types of handwritten numbers
In terms of training time, CNTK and MXNet performed similarly (162–164 s/epoch), while TensorFlow took 179s/epoch, and Theano showed significantly higher training times.
[Image: Comparative analysis of four frameworks: TensorFlow, MXNet, CNTK, Theano]
[Image: Comparative analysis of four frameworks: TensorFlow, MXNet, CNTK, Theano]
**Test 5: BABI & RNN**
Type of model: Recurrent Neural Network (RNN)
Dataset/Task: bAbi Project (https://research.fb.com/downloads/babi/)
Goal: Train two RNNs based on stories and questions to answer a series of bAbi tasks.
MXNet was not used in this test. TensorFlow and Theano were more than twice as fast as CNTK in this scenario.
[Image: Comparative analysis of four frameworks: TensorFlow, MXNet, CNTK, Theano]
[Image: Comparative analysis of four frameworks: TensorFlow, MXNet, CNTK, Theano]
[Image: Comparative analysis of four frameworks: TensorFlow, MXNet, CNTK, Theano]
**Summary of Results**
TensorFlow performed best in CNN tests but struggled in RNN tasks. CNTK excelled in RNN tasks like Babi and MNIST but lagged in CNN testing. MXNet outperformed others in RNN tests and showed superior performance in MLP, though it lacks full support for Keras v2, leading to potential discrepancies. Theano performed well in deep neural networks (MLP).
**Conclusion**
As shown by the results, each deep learning framework has its own strengths, and no single framework is universally superior. CNTK works well as a Keras backend for RNN tasks, while TensorFlow is ideal for CNNs. MXNet shows great promise in performance but still needs full Keras compatibility. These frameworks continue to evolve, offering improved performance and easier deployment. When choosing a framework for production, performance, ease of deployment, and ancillary tools should all be considered. Although the performance metrics here are measured through Keras, they still offer valuable insights into the capabilities of these frameworks.
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