This paper evaluated the performance of MLP, CNN, and RNN models through five distinct tasks. The core of the machine not only outlined the experiments but also utilized Keras (with TensorFlow as the backend) to test the CNN model on the MNIST dataset.
If you're wondering why Keras has become so popular in data science and deep learning, it's because of its strong support across major cloud platforms and deep learning frameworks. The official version of Keras currently 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 introduced Core ML and Keras v1.2. However, as of now, MXNet seems to only support Keras v1.2.2, not the latest version, v2.0.5.
Although Keras allows developers to use any of the supported backends, it is essentially a high-level API for deep learning libraries and does not provide full access to all the low-level parameter tuning features available in each framework. Therefore, if you need fine-grained control over parameters, it might be better to use the underlying framework directly instead of relying on Keras. That said, Keras remains an excellent tool for early-stage 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 that building the model was very straightforward, making it easy for beginners to grasp the overall 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’s GitHub project. The following figure shows how we initialized training 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 structure of the network. `Sequential` represents a linear stack of layers. After defining the sequential model, we can add different layers from the input to build the complete network. The architecture first uses a 2D convolutional layer (`Conv2D`) with a kernel size of 3x3 and ReLU activation. The first parameter, 32, indicates the number of filters. It also includes a MaxPooling2D layer with a pool size of (2,2), which downsamples the input. The Dropout layer randomly deactivates 25% of the neurons during training. The Dense layer is a fully connected layer, and the Flatten layer converts multi-dimensional input into a one-dimensional array, commonly used when transitioning from convolutional layers to dense layers. For more detailed code and explanations, check out the GitHub project at the heart of the machine.
Below is the specifics of the Jasmeet Bhatia assessment.
**Keras Backend Framework Performance Testing**
Keras enables developers to quickly compare the performance of different deep learning frameworks as backends. A configuration parameter in Keras determines which backend to use, allowing identical models to run on different frameworks like TensorFlow, CNTK, or Theano. For MXNet, since it only supports Keras v1.2.2, some minor code adjustments are required. While the model can be fine-tuned for better performance on each framework, Keras still offers a great way to compare the basic performance of these libraries.
Earlier comparisons focused mainly on TensorFlow and Theano as backends, but this article provides a broader comparison based on the latest versions of Keras and deep learning frameworks.
Let’s look at the testing configuration. All performance tests were conducted on an Azure NC6 VM equipped with an Nvidia Tesla K80 GPU, using the Ubuntu-based Azure DSVM image. In addition to 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 differences in dependencies among deep learning frameworks, our tests were run under 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 evaluate the performance of different deep learning frameworks, we used five different test models, as described below. 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 for MXNet were excluded due to compatibility issues, as MXNet does not support the latest Keras version and requires significant code changes. For the remaining tests, MXNet as a backend required minor adjustments due to function name changes in newer Keras versions.
**Test 1: CIFAR-10 & CNN**
Type of learning model: Convolutional Neural Network (CNN)
Dataset/Task: CIFAR-10 Small Image Dataset
Goal: Classify images into 10 categories
TensorFlow showed slightly better training speed than MXNet. In terms of accuracy and convergence, CNTK led in the first 25 epochs, but after 50 epochs, the other frameworks reached similar accuracy levels, with CNTK slightly dropping.
[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 learning 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 learning 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 performance (2.5–2.7 s/epoch), while MXNet completed training in just 1.4 s/epoch. MXNet also 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 learning model: Hierarchical Recurrent Neural Network (HRNN)
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 179 s/epoch, and Theano was significantly slower.
[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 learning model: Recurrent Neural Network (RNN)
Dataset/Task: bAbi Project (https://research.fb.com/downloads/babi/)
Objective: 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 on this task.
[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 not as well in RNN tests. CNTK excelled in RNN tasks like Babi and MNIST, but lagged behind TensorFlow in CNN testing. MXNet showed great potential in RNN performance and outperformed others in MLP tests, though it lacks support for Keras v2, requiring code modifications. Theano performed well in deep neural networks (MLP).
**Conclusion**
As shown by the results, each deep learning framework has its 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 needs full Keras support. These frameworks continue to evolve, improving performance and deployment ease. When choosing a framework for production, performance, ease of deployment, and supporting tools are all important factors. Although performance is measured through Keras, this article provides a helpful overview of the performance of these frameworks.
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