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##Tools to Design or Visualize Architecture of Neural Network
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# Tools to Design or Visualize Architecture of Neural Network
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1.[**Net2Vis**](https://viscom.net2vis.uni-ulm.de/OG1Br2BAkYSwwrV6CADl4X5EfErFjUzvuUwXWDdLbdsIXNhb9L): Net2Vis automatically generates abstract visualizations for convolutional neural networks from Keras code.
Net2Vis automatically generates abstract visualizations for convolutional neural networks from Keras code.
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2.[**visualkeras**](https://github.com/paulgavrikov/visualkeras/) : Visualkeras is a Python package to help visualize Keras (either standalone or included in tensorflow) neural network architectures. It allows easy styling to fit most needs. As of now it supports layered style architecture generation which is great for CNNs (Convolutional Neural Networks) and a grap style architecture.
Visualkeras is a Python package to help visualize Keras (either standalone or included in tensorflow) neural network architectures. It allows easy styling to fit most needs. As of now it supports layered style architecture generation which is great for CNNs (Convolutional Neural Networks) and a grap style architecture.
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```python
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import visualkeras
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3.[**draw_convnet**](https://github.com/gwding/draw_convnet) : Python script for illustrating Convolutional Neural Network (ConvNet)
5.**[PlotNeuralNet](https://github.com/HarisIqbal88/PlotNeuralNet)** : Latex code for drawing neural networks for reports and presentation. Have a look into examples to see how they are made. Additionally, lets consolidate any improvements that you make and fix any bugs to help more people with this code.
Latex code for drawing neural networks for reports and presentation. Have a look into examples to see how they are made. Additionally, lets consolidate any improvements that you make and fix any bugs to help more people with this code.
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6.**[Tensorboard](https://www.tensorflow.org/tensorboard/graphs)** - TensorBoard’s **Graphs dashboard** is a powerful tool for examining your TensorFlow model.
7.**[Caffe](https://github.com/BVLC/caffe/blob/master/python/caffe/draw.py)** - In Caffe you can use [caffe/draw.py](https://github.com/BVLC/caffe/blob/master/python/caffe/draw.py) to draw the NetParameter protobuffer:
9.**[keras-sequential-ascii](https://github.com/stared/keras-sequential-ascii/)** - A library for [Keras](https://keras.io/) for investigating architectures and parameters of sequential models.
13.**[Keras Visualization](https://keras.io/visualization/)** - The [keras.utils.vis_utils module](https://keras.io/visualization/) provides utility functions to plot a Keras model (using graphviz)
14.**[Conx](https://conx.readthedocs.io/en/latest/index.html)** - The Python package `conx` can visualize networks with activations with the function `net.picture()` to produce SVG, PNG, or PIL Images like this:
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The [keras.utils.vis_utils module](https://keras.io/visualization/) provides utility functions to plot a Keras model (using graphviz)
15.**[ENNUI](https://math.mit.edu/ennui/)** - Working on a drag-and-drop neural network visualizer (and more). Here's an example of a visualization for a LeNet-like architecture.
17.**[GraphCore](https://www.graphcore.ai/posts/what-does-machine-learning-look-like)** - These approaches are more oriented towards visualizing neural network operation, however, NN architecture is also somewhat visible on the resulting diagrams.
These approaches are more oriented towards visualizing neural network operation, however, NN architecture is also somewhat visible on the resulting diagrams.
Neataptic offers flexible neural networks; neurons and synapses can be removed with a single line of code. No fixed architecture is required for neural networks to function at all. This flexibility allows networks to be shaped for your dataset through neuro-evolution, which is done using multiple threads.
19.**[TensorSpace](https://tensorspace.org/)** : TensorSpace is a neural network 3D visualization framework built by TensorFlow.js, Three.js and Tween.js. TensorSpace provides Layer APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. By applying TensorSpace API, it is more intuitive to visualize and understand any pre-trained models built by TensorFlow, Keras, TensorFlow.js, etc.
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Neataptic offers flexible neural networks; neurons and synapses can be removed with a single line of code. No fixed architecture is required for neural networks to function at all. This flexibility allows networks to be shaped for your dataset through neuro-evolution, which is done using multiple threads.
TensorSpace is a neural network 3D visualization framework built by TensorFlow.js, Three.js and Tween.js. TensorSpace provides Layer APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. By applying TensorSpace API, it is more intuitive to visualize and understand any pre-trained models built by TensorFlow, Keras, TensorFlow.js, etc.
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