Keras-mxnet – (1) Installation and Validation
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Keras is a high-level neural networks API in Python and capable of running on the top of Tensorflow, CNTK, Theano or Mxnet. It was developed with a focus on enabling fast experimentation. Bing able to go from idea to result with the least possible delay is key to doing good research. – from Keras.io
Table of Contents
1 Install Keras with Apache MXNet backend
$ pip/pip3 install h5py graphviz pydot --user
$ pip/pip3 install mxnet-mkl --user
$ pip/pip3 install keras-mxnet --user
When the Keras-mxnet and MXNet is installed, modify the ~/.keras.json and setting are:
backend: mxnet
image_data_format: channels_first
Then we validation the installation by:
$python
>>> import keras as K
Using mxnet backend
2 Train MNIST Classification Model
Using MXNet backend
Downloading data from [https://s3.amazonaws.com/img-datasets/mnist.npz](https://s3.amazonaws.com/img-datasets/mnist.npz)
... ...
60000 train samples
10000 test samples
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 512) 401920
_________________________________________________________________
dropout_1 (Dropout) (None, 512) 0
_________________________________________________________________
dense_2 (Dense) (None, 512) 262656
_________________________________________________________________
dropout_2 (Dropout) (None, 512) 0
_________________________________________________________________
dense_3 (Dense) (None, 10) 5130
=================================================================
Total params: 669,706
Trainable params: 669,706
Non-trainable params: 0
________________________________________________________________
Train on 60000 samples, validate on 10000 samples
Congratulations! You are using Keras with MXNet as backend!