blob: 6d9138027bf79befc1c27a3c061759b7fb4598b5 [file] [log] [blame]
<!--
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
-->
# Train a RBM model against MNIST dataset
This example is to train an RBM model using the
MNIST dataset. The RBM model and its hyper-parameters are set following
[Hinton's paper](http://www.cs.toronto.edu/~hinton/science.pdf)
## Running instructions
1. Download the pre-processed [MNIST dataset](https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz)
2. Start the training
python train.py mnist.pkl.gz
By default the training code would run on CPU. To run it on a GPU card, please start
the program with an additional argument
python train.py mnist.pkl.gz --use_gpu