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Adding instructions for getting CIFAR data.

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Matt Graham 2017-01-24 16:58:58 +00:00
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notebooks/09a_Object_recognition_with_CIFAR-10_and_CIFAR-100.ipynb
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@ -128,7 +128,16 @@
"\n",
"Both data provider classes provide a `label_map` attribute which is a list of strings which are the class labels corresponding to the integer targets (i.e. prior to conversion to a 1-of-K encoded binary vector).\n",
"\n",
"Below example code is given for creating instances of the CIFAR-10 and CIFAR-100 data provider objects and using them to train simple two-layer feedforward network models with rectified linear activations in TensorFlow. You may wish to use this code as a starting point for your own experiments."
"Below example code is given for creating instances of the CIFAR-10 and CIFAR-100 data provider objects and using them to train simple two-layer feedforward network models with rectified linear activations in TensorFlow. You may wish to use this code as a starting point for your own experiments.\n",
"\n",
"\n",
"### Accessing the CIFAR-10 and CIFAR-100 data\n",
"\n",
"Before using the data provider objects you will need to copy the associated data files in to your local `mlp/data` directory (or wherever your `MLP_DATA_DIR` environment variable points to if different). The data is available as six compressed NumPy `.npz` files, (`cifar-10-train.npz, cifar-10-valid.npz, cifar-10-test-inputs.npz` and `cifar-100-train.npz, cifar-100-valid.npz, cifar-100-test.npz`) in the AFS directory `/afs/inf.ed.ac.uk/group/teaching/mlp/data`. Assuming your local `mlpractical` repository is in your home directory you should be able to copy the required files by running\n",
"\n",
"```\n",
"cp /afs/inf.ed.ac.uk/group/teaching/mlp/data/cifar*.npz ~/mlpractical/data\n",
"```"
]
},
{