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base repository: fastai/courses
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Commits on Aug 2, 2017

  1. Add first iteration of cats v dogs

    @nambrot
    nambrot committed Aug 2, 2017
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    ae0681f View commit details
Showing with 193 additions and 0 deletions.
  1. +193 −0 deeplearning1/nbs/catsvdogs.ipynb
193 changes: 193 additions & 0 deletions deeplearning1/nbs/catsvdogs.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create Submission for Dogs v Cats"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10). Please switch to the gpuarray backend. You can get more information about how to switch at this URL:\n",
" https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29\n",
"\n",
"WARNING (theano.sandbox.cuda): CUDA is installed, but device gpu is not available (error: Unable to get the number of gpus available: CUDA driver version is insufficient for CUDA runtime version)\n",
"Using Theano backend.\n"
]
}
],
"source": [
"\n",
"from __future__ import division,print_function\n",
"\n",
"import os, json\n",
"from glob import glob\n",
"import numpy as np\n",
"np.set_printoptions(precision=4, linewidth=100)\n",
"from matplotlib import pyplot as plt\n",
"import utils; reload(utils)\n",
"from utils import plots\n",
"\n",
"path = \"/home/docker/fastai/data/dogscats/sample/\"\n",
"batch_size=64\n",
"training_epochs=100"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 16 images belonging to 2 classes.\n",
"Found 8 images belonging to 2 classes.\n"
]
}
],
"source": [
"import vgg16; reload(vgg16)\n",
"from vgg16 import Vgg16\n",
"vgg = Vgg16()\n",
"# Grab a few images at a time for training and validation.\n",
"# NB: They must be in subdirectories named based on their category\n",
"batches = vgg.get_batches(path+'train', batch_size=batch_size)\n",
"val_batches = vgg.get_batches(path+'valid', batch_size=batch_size*2)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING (theano.configdefaults): install mkl with `conda install mkl-service`: No module named mkl\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/1\n",
"16/16 [==============================] - 10s - loss: 1.0448 - acc: 0.5625 - val_loss: 0.4350 - val_acc: 0.7500\n"
]
}
],
"source": [
"vgg.finetune(batches)\n",
"vgg.fit(batches, val_batches, nb_epoch=training_epochs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 12500 images belonging to 1 classes.\n"
]
}
],
"source": [
"test_batches = vgg.get_batches('/home/docker/fastai/data/dogscats/test', shuffle=False, batch_size=batch_size, class_mode=None)\n",
"preds = vgg.model.predict_generator(test_batches, test_batches.nb_sample)\n",
"ids = [int(f[8:f.find('.')]) for f in test_batches.filenames]\n",
"np.savetxt(\n",
" '/home/docker/fastai/data/dogscats/predictions.csv',\n",
" np.stack([ids[:64], preds[:, 1]], axis=1),\n",
" fmt='%d,%.5f',\n",
" header='id,label')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 9.9282e-01, 3.7662e-02, 1.2637e-01, 9.1407e-01, 9.8101e-01, 3.2519e-03,\n",
" 1.4810e-01, 9.2504e-01, 9.7665e-01, 1.2542e-01, 4.2551e-01, 4.7124e-02,\n",
" 3.8424e-03, 9.5007e-05, 1.9337e-03, 9.2162e-01, 4.3270e-01, 1.0448e-01,\n",
" 2.5918e-03, 4.0333e-01, 9.9630e-01, 1.2269e-02, 1.8081e-02, 1.3105e-01,\n",
" 9.5527e-03, 9.0018e-01, 3.5744e-02, 7.2640e-02, 9.3611e-01, 9.9815e-01,\n",
" 9.9639e-01, 6.0718e-01, 8.8965e-01, 6.8264e-01, 6.1572e-02, 6.9096e-01,\n",
" 7.2765e-01, 9.9822e-01, 1.1135e-02, 1.0433e-01, 4.3059e-02, 6.4779e-01,\n",
" 6.5410e-01, 8.1530e-01, 4.7941e-01, 8.3378e-01, 9.4393e-01, 7.4797e-01,\n",
" 2.1743e-03, 9.5461e-01, 8.9325e-02, 8.4831e-01, 3.7092e-02, 4.6873e-01,\n",
" 4.6222e-01, 9.9916e-01, 9.6830e-01, 4.1478e-01, 1.4131e-01, 1.1278e-02,\n",
" 2.5148e-04, 5.2522e-02, 9.1643e-01, 3.6839e-02], dtype=float32)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}