Merge pull request fastai#144 from andyli/travis

TravisCI for automated testing

.travis.yml

@@ -0,0 +1,51 @@
+language: python
+python: 2.7
+dist: trusty
+sudo: false
+
+env:
+  global:
+    - NBCONVERT_ARGS="--to html --execute --ExecutePreprocessor.kernel_name=python --ExecutePreprocessor.timeout=-1"
+
+install:
+  # xvfb
+  # https://docs.travis-ci.com/user/gui-and-headless-browsers/
+  - "export DISPLAY=:99.0"
+  - "sh -e /etc/init.d/xvfb start"
+  # install conda
+  # https://conda.io/docs/travis.html
+  - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
+      wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh;
+    else
+      wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
+    fi
+  - bash miniconda.sh -b -f -p $HOME/miniconda
+  - export PATH="$HOME/miniconda/bin:$PATH"
+  - hash -r
+  - conda config --set always_yes yes --set changeps1 no
+  - conda update -q conda
+  - conda config --add channels conda-forge
+  - conda info -a
+  - conda create -q --name fastai --file requirements.txt python=$TRAVIS_PYTHON_VERSION
+  - source activate fastai
+  - conda list
+
+script:
+  - mkdir -p deeplearning1/nbs/data
+  - if [ ! -d deeplearning1/nbs/data/dogscats ]; then
+    curl -sSLO "http://files.fast.ai/data/dogscats.zip" &&
+    unzip -qq -d deeplearning1/nbs/data dogscats.zip;
+    fi
+  - travis_wait jupyter nbconvert deeplearning1/nbs/lesson1.ipynb $NBCONVERT_ARGS
+  - travis_wait jupyter nbconvert deeplearning1/nbs/lesson2.ipynb $NBCONVERT_ARGS
+
+before_cache:
+  # We only cache the archives but not the extracted packages,
+  # such that the miniconda installer script can extract correctly.
+  - rm -rf $HOME/miniconda/pkgs/*/
+cache:
+  timeout: 1000
+  directories:
+    - $HOME/miniconda/pkgs
+    - $HOME/.keras/models
+    - deeplearning1/nbs/data

deeplearning1/nbs/lesson1.ipynb

@@ -70,8 +70,8 @@
    },
    "outputs": [],
    "source": [
-    "path = \"data/dogscats/\"\n",
-    "#path = \"data/dogscats/sample/\""
+    "# path = \"data/dogscats/\"\n",
+    "path = \"data/dogscats/sample/\""
    ]
   },
   {

deeplearning1/nbs/lesson2.ipynb

@@ -374,8 +374,8 @@
    },
    "outputs": [],
    "source": [
-    "#path = \"data/dogscats/sample/\"\n",
-    "path = \"data/dogscats/\"\n",
+    "path = \"data/dogscats/sample/\"\n",
+    "# path = \"data/dogscats/\"\n",
     "model_path = path + 'models/'\n",
     "if not os.path.exists(model_path): os.mkdir(model_path)"
    ]
@@ -395,8 +395,8 @@
    },
    "outputs": [],
    "source": [
-    "batch_size=100\n",
-    "#batch_size=4"
+    "# batch_size=100\n",
+    "batch_size=4"
    ]
   },
   {
@@ -1160,7 +1160,10 @@
     "#3. The images we were most confident were cats, but are actually dogs\n",
     "incorrect_cats = np.where((preds==0) & (preds!=val_labels[:,1]))[0]\n",
     "most_incorrect_cats = np.argsort(probs[incorrect_cats])[::-1][:n_view]\n",
-    "plots_idx(incorrect_cats[most_incorrect_cats], probs[incorrect_cats][most_incorrect_cats])"
+    "if len(most_incorrect_cats):\n",
+    "    plots_idx(incorrect_cats[most_incorrect_cats], probs[incorrect_cats][most_incorrect_cats])\n",
+    "else:\n",
+    "    print('No incorrect cats!')"
    ]
   },
   {
@@ -1187,7 +1190,10 @@
     "#3. The images we were most confident were dogs, but are actually cats\n",
     "incorrect_dogs = np.where((preds==1) & (preds!=val_labels[:,1]))[0]\n",
     "most_incorrect_dogs = np.argsort(probs[incorrect_dogs])[:n_view]\n",
-    "plots_idx(incorrect_dogs[most_incorrect_dogs], 1-probs[incorrect_dogs][most_incorrect_dogs])"
+    "if len(most_incorrect_dogs):\n",
+    "    plots_idx(incorrect_dogs[most_incorrect_dogs], 1-probs[incorrect_dogs][most_incorrect_dogs])\n",
+    "else:\n",
+    "    print('No incorrect dogs!')"
    ]
   },
   {
@@ -1519,8 +1525,8 @@
    "outputs": [],
    "source": [
     "def fit_model(model, batches, val_batches, nb_epoch=1):\n",
-    "    model.fit_generator(batches, samples_per_epoch=batches.N, nb_epoch=nb_epoch, \n",
-    "                        validation_data=val_batches, nb_val_samples=val_batches.N)"
+    "    model.fit_generator(batches, samples_per_epoch=batches.n, nb_epoch=nb_epoch, \n",
+    "                        validation_data=val_batches, nb_val_samples=val_batches.n)"
    ]
   },
   {
@@ -1958,7 +1964,7 @@
    "outputs": [],
    "source": [
     "model.load_weights(model_path+'finetune2.h5')\n",
-    "model.evaluate_generator(get_batches(path+'valid', gen, False, batch_size*2), val_batches.N)"
+    "model.evaluate_generator(get_batches(path+'valid', gen, False, batch_size*2), val_batches.n)"
    ]
   },
   {

requirements.txt

@@ -0,0 +1,10 @@
+jupyter
+matplotlib
+pillow
+keras==1.2.2
+theano
+tensorflow
+pandas
+scikit-learn
+bcolz
+sympy