Merge branch 'master' of github.com:charlesreid1/lfw_fuel

* 'master' of github.com:charlesreid1/lfw_fuel:
  Fix import statement and argument name to match latest Keras API.
  Update example LFW CNN to use latest Keras API. → → Pretty sure these are not equivalent, but I had some trouble → understanding the prior conv. neural network, so I did my best → to come up with a one-to-one translation. Since it is just a → lightweight example, no biggie smalls.
  Update map() call to work with Python 2 or Python 3.

README.md

@@ -95,21 +95,28 @@ keras project.
 python setup.py install
 ```
 
-You can also rebuild the hdf5 files from scratch by running the
-`kero-download` and `kero-convert` scripts, which are currently
-part of the kerosene installation. For example:
+You can also rebuild the hdf5 files from scratch by running 
+`fuel-download` and `fuel-convert` with updated settings for
+`EXTRA_DOWNLOADERS` and `EXTRA_CONVERTERS`.
 
 ```bash
-kero-download lfw_fuel/lfw.py
-kero-convert lfw_fuel/lfw.py
+FUEL_EXTRA_DOWNLOADERS="lfw_fuel" fuel-download lfw
+FUEL_EXTRA_CONVERTERS="lfw_fuel" fuel-convert lfw
 ```
 
 This will convert the original version of lfw, but funneled and
 deepfunneled formats are also supported:
 
 ```bash
-kero-download lfw_fuel/lfw.py --format deepfunneled
-kero-convert lfw_fuel/lfw.py --format deepfunneled
+FUEL_EXTRA_DOWNLOADERS="lfw_fuel" fuel-download lfw --format deepfunneled
+FUEL_EXTRA_CONVERTERS="lfw_fuel" fuel-convert lfw --format deepfunneled
+```
+
+These settings can also be set in the `~/.fuelrc` file:
+
+```
+extra_downloaders: ['lfw_fuel']
+extra_converters: ['lfw_fuel']
 ```
 
 ## License

example/run-lfw.py

@@ -5,7 +5,7 @@ np.random.seed(1337)  # for reproducibility
 
 from keras.models import Sequential
 from keras.layers.core import Dense, Dropout, Activation, Flatten
-from keras.layers.convolutional import Convolution2D, MaxPooling2D
+from keras.layers.convolutional import Conv2D, MaxPooling2D
 from keras.utils import np_utils
 from scipy.misc import imresize 
 
@@ -37,8 +37,8 @@ def cropImage(im):
 # the data, shuffled and split between train and test sets
 (X_train, y_train), (X_test, y_test) = lfw.load_data("deepfunneled")
 # crop features
-X_train = np.asarray(map(cropImage, X_train))
-X_test = np.asarray(map(cropImage, X_test))
+X_train = np.asarray(list(map(cropImage, X_train)))
+X_test = np.asarray(list(map(cropImage, X_test)))
 
 # print shape of data while model is building
 print("{1} train samples, {2} channel{0}, {3}x{4}".format("" if X_train.shape[1] == 1 else "s", *X_train.shape))
@@ -50,24 +50,24 @@ Y_test = np_utils.to_categorical(y_test, nb_classes)
 
 model = Sequential()
 
-model.add(Convolution2D(32, 6, 3, 3, border_mode='full'))
+model.add(Conv2D(32, (3,3), input_shape=(6,32,32), padding='valid'))
 model.add(Activation('relu'))
-model.add(Convolution2D(32, 32, 3, 3))
+model.add(Conv2D(32, (3,3), input_shape=(6,32,32), padding='valid'))
 model.add(Activation('relu'))
-model.add(MaxPooling2D(poolsize=(2, 2)))
+model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Dropout(0.25))
 
 model.add(Flatten())
-model.add(Dense(feature_width*feature_height*8, 128))
+model.add(Dense(128))
 model.add(Activation('relu'))
 model.add(Dropout(0.5))
 
-model.add(Dense(128, nb_classes))
+model.add(Dense(nb_classes))
 model.add(Activation('softmax'))
 
-model.compile(loss='categorical_crossentropy', optimizer='adadelta')
+model.compile(loss='categorical_crossentropy', metrics=['binary_accuracy'], optimizer='adadelta')
 
-model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=1, validation_data=(X_test, Y_test))
-score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
+model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epoch, verbose=1, validation_data=(X_test, Y_test))
+score = model.evaluate(X_test, Y_test, verbose=0)
 print('Test score:', score[0])
 print('Test accuracy:', score[1])

lfw_fuel/__init__.py

@@ -0,0 +1,4 @@
+from lfw_fuel import lfw
+
+all_downloaders = (('lfw', lfw.download_subparser),)
+all_converters =  (('lfw', lfw.convert_subparser),)

lfw_fuel/lfw.py

@@ -51,7 +51,7 @@ def resolve_filename(format):
 def downloader_wrapper(format, directory, **kwargs):
     # add the right format file to the download list
     files.insert(0, "{}.tgz".format(resolve_filename(format)))
-    urls = map(lambda s: 'http://vis-www.cs.umass.edu/lfw/' + s, files)
+    urls = list(map(lambda s: 'http://vis-www.cs.umass.edu/lfw/' + s, files))
     default_downloader(directory, urls=urls, filenames=files, **kwargs)
 
 # this subparser hook is used for briq-download
@@ -67,9 +67,9 @@ def download_subparser(subparser):
     subparser.add_argument(
         "--format", help="alternate format", type=str, default=None)
 
-    urls = map(lambda s: 'http://vis-www.cs.umass.edu/lfw/' + s, files)
+    urls = list(map(lambda s: 'http://vis-www.cs.umass.edu/lfw/' + s, files))
 
-    subparser.set_defaults(func=downloader_wrapper)
+    return downloader_wrapper
 
 
 ########### Convert section ##############
@@ -121,9 +121,9 @@ def convert_lfw(directory, basename, output_directory):
 
     print("--> Building test/train lists")
     # build lists, throwing away heading
-    with open('pairsDevTrain.txt', 'rb') as csvfile:
+    with open('pairsDevTrain.txt', 'r') as csvfile:
         trainrows = list(csv.reader(csvfile, delimiter='\t'))[1:]
-    with open('pairsDevTest.txt', 'rb') as csvfile:
+    with open('pairsDevTest.txt', 'r') as csvfile:
         testrows = list(csv.reader(csvfile, delimiter='\t'))[1:]
 
     print("--> Converting")
@@ -131,8 +131,8 @@ def convert_lfw(directory, basename, output_directory):
     train_images = load_images("train", tar, tar_subdir, trainrows)
     test_images  = load_images("test",  tar, tar_subdir, testrows)
 
-    train_labels = np.array(map(lambda r:loadLabelsFromRow(r), trainrows))
-    test_labels = np.array(map(lambda r:loadLabelsFromRow(r), testrows))
+    train_labels = np.array(list(map(lambda r:loadLabelsFromRow(r), trainrows)))
+    test_labels = np.array(list(map(lambda r:loadLabelsFromRow(r), testrows)))
 
     train_features = np.array([[f[0,:,:,0], f[0,:,:,1], f[0,:,:,2], f[1,:,:,0], f[1,:,:,1], f[1,:,:,2]] for f in train_images])
     test_features  = np.array([[f[0,:,:,0], f[0,:,:,1], f[0,:,:,2], f[1,:,:,0], f[1,:,:,1], f[1,:,:,2]] for f in test_images])
@@ -173,7 +173,7 @@ def convert_subparser(subparser):
     # optional format can be funneled, deepfunneled, etc
     subparser.add_argument(
         "--format", help="alternate format", type=str, default=None)
-    subparser.set_defaults(func=convert_lfw_wrapper)
+    return convert_lfw_wrapper
 
 
 ########### Fuel Dataset section ##############

setup.py

@@ -2,7 +2,7 @@ from setuptools import setup
 from setuptools import find_packages
 
 setup(name='lfw_fuel',
-      version='0.1.0',
+      version='0.1.1',
       description='Labeled Faces in the Wild fuel dataset',
       author='Tom White',
       author_email='tom@sixdozen.com',