add example

examples/pascal_voc_jaccard_loss.py

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+'''
+Trains a DenseNet-40-12 model on the CIFAR-10 Dataset.
+
+Gets a 94.84% accuracy score after 100 epochs.
+'''
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import division
+
+import numpy as np
+
+from keras import backend as K
+from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
+from keras.datasets import pascal_voc
+from keras.optimizers import Adam
+from keras.preprocessing.image import ImageDataGenerator
+from keras.utils import np_utils
+from keras_contrib.applications import DenseNet
+
+batch_size = 64
+nb_classes = 10
+epochs = 100
+
+img_rows, img_cols = 32, 32
+img_channels = 3
+
+# Parameters for the DenseNet model builder
+img_dim = (img_channels, img_rows, img_cols) if K.image_data_format() == 'channels_first' else (img_rows, img_cols, img_channels)
+depth = 40
+nb_dense_block = 3
+growth_rate = 12
+nb_filter = 16
+dropout_rate = 0.0  # 0.0 for data augmentation
+
+# Create the model (without loading weights)
+model = DenseNet(depth=depth, nb_dense_block=nb_dense_block,
+                 growth_rate=growth_rate, nb_filter=nb_filter,
+                 dropout_rate=dropout_rate,
+                 input_shape=img_dim,
+                 weights=None)
+print('Model created')
+
+model.summary()
+
+optimizer = Adam(lr=1e-3)  # Using Adam instead of SGD to speed up training
+model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['acc'])
+print('Finished compiling')
+
+(trainX, trainY), (testX, testY) = pascal_voc.load_data()
+
+trainX = trainX.astype('float32')
+testX = testX.astype('float32')
+
+trainX /= 255.
+testX /= 255.
+
+Y_train = np_utils.to_categorical(trainY, nb_classes)
+Y_test = np_utils.to_categorical(testY, nb_classes)
+
+generator = ImageDataGenerator(rotation_range=15,
+                               width_shift_range=5. / 32,
+                               height_shift_range=5. / 32)
+
+generator.fit(trainX, seed=0)
+
+weights_file = 'DenseNet-40-12-CIFAR-10.h5'
+
+lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1),
+                               cooldown=0, patience=10, min_lr=0.5e-6)
+early_stopper = EarlyStopping(monitor='val_acc', min_delta=1e-4, patience=20)
+model_checkpoint = ModelCheckpoint(weights_file, monitor='val_acc', save_best_only=True,
+                                   save_weights_only=True, mode='auto')
+
+callbacks = [lr_reducer, early_stopper, model_checkpoint]
+
+model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size), steps_per_epoch=len(trainX) // batch_size,
+                    epochs=epochs,
+                    callbacks=callbacks,
+                    validation_data=(testX, Y_test),
+                    verbose=2)
+
+scores = model.evaluate(testX, Y_test, batch_size=batch_size)
+print('Test loss : ', scores[0])
+print('Test accuracy : ', scores[1])