please see https://github.com/keras-team/keras/issues/2115 for more details.

keras_weighted_categorical_crossentropy.py

'''Train a simple deep NN on the MNIST dataset.
Get to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
'''

import numpy as np
np.random.seed(1337)  # for reproducibility
from functools import partial
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD, Adam, RMSprop
from keras.utils import np_utils
import keras.backend as K
from itertools import product

# Custom loss function with costs

def w_categorical_crossentropy(y_true, y_pred, weights):
    nb_cl = len(weights)
    final_mask = K.zeros_like(y_pred[:, 0])
    y_pred_max = K.max(y_pred, axis=1)
    y_pred_max = K.expand_dims(y_pred_max, 1)
    y_pred_max_mat = K.equal(y_pred, y_pred_max)
    for c_p, c_t in product(range(nb_cl), range(nb_cl)):
        final_mask += (K.cast(weights[c_t, c_p], K.floatx()) * K.cast(
            y_pred_max_mat[:, c_p], K.floatx()) * K.cast(
                y_true[:, c_t], K.floatx()))
    return K.categorical_crossentropy(y_pred, y_true) * final_mask


w_array = np.ones((10, 10))
w_array[1, 7] = 1.2
w_array[7, 1] = 1.2

ncce = partial(w_categorical_crossentropy, weights=w_array)
ncce.__name__ = 'w_categorical_crossentropy'

batch_size = 128
nb_classes = 10
nb_epoch = 20

# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

model = Sequential()
model.add(Dense(512, input_shape=(784, )))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax'))

rms = RMSprop()
model.compile(loss=ncce, optimizer=rms)

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=1)
print(score)
print('Test score:', score[0])
print('Test accuracy:', score[1])