qiwihui · July 11, 2019 07:50
diff --git a/keras_weighted_categorical_crossentropy.py b/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])
	'''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])