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fcn benchmark
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from keras.models import Sequential | |
from keras.layers.core import Permute | |
from keras.layers.convolutional import Convolution2D | |
from keras.layers.core import Activation | |
import theano | |
import theano.tensor as T | |
import datetime | |
import numpy as np | |
now = datetime.datetime.now | |
if theano.config.floatX == 'float64': | |
epsilon = 1.0e-9 | |
else: | |
epsilon = 1.0e-7 | |
def categorical_crossentropy_2d(y_true, y_pred): | |
y_pred = T.clip(y_pred, epsilon, 1.0 - epsilon) | |
# scale preds so that the class probas of each sample sum to 1 | |
y_pred /= y_pred.sum(axis=-1, keepdims=True) | |
cce = - (y_true * T.log(y_pred)).sum() | |
return cce | |
# input data | |
np.random.seed(123456) | |
n_images = 10 | |
s = (850, 649) | |
x = np.random.rand(n_images, 1, s[0], s[1]).astype('float32') | |
y = np.random.rand(n_images, s[0], s[1], 21).astype('float32') | |
layers = [ | |
Convolution2D(64, 3, 3, activation='relu', border_mode='same', input_shape=(1, s[0], s[1])), | |
Convolution2D(64, 3, 3, activation='relu', border_mode='same'), | |
Convolution2D(21, 3, 3, activation='relu', border_mode='same'), | |
Permute((2, 3, 1)), | |
Activation('softmax') | |
] | |
model = Sequential() | |
for l in layers: | |
model.add(l) | |
# compile | |
t = now() | |
model.compile(optimizer='adagrad', loss=categorical_crossentropy_2d) | |
print('Compilation time %s' % (now() - t)) | |
# train | |
t = now() | |
model.fit(x, y, batch_size=1, nb_epoch=1) | |
print('Training time for %d images (batch_size=1): %s' % (n_images, (now() - t))) | |
t = now() | |
model.fit(x, y, batch_size=n_images, nb_epoch=1) | |
print('Training time for %d images (batch_size=%s): %s' % (n_images, n_images, (now() - t))) | |
model.save_weights('w.h5', overwrite=True) | |
# predict | |
t = now() | |
model.predict(x) | |
print('Prediction time for %d images: %s' % (n_images, (now() - t))) |
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