model.compile(optimizer, loss=earth_mover_loss)
# load weights from trained model if it exists
if os.path.exists('weights/nasnet_large_weights.h5'):
model.load_weights('weights/nasnet_large_weights.h5')
# load pre-trained NIMA(NASNet Mobile) classifier weights
if os.path.exists('weights/nasnet_large_pretrained_weights.h5'):
model.load_weights('weights/nasnet_large_pretrained_weights.h5',
by_name=True)
checkpoint = ModelCheckpoint('weights/nasnet_large_weights.h5',
monitor='val_loss',
verbose=1,
save_weights_only=True,
save_best_only=True,
mode='min')
tensorboard = TensorBoardBatch(log_dir='./nasnet_logs/')
callbacks = [checkpoint, tensorboard]
batchsize = 200
epochs = 20
model.fit_generator(train_generator(batchsize=batchsize),
steps_per_epoch=(250000. // batchsize),
epochs=epochs,
verbose=1,
callbacks=callbacks,
validation_data=val_generator(batchsize=batchsize),
validation_steps=(5000. // batchsize))
def _float32_feature_list(floats):
return tf.train.Feature(float_list=tf.train.FloatList(value=floats))
model = NASNetLarge((image_size, image_size, 3),
include_top=False,
pooling='avg')
model.summary()
# ''' TRAIN SET '''
nb_samples = 250000 * 2
batchsize = 200
with sess.as_default():
generator = train_generator(batchsize, shuffle=False)
writer = tf.python_io.TFRecordWriter('weights/nasnet_large_train.tfrecord')
count = 0
for _ in range(nb_samples // batchsize):
x_batch, y_batch = next(generator)
with sess.as_default():
x_batch = model.predict(x_batch, batchsize, verbose=1)
for i, (x, y) in enumerate(zip(x_batch, y_batch)):
examples = {
'features': _float32_feature_list(x.flatten()),
'scores': _float32_feature_list(y.flatten()),
}
features = tf.train.Features(feature=examples)
