| import numpy as np | |
| from search import * | |
| ## Flip-flop | |
| class MaximizeAlternations(Problem): | |
| def __init__(self, initial): | |
| """initial state is a numpy array with each element representing a bit 0 or 1, | |
| goal state is same size as initial state with all bits turned 1""" | |
| assert sum([n != 0 and n != 1 for n in initial]) == 0, "initial state must contain binary values." | |
| self.initial = initial |
| class VideoHelper(object): | |
| def __init__(self, video_file, frame_start=0, frame_end=np.float('inf'), do_loop=True, preload_frames=1000): | |
| self.preloaded = False | |
| self.preload_frames = preload_frames | |
| self._init_capture(video_file, frame_start, frame_end) | |
| self.do_loop = do_loop | |
| self.paused = False | |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman