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from scipy.misc import imread, imresize | |
from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, merge, Reshape, Activation | |
from keras.models import Model | |
from keras.regularizers import l2 | |
from keras.optimizers import SGD | |
from googlenet_custom_layers import PoolHelper,LRN | |
def create_googlenet(weights_path=None): |
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'''This script goes along the blog post | |
"Building powerful image classification models using very little data" | |
from blog.keras.io. | |
It uses data that can be downloaded at: | |
https://www.kaggle.com/c/dogs-vs-cats/data | |
In our setup, we: | |
- created a data/ folder | |
- created train/ and validation/ subfolders inside data/ |