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cnrblm / classifier_from_little_data_script_3.py
Created May 21, 2019 — forked from fchollet/classifier_from_little_data_script_3.py
Fine-tuning a Keras model. Updated to the Keras 2.0 API.
View classifier_from_little_data_script_3.py
'''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/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats
View classifier_from_little_data_script_2.py
'''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/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats
View gist:7a075301b536087417341653d5c35d83
import numpy as np
from keras.layers import Dropout
from keras import applications
from keras.layers import Dense, GlobalAveragePooling2D, merge, Input
from keras.models import Model
max_words = 10000
epochs = 50
batch_size = 32
View extract_vgg_16_features.py
from os import listdir
from pickle import dump
from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.applications.vgg16 import preprocess_input
from keras.models import Model
import argparse
parser = argparse.ArgumentParser()