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Commit 0882e2fc authored by Welter, Felix's avatar Welter, Felix
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Add notebooks and evaluation metrics

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CaptionProcessing.ipynb 0 → 100644
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ImageFeatureCreation.ipynb 0 → 100644
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%% Cell type:code id: tags:
``` python
from keras.preprocessing.image import load_img
from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import img_to_array
from keras.applications.vgg16 import preprocess_input
from keras.applications.vgg16 import decode_predictions
from keras import Model
import matplotlib.pyplot as plt
import json
import os
from tqdm import tqdm
import pickle
```
%% Output
Using TensorFlow backend.
%% Cell type:code id: tags:
``` python
# get archive.zip from https://www.kaggle.com/adityajn105/flickr8k/notebooks
# !unzip -q archive.zip
```
%% Cell type:code id: tags:
``` python
model = VGG16()
```
%% Cell type:code id: tags:
``` python
image = load_img('Images/1000268201_693b08cb0e.jpg', target_size=(224, 224))
```
%% Cell type:code id: tags:
``` python
image
```
%% Output
<PIL.Image.Image image mode=RGB size=224x224 at 0x7F6FD07C3110>
%% Cell type:code id: tags:
``` python
# convert the image pixels to a numpy array
image = img_to_array(image)
image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
image = preprocess_input(image)
yhat = model.predict(image)
```
%% Cell type:code id: tags:
``` python
plt.plot(yhat[0], "bo")
plt.show()
```
%% Output
%% Cell type:code id: tags:
``` python
labels = decode_predictions(yhat)
for label in labels[0][:6]:
label = label
print('%s (%.2f%%)' % (label[1], label[2]*100))
```
%% Output
apiary (37.05%)
greenhouse (9.22%)
pot (9.21%)
rain_barrel (7.80%)
milk_can (3.24%)
%% Cell type:code id: tags:
``` python
# Ditch the last layer
sliced_model = Model(inputs=model.inputs, outputs=model.layers[-2].output)
plt.plot(sliced_model.predict(image)[0], "bo", markersize=1)
```
%% Output
[<matplotlib.lines.Line2D at 0x7f6f4c76b810>]
%% Cell type:code id: tags:
``` python
%timeit sliced_model.predict(image)
```
%% Output
388 ms ± 24 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%% Cell type:code id: tags:
``` python
word_feature_file = 'img_features.pkl'
image_folder = "Images"
if not os.path.isfile(word_feature_file):
image_names = os.listdir(image_folder)
img_vectors = {}
for image_name in tqdm(image_names):
image = img_to_array(load_img(os.path.join(image_folder, image_name), target_size=(224, 224)))
image_input = preprocess_input(image.reshape((1, image.shape[0], image.shape[1], image.shape[2])))
vec = sliced_model.predict(image_input)[0]
img_vectors[image_name] = vec.tolist()
with open(word_feature_file, 'wb') as handle:
pickle.dump(img_vectors, handle)
with open("img_features.json", "w") as handle:
json.dump(img_vectors, handle)
else:
with open(word_feature_file, 'rb') as handle:
img_vectors = pickle.load(handle)
```
%% Output
100%|██████████| 8091/8091 [54:37<00:00, 2.47it/s]
%% Cell type:code id: tags:
``` python
img_vectors[os.listdir(image_folder)[111]]
```
%% Output
array([1.9173548, 0. , 2.1064095, ..., 0. , 0. ,
0. ], dtype=float32)
%% Cell type:code id: tags:
``` python
!ls -lsa
```
%% Output
total 1690420
4 drwxr-xr-x 11 jupyter jupyter 4096 Dec 17 10:03 .
4 drwxr-xr-x 3 root root 4096 Nov 22 10:27 ..
4 -rw------- 1 jupyter jupyter 80 Dec 3 15:49 .bash_history
4 drwxr-xr-x 6 jupyter jupyter 4096 Dec 6 11:33 .cache
4 drwxr-xr-x 4 jupyter jupyter 4096 Nov 22 10:36 .config
4 drwxr-xr-x 2 jupyter jupyter 4096 Nov 22 10:30 .docker
4 drwxr-xr-x 2 jupyter jupyter 4096 Dec 17 09:09 .ipynb_checkpoints
4 drwxr-xr-x 5 jupyter jupyter 4096 Nov 22 10:36 .ipython
4 drwxr-xr-x 3 jupyter jupyter 4096 Nov 22 10:34 .jupyter
4 drwxr-xr-x 3 jupyter jupyter 4096 Nov 22 11:20 .keras
4 drwxr-xr-x 3 jupyter jupyter 4096 Nov 22 10:30 .local
1580 -rw-r--r-- 1 jupyter jupyter 1615666 Dec 6 12:09 CaptionProcessing.ipynb
188 -rw-r--r-- 1 jupyter jupyter 190383 Dec 17 10:03 ImageFeatureCreation.ipynb
396 drwxr-xr-x 2 jupyter jupyter 405504 Nov 22 11:37 Images
1086892 -rw-r--r-- 1 jupyter jupyter 1112971163 Nov 22 11:34 archive.zip
3244 -rw-r--r-- 1 jupyter jupyter 3319294 Apr 27 2020 captions.txt
306340 -rw-r--r-- 1 jupyter jupyter 313688397 Dec 17 10:04 img_features.json
291680 -rw-r--r-- 1 jupyter jupyter 298676767 Dec 17 10:03 img_features.pkl
56 -rw-r--r-- 1 jupyter jupyter 55857 Dec 6 12:01 model.png
scores_full.pickle 0 → 100644
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