Datasets:
HenryAI
/
KerasCodeExamples.txt

Dataset card Data Studio Files
xet
 Community
text
stringlengths
0
4.99k
Epoch 94/100
176/176 [==============================] - 22s 125ms/step - loss: 0.7692 - accuracy: 0.7689 - top-5-accuracy: 0.9599 - val_loss: 1.8928 - val_accuracy: 0.5506 - val_top-5-accuracy: 0.8184
Epoch 95/100
176/176 [==============================] - 22s 126ms/step - loss: 0.7783 - accuracy: 0.7661 - top-5-accuracy: 0.9597 - val_loss: 1.8646 - val_accuracy: 0.5490 - val_top-5-accuracy: 0.8166
Epoch 96/100
176/176 [==============================] - 22s 125ms/step - loss: 0.7547 - accuracy: 0.7711 - top-5-accuracy: 0.9638 - val_loss: 1.9347 - val_accuracy: 0.5484 - val_top-5-accuracy: 0.8150
Epoch 97/100
176/176 [==============================] - 22s 125ms/step - loss: 0.7603 - accuracy: 0.7692 - top-5-accuracy: 0.9616 - val_loss: 1.8966 - val_accuracy: 0.5522 - val_top-5-accuracy: 0.8144
Epoch 98/100
176/176 [==============================] - 22s 125ms/step - loss: 0.7595 - accuracy: 0.7730 - top-5-accuracy: 0.9610 - val_loss: 1.8728 - val_accuracy: 0.5470 - val_top-5-accuracy: 0.8170
Epoch 99/100
176/176 [==============================] - 22s 125ms/step - loss: 0.7542 - accuracy: 0.7736 - top-5-accuracy: 0.9622 - val_loss: 1.9132 - val_accuracy: 0.5504 - val_top-5-accuracy: 0.8156
Epoch 100/100
176/176 [==============================] - 22s 125ms/step - loss: 0.7410 - accuracy: 0.7787 - top-5-accuracy: 0.9635 - val_loss: 1.9233 - val_accuracy: 0.5428 - val_top-5-accuracy: 0.8120
313/313 [==============================] - 4s 12ms/step - loss: 1.8487 - accuracy: 0.5514 - top-5-accuracy: 0.8186
Test accuracy: 55.14%
Test top 5 accuracy: 81.86%
After 100 epochs, the ViT model achieves around 55% accuracy and 82% top-5 accuracy on the test data. These are not competitive results on the CIFAR-100 dataset, as a ResNet50V2 trained from scratch on the same data can achieve 67% accuracy.
Note that the state of the art results reported in the paper are achieved by pre-training the ViT model using the JFT-300M dataset, then fine-tuning it on the target dataset. To improve the model quality without pre-training, you can try to train the model for more epochs, use a larger number of Transformer layers, res...
Image segmentation model trained from scratch on the Oxford Pets dataset
Download the data
!curl -O https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz
!curl -O https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz
!tar -xf images.tar.gz
!tar -xf annotations.tar.gz
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 755M 100 755M 0 0 6943k 0 0:01:51 0:01:51 --:--:-- 7129k
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 18.2M 100 18.2M 0 0 5692k 0 0:00:03 0:00:03 --:--:-- 5692k
Prepare paths of input images and target segmentation masks
import os
input_dir = \"images/\"
target_dir = \"annotations/trimaps/\"
img_size = (160, 160)
num_classes = 3
batch_size = 32
input_img_paths = sorted(
[
os.path.join(input_dir, fname)
for fname in os.listdir(input_dir)
if fname.endswith(\".jpg\")
]
)
target_img_paths = sorted(
[
os.path.join(target_dir, fname)
for fname in os.listdir(target_dir)
if fname.endswith(\".png\") and not fname.startswith(\".\")
]
)
print(\"Number of samples:\", len(input_img_paths))
for input_path, target_path in zip(input_img_paths[:10], target_img_paths[:10]):
print(input_path, \"|\", target_path)
Number of samples: 7390
images/Abyssinian_1.jpg | annotations/trimaps/Abyssinian_1.png
images/Abyssinian_10.jpg | annotations/trimaps/Abyssinian_10.png
images/Abyssinian_100.jpg | annotations/trimaps/Abyssinian_100.png
images/Abyssinian_101.jpg | annotations/trimaps/Abyssinian_101.png
images/Abyssinian_102.jpg | annotations/trimaps/Abyssinian_102.png
images/Abyssinian_103.jpg | annotations/trimaps/Abyssinian_103.png
images/Abyssinian_104.jpg | annotations/trimaps/Abyssinian_104.png
images/Abyssinian_105.jpg | annotations/trimaps/Abyssinian_105.png
images/Abyssinian_106.jpg | annotations/trimaps/Abyssinian_106.png
images/Abyssinian_107.jpg | annotations/trimaps/Abyssinian_107.png
What does one input image and corresponding segmentation mask look like?
from IPython.display import Image, display
from tensorflow.keras.preprocessing.image import load_img
import PIL
from PIL import ImageOps
# Display input image #7
display(Image(filename=input_img_paths[9]))
# Display auto-contrast version of corresponding target (per-pixel categories)
img = PIL.ImageOps.autocontrast(load_img(target_img_paths[9]))
display(img)
jpeg
png
Prepare Sequence class to load & vectorize batches of data
from tensorflow import keras
import numpy as np
from tensorflow.keras.preprocessing.image import load_img
class OxfordPets(keras.utils.Sequence):
\"\"\"Helper to iterate over the data (as Numpy arrays).\"\"\"
def __init__(self, batch_size, img_size, input_img_paths, target_img_paths):
self.batch_size = batch_size