Datasets:
HenryAI
/
KerasCodeExamples.txt

Dataset card Data Studio Files
xet
 Community
text
stringlengths
0
4.99k
96/96 [==============================] - 42s 277ms/step - loss: 12.1264 - acc: 0.4636 - val_loss: 14.9451 - val_acc: 0.4158
Epoch 14/30
2021-09-17 05:27:42.513445: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:175] Filling up shuffle buffer (this may take a while): 63 of 256
2021-09-17 05:27:47.675342: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:228] Shuffle buffer filled.
96/96 [==============================] - 42s 277ms/step - loss: 11.8244 - acc: 0.4724 - val_loss: 14.9751 - val_acc: 0.4148
Epoch 15/30
2021-09-17 05:28:24.371225: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:175] Filling up shuffle buffer (this may take a while): 63 of 256
2021-09-17 05:28:29.829654: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:228] Shuffle buffer filled.
96/96 [==============================] - 42s 277ms/step - loss: 11.5644 - acc: 0.4776 - val_loss: 15.0377 - val_acc: 0.4167
Epoch 16/30
2021-09-17 05:29:06.564650: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:175] Filling up shuffle buffer (this may take a while): 62 of 256
2021-09-17 05:29:11.945996: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:228] Shuffle buffer filled.
96/96 [==============================] - 42s 277ms/step - loss: 11.3046 - acc: 0.4852 - val_loss: 15.0575 - val_acc: 0.4135
<keras.callbacks.History at 0x7fb3d4b1b1d0>
Check sample predictions
vocab = vectorization.get_vocabulary()
index_lookup = dict(zip(range(len(vocab)), vocab))
max_decoded_sentence_length = SEQ_LENGTH - 1
valid_images = list(valid_data.keys())
def generate_caption():
# Select a random image from the validation dataset
sample_img = np.random.choice(valid_images)
# Read the image from the disk
sample_img = decode_and_resize(sample_img)
img = sample_img.numpy().clip(0, 255).astype(np.uint8)
plt.imshow(img)
plt.show()
# Pass the image to the CNN
img = tf.expand_dims(sample_img, 0)
img = caption_model.cnn_model(img)
# Pass the image features to the Transformer encoder
encoded_img = caption_model.encoder(img, training=False)
# Generate the caption using the Transformer decoder
decoded_caption = \"<start> \"
for i in range(max_decoded_sentence_length):
tokenized_caption = vectorization([decoded_caption])[:, :-1]
mask = tf.math.not_equal(tokenized_caption, 0)
predictions = caption_model.decoder(
tokenized_caption, encoded_img, training=False, mask=mask
)
sampled_token_index = np.argmax(predictions[0, i, :])
sampled_token = index_lookup[sampled_token_index]
if sampled_token == \" <end>\":
break
decoded_caption += \" \" + sampled_token
decoded_caption = decoded_caption.replace(\"<start> \", \"\")
decoded_caption = decoded_caption.replace(\" <end>\", \"\").strip()
print(\"Predicted Caption: \", decoded_caption)
# Check predictions for a few samples
generate_caption()
generate_caption()
generate_caption()
png
Predicted Caption: a group of dogs race in the snow
png
Predicted Caption: a man in a blue canoe on a lake
png
Predicted Caption: a black and white dog is running through a green grass
End Notes
We saw that the model starts to generate reasonable captions after a few epochs. To keep this example easily runnable, we have trained it with a few constraints, like a minimal number of attention heads. To improve the predictions, you can try changing these training settings and find a good model for your use case.
Training an image classifier from scratch on the Kaggle Cats vs Dogs dataset.
Introduction
This example shows how to do image classification from scratch, starting from JPEG image files on disk, without leveraging pre-trained weights or a pre-made Keras Application model. We demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset.
We use the image_dataset_from_directory utility to generate the datasets, and we use Keras image preprocessing layers for image standardization and data augmentation.
Setup
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
Load the data: the Cats vs Dogs dataset
Raw data download
First, let's download the 786M ZIP archive of the raw data:
!curl -O https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip
!unzip -q kagglecatsanddogs_3367a.zip
!ls
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed