Datasets:
HenryAI
/
KerasCodeExamples.txt

Dataset card Data Studio Files
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4.99k
validation_data=validation_ds,
epochs=epochs,
callbacks=[edit_distance_callback],
)
Epoch 1/10
1357/1357 [==============================] - 89s 51ms/step - loss: 13.6670 - val_loss: 11.8041
Mean edit distance for epoch 1: 20.5117
Epoch 2/10
1357/1357 [==============================] - 48s 36ms/step - loss: 10.6864 - val_loss: 9.6994
Mean edit distance for epoch 2: 20.1167
Epoch 3/10
1357/1357 [==============================] - 48s 35ms/step - loss: 9.0437 - val_loss: 8.0355
Mean edit distance for epoch 3: 19.7270
Epoch 4/10
1357/1357 [==============================] - 48s 35ms/step - loss: 7.6098 - val_loss: 6.4239
Mean edit distance for epoch 4: 19.1106
Epoch 5/10
1357/1357 [==============================] - 48s 35ms/step - loss: 6.3194 - val_loss: 4.9814
Mean edit distance for epoch 5: 18.4894
Epoch 6/10
1357/1357 [==============================] - 48s 35ms/step - loss: 5.3417 - val_loss: 4.1307
Mean edit distance for epoch 6: 18.1909
Epoch 7/10
1357/1357 [==============================] - 48s 35ms/step - loss: 4.6396 - val_loss: 3.7706
Mean edit distance for epoch 7: 18.1224
Epoch 8/10
1357/1357 [==============================] - 48s 35ms/step - loss: 4.1926 - val_loss: 3.3682
Mean edit distance for epoch 8: 17.9387
Epoch 9/10
1357/1357 [==============================] - 48s 36ms/step - loss: 3.8532 - val_loss: 3.1829
Mean edit distance for epoch 9: 17.9074
Epoch 10/10
1357/1357 [==============================] - 49s 36ms/step - loss: 3.5769 - val_loss: 2.9221
Mean edit distance for epoch 10: 17.7960
Inference
# A utility function to decode the output of the network.
def decode_batch_predictions(pred):
input_len = np.ones(pred.shape[0]) * pred.shape[1]
# Use greedy search. For complex tasks, you can use beam search.
results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0][
:, :max_len
]
# Iterate over the results and get back the text.
output_text = []
for res in results:
res = tf.gather(res, tf.where(tf.math.not_equal(res, -1)))
res = tf.strings.reduce_join(num_to_char(res)).numpy().decode(\"utf-8\")
output_text.append(res)
return output_text
# Let's check results on some test samples.
for batch in test_ds.take(1):
batch_images = batch[\"image\"]
_, ax = plt.subplots(4, 4, figsize=(15, 8))
preds = prediction_model.predict(batch_images)
pred_texts = decode_batch_predictions(preds)
for i in range(16):
img = batch_images[i]
img = tf.image.flip_left_right(img)
img = tf.transpose(img, perm=[1, 0, 2])
img = (img * 255.0).numpy().clip(0, 255).astype(np.uint8)
img = img[:, :, 0]
title = f\"Prediction: {pred_texts[i]}\"
ax[i // 4, i % 4].imshow(img, cmap=\"gray\")
ax[i // 4, i % 4].set_title(title)
ax[i // 4, i % 4].axis(\"off\")
plt.show()
png
To get better results the model should be trained for at least 50 epochs.
Final remarks
The prediction_model is fully compatible with TensorFlow Lite. If you are interested, you can use it inside a mobile application. You may find this notebook to be useful in this regard.
Not all the training examples are perfectly aligned as observed in this example. This can hurt model performance for complex sequences. To this end, we can leverage Spatial Transformer Networks (Jaderberg et al.) that can help the model learn affine transformations that maximize its performance.
Implement an image captioning model using a CNN and a Transformer.
Setup
import os
import re
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.applications import efficientnet
from tensorflow.keras.layers import TextVectorization
seed = 111
np.random.seed(seed)
tf.random.set_seed(seed)
Download the dataset
We will be using the Flickr8K dataset for this tutorial. This dataset comprises over 8,000 images, that are each paired with five different captions.