Datasets:
HenryAI
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KerasCodeExamples.txt

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for image, label in ds.map(extraction):
image = image.numpy()
label = str(label.numpy())
if label not in self.data:
self.data[label] = []
self.data[label].append(image)
self.labels = list(self.data.keys())
def get_mini_dataset(
self, batch_size, repetitions, shots, num_classes, split=False
):
temp_labels = np.zeros(shape=(num_classes * shots))
temp_images = np.zeros(shape=(num_classes * shots, 28, 28, 1))
if split:
test_labels = np.zeros(shape=(num_classes))
test_images = np.zeros(shape=(num_classes, 28, 28, 1))
# Get a random subset of labels from the entire label set.
label_subset = random.choices(self.labels, k=num_classes)
for class_idx, class_obj in enumerate(label_subset):
# Use enumerated index value as a temporary label for mini-batch in
# few shot learning.
temp_labels[class_idx * shots : (class_idx + 1) * shots] = class_idx
# If creating a split dataset for testing, select an extra sample from each
# label to create the test dataset.
if split:
test_labels[class_idx] = class_idx
images_to_split = random.choices(
self.data[label_subset[class_idx]], k=shots + 1
)
test_images[class_idx] = images_to_split[-1]
temp_images[
class_idx * shots : (class_idx + 1) * shots
] = images_to_split[:-1]
else:
# For each index in the randomly selected label_subset, sample the
# necessary number of images.
temp_images[
class_idx * shots : (class_idx + 1) * shots
] = random.choices(self.data[label_subset[class_idx]], k=shots)
dataset = tf.data.Dataset.from_tensor_slices(
(temp_images.astype(np.float32), temp_labels.astype(np.int32))
)
dataset = dataset.shuffle(100).batch(batch_size).repeat(repetitions)
if split:
return dataset, test_images, test_labels
return dataset
import urllib3
urllib3.disable_warnings() # Disable SSL warnings that may happen during download.
train_dataset = Dataset(training=True)
test_dataset = Dataset(training=False)
Downloading and preparing dataset omniglot/3.0.0 (download: 17.95 MiB, generated: Unknown size, total: 17.95 MiB) to /root/tensorflow_datasets/omniglot/3.0.0...
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Shuffling and writing examples to /root/tensorflow_datasets/omniglot/3.0.0.incompleteXTNZJN/omniglot-train.tfrecord
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Shuffling and writing examples to /root/tensorflow_datasets/omniglot/3.0.0.incompleteXTNZJN/omniglot-test.tfrecord
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Shuffling and writing examples to /root/tensorflow_datasets/omniglot/3.0.0.incompleteXTNZJN/omniglot-small1.tfrecord
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Shuffling and writing examples to /root/tensorflow_datasets/omniglot/3.0.0.incompleteXTNZJN/omniglot-small2.tfrecord
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Dataset omniglot downloaded and prepared to /root/tensorflow_datasets/omniglot/3.0.0. Subsequent calls will reuse this data.
Visualize some examples from the dataset
_, axarr = plt.subplots(nrows=5, ncols=5, figsize=(20, 20))
sample_keys = list(train_dataset.data.keys())
for a in range(5):
for b in range(5):
temp_image = train_dataset.data[sample_keys[a]][b]
temp_image = np.stack((temp_image[:, :, 0],) * 3, axis=2)
temp_image *= 255
temp_image = np.clip(temp_image, 0, 255).astype(\"uint8\")
if b == 2:
axarr[a, b].set_title(\"Class : \" + sample_keys[a])
axarr[a, b].imshow(temp_image, cmap=\"gray\")