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HenryAI
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KerasCodeExamples.txt

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\"tf_flowers\", split=[\"train[:85%]\", \"train[85%:]\"], as_supervised=True,
)
Downloading and preparing dataset tf_flowers/3.0.1 (download: 218.21 MiB, generated: 221.83 MiB, total: 440.05 MiB) to /root/tensorflow_datasets/tf_flowers/3.0.1...
Dataset tf_flowers downloaded and prepared to /root/tensorflow_datasets/tf_flowers/3.0.1. Subsequent calls will reuse this data.
Visualise the dataset
plt.figure(figsize=(10, 10))
for i, (image, label) in enumerate(train_ds.take(9)):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(image)
plt.title(int(label))
plt.axis(\"off\")
png
Define hyperparameters
RESIZE_TO = 384
CROP_TO = 224
BATCH_SIZE = 64
STEPS_PER_EPOCH = 10
AUTO = tf.data.AUTOTUNE # optimise the pipeline performance
NUM_CLASSES = 5 # number of classes
SCHEDULE_LENGTH = (
500 # we will train on lower resolution images and will still attain good results
)
SCHEDULE_BOUNDARIES = [
200,
300,
400,
] # more the dataset size the schedule length increase
The hyperparamteres like SCHEDULE_LENGTH and SCHEDULE_BOUNDARIES are determined based on empirical results. The method has been explained in the original paper and in their Google AI Blog Post.
The SCHEDULE_LENGTH is aslo determined whether to use MixUp Augmentation or not. You can also find an easy MixUp Implementation in Keras Coding Examples.
Define preprocessing helper functions
SCHEDULE_LENGTH = SCHEDULE_LENGTH * 512 / BATCH_SIZE
@tf.function
def preprocess_train(image, label):
image = tf.image.random_flip_left_right(image)
image = tf.image.resize(image, (RESIZE_TO, RESIZE_TO))
image = tf.image.random_crop(image, (CROP_TO, CROP_TO, 3))
image = image / 255.0
return (image, label)
@tf.function
def preprocess_test(image, label):
image = tf.image.resize(image, (RESIZE_TO, RESIZE_TO))
image = image / 255.0
return (image, label)
DATASET_NUM_TRAIN_EXAMPLES = train_ds.cardinality().numpy()
repeat_count = int(
SCHEDULE_LENGTH * BATCH_SIZE / DATASET_NUM_TRAIN_EXAMPLES * STEPS_PER_EPOCH
)
repeat_count += 50 + 1 # To ensure at least there are 50 epochs of training
Define the data pipeline
# Training pipeline
pipeline_train = (
train_ds.shuffle(10000)
.repeat(repeat_count) # Repeat dataset_size / num_steps
.map(preprocess_train, num_parallel_calls=AUTO)
.batch(BATCH_SIZE)
.prefetch(AUTO)
)
# Validation pipeline
pipeline_validation = (
validation_ds.map(preprocess_test, num_parallel_calls=AUTO)
.batch(BATCH_SIZE)
.prefetch(AUTO)
)
Visualise the training samples
image_batch, label_batch = next(iter(pipeline_train))
plt.figure(figsize=(10, 10))
for n in range(25):
ax = plt.subplot(5, 5, n + 1)
plt.imshow(image_batch[n])
plt.title(label_batch[n].numpy())
plt.axis(\"off\")
png
Load pretrained TF-Hub model into a KerasLayer
bit_model_url = \"https://tfhub.dev/google/bit/m-r50x1/1\"
bit_module = hub.KerasLayer(bit_model_url)
Create BigTransfer (BiT) model
To create the new model, we:
Cut off the BiT model’s original head. This leaves us with the “pre-logits” output. We do not have to do this if we use the ‘feature extractor’ models (i.e. all those in subdirectories titled feature_vectors), since for those models the head has already been cut off.
Add a new head with the number of outputs equal to the number of classes of our new task. Note that it is important that we initialise the head to all zeroes.
class MyBiTModel(keras.Model):
def __init__(self, num_classes, module, **kwargs):
super().__init__(**kwargs)