Datasets:
HenryAI
/
KerasCodeExamples.txt

Dataset card Data Studio Files
xet
 Community
text
stringlengths
0
4.99k
# Initialize `RandAugment` object with 2 layers of
# augmentation transforms and strength of 9.
augmenter = RandAugment(num_layers=2, magnitude=9)
For training the teacher model, we will only be using two geometric augmentation transforms: random horizontal flip and random crop.
def preprocess_train(image, label, noisy=True):
image = tf.image.random_flip_left_right(image)
# We first resize the original image to a larger dimension
# and then we take random crops from it.
image = tf.image.resize(image, [RESIZE_TO, RESIZE_TO])
image = tf.image.random_crop(image, [CROP_TO, CROP_TO, 3])
if noisy:
image = augmenter.distort(image)
return image, label
def preprocess_test(image, label):
image = tf.image.resize(image, [CROP_TO, CROP_TO])
return image, label
train_ds = tf.data.Dataset.from_tensor_slices((new_train_x, new_y_train))
validation_ds = tf.data.Dataset.from_tensor_slices((val_x, val_y))
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test))
We make sure train_clean_ds and train_noisy_ds are shuffled using the same seed to ensure their orders are exactly the same. This will be helpful during training the student model.
# This dataset will be used to train the first model.
train_clean_ds = (
train_ds.shuffle(BATCH_SIZE * 10, seed=42)
.map(lambda x, y: (preprocess_train(x, y, noisy=False)), num_parallel_calls=AUTO)
.batch(BATCH_SIZE)
.prefetch(AUTO)
)
# This prepares the `Dataset` object to use RandAugment.
train_noisy_ds = (
train_ds.shuffle(BATCH_SIZE * 10, seed=42)
.map(preprocess_train, num_parallel_calls=AUTO)
.batch(BATCH_SIZE)
.prefetch(AUTO)
)
validation_ds = (
validation_ds.map(preprocess_test, num_parallel_calls=AUTO)
.batch(BATCH_SIZE)
.prefetch(AUTO)
)
test_ds = (
test_ds.map(preprocess_test, num_parallel_calls=AUTO)
.batch(BATCH_SIZE)
.prefetch(AUTO)
)
# This dataset will be used to train the second model.
consistency_training_ds = tf.data.Dataset.zip((train_clean_ds, train_noisy_ds))
Visualize the datasets
sample_images, sample_labels = next(iter(train_clean_ds))
plt.figure(figsize=(10, 10))
for i, image in enumerate(sample_images[:9]):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(image.numpy().astype(\"int\"))
plt.axis(\"off\")
sample_images, sample_labels = next(iter(train_noisy_ds))
plt.figure(figsize=(10, 10))
for i, image in enumerate(sample_images[:9]):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(image.numpy().astype(\"int\"))
plt.axis(\"off\")
png
png
Define a model building utility function
We now define our model building utility. Our model is based on the ResNet50V2 architecture.
def get_training_model(num_classes=10):
resnet50_v2 = tf.keras.applications.ResNet50V2(
weights=None, include_top=False, input_shape=(CROP_TO, CROP_TO, 3),
)
model = tf.keras.Sequential(
[
layers.Input((CROP_TO, CROP_TO, 3)),
layers.Rescaling(scale=1.0 / 127.5, offset=-1),
resnet50_v2,
layers.GlobalAveragePooling2D(),
layers.Dense(num_classes),
]
)
return model
In the interest of reproducibility, we serialize the initial random weights of the teacher network.
initial_teacher_model = get_training_model()
initial_teacher_model.save_weights(\"initial_teacher_model.h5\")
Train the teacher model
As noted in Noisy Student Training, if the teacher model is trained with geometric ensembling and when the student model is forced to mimic that, it leads to better performance. The original work uses Stochastic Depth and Dropout to bring in the ensembling part but for this example, we will use Stochastic Weight Averag...
# Define the callbacks.
reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(patience=3)