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KerasCodeExamples.txt

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training[window_length - 1 : 0 : -1], training, training[-1:-window_length:-1]
]
test_s = np.r_[
testing[window_length - 1 : 0 : -1], testing, testing[-1:-window_length:-1]
]
w = np.hamming(window_length)
train_y = np.convolve(w / w.sum(), train_s, mode=\"valid\")
test_y = np.convolve(w / w.sum(), test_s, mode=\"valid\")
# Display the training accuracies.
x = np.arange(0, len(test_y), 1)
plt.plot(x, test_y, x, train_y)
plt.legend([\"test\", \"train\"])
plt.grid()
train_set, test_images, test_labels = dataset.get_mini_dataset(
eval_batch_size, eval_iters, shots, classes, split=True
)
for images, labels in train_set:
with tf.GradientTape() as tape:
preds = model(images)
loss = keras.losses.sparse_categorical_crossentropy(labels, preds)
grads = tape.gradient(loss, model.trainable_weights)
optimizer.apply_gradients(zip(grads, model.trainable_weights))
test_preds = model.predict(test_images)
test_preds = tf.argmax(test_preds).numpy()
_, axarr = plt.subplots(nrows=1, ncols=5, figsize=(20, 20))
sample_keys = list(train_dataset.data.keys())
for i, ax in zip(range(5), axarr):
temp_image = np.stack((test_images[i, :, :, 0],) * 3, axis=2)
temp_image *= 255
temp_image = np.clip(temp_image, 0, 255).astype(\"uint8\")
ax.set_title(
\"Label : {}, Prediction : {}\".format(int(test_labels[i]), test_preds[i])
)
ax.imshow(temp_image, cmap=\"gray\")
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
plt.show()
png
png
Mitigating resolution discrepancy between training and test sets.
Introduction
It is a common practice to use the same input image resolution while training and testing vision models. However, as investigated in Fixing the train-test resolution discrepancy (Touvron et al.), this practice leads to suboptimal performance. Data augmentation is an indispensable part of the training process of deep ne...
In this example, we implement the FixRes techniques introduced by Touvron et al. to fix this discrepancy.
Imports
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow as tf
import tensorflow_datasets as tfds
tfds.disable_progress_bar()
import matplotlib.pyplot as plt
Load the tf_flowers dataset
train_dataset, val_dataset = tfds.load(
\"tf_flowers\", split=[\"train[:90%]\", \"train[90%:]\"], as_supervised=True
)
num_train = train_dataset.cardinality()
num_val = val_dataset.cardinality()
print(f\"Number of training examples: {num_train}\")
print(f\"Number of validation examples: {num_val}\")
Number of training examples: 3303
Number of validation examples: 367
Data preprocessing utilities
We create three datasets:
A dataset with a smaller resolution - 128x128.
Two datasets with a larger resolution - 224x224.
We will apply different augmentation transforms to the larger-resolution datasets.
The idea of FixRes is to first train a model on a smaller resolution dataset and then fine-tune it on a larger resolution dataset. This simple yet effective recipe leads to non-trivial performance improvements. Please refer to the original paper for results.
# Reference: https://github.com/facebookresearch/FixRes/blob/main/transforms_v2.py.
batch_size = 128
auto = tf.data.AUTOTUNE
smaller_size = 128
bigger_size = 224
size_for_resizing = int((bigger_size / smaller_size) * bigger_size)
central_crop_layer = layers.CenterCrop(bigger_size, bigger_size)
def preprocess_initial(train, image_size):
\"\"\"Initial preprocessing function for training on smaller resolution.
For training, do random_horizontal_flip -> random_crop.
For validation, just resize.
No color-jittering has been used.