text stringlengths 0 4.99k |
|---|
/ float(self.total_steps - self.warmup_steps) |
) |
learning_rate = 0.5 * self.learning_rate_base * (1 + cos_annealed_lr) |
if self.warmup_steps > 0: |
if self.learning_rate_base < self.warmup_learning_rate: |
raise ValueError( |
\"Learning_rate_base must be larger or equal to \" |
\"warmup_learning_rate.\" |
) |
slope = ( |
self.learning_rate_base - self.warmup_learning_rate |
) / self.warmup_steps |
warmup_rate = slope * tf.cast(step, tf.float32) + self.warmup_learning_rate |
learning_rate = tf.where( |
step < self.warmup_steps, warmup_rate, learning_rate |
) |
return tf.where( |
step > self.total_steps, 0.0, learning_rate, name=\"learning_rate\" |
) |
We can now plot a a graph of learning rates generated using this schedule. |
ARTIFICIAL_EPOCHS = 1000 |
ARTIFICIAL_BATCH_SIZE = 512 |
DATASET_NUM_TRAIN_EXAMPLES = 1020 |
TOTAL_STEPS = int( |
DATASET_NUM_TRAIN_EXAMPLES / ARTIFICIAL_BATCH_SIZE * ARTIFICIAL_EPOCHS |
) |
scheduled_lrs = WarmUpCosine( |
learning_rate_base=INIT_LR, |
total_steps=TOTAL_STEPS, |
warmup_learning_rate=0.0, |
warmup_steps=1500, |
) |
lrs = [scheduled_lrs(step) for step in range(TOTAL_STEPS)] |
plt.plot(lrs) |
plt.xlabel(\"Step\", fontsize=14) |
plt.ylabel(\"LR\", fontsize=14) |
plt.show() |
png |
The original paper uses at least 1000 epochs and a batch size of 512 to perform \"function matching\". The objective of this example is to present a workflow to implement the recipe and not to demonstrate the results when they are applied at full scale. However, these recipes will transfer to the original settings from... |
Training |
optimizer = tfa.optimizers.AdamW( |
weight_decay=WEIGHT_DECAY, learning_rate=scheduled_lrs, clipnorm=CLIP_THRESHOLD |
) |
student_model = get_resnetv2() |
distiller = Distiller(student=student_model, teacher=teacher_model) |
distiller.compile( |
optimizer, |
metrics=[keras.metrics.SparseCategoricalAccuracy()], |
distillation_loss_fn=keras.losses.KLDivergence(), |
temperature=TEMPERATURE, |
) |
history = distiller.fit( |
train_ds, |
steps_per_epoch=int(np.ceil(DATASET_NUM_TRAIN_EXAMPLES / BATCH_SIZE)), |
validation_data=validation_ds, |
epochs=30, # This should be at least 1000. |
) |
student = distiller.student |
student_model.compile(metrics=[\"accuracy\"]) |
_, top1_accuracy = student.evaluate(test_ds) |
print(f\"Top-1 accuracy on the test set: {round(top1_accuracy * 100, 2)}%\") |
Epoch 1/30 |
16/16 [==============================] - 74s 3s/step - distillation_loss: 0.0070 - val_sparse_categorical_accuracy: 0.0039 - val_distillation_loss: 0.0061 |
Epoch 2/30 |
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0059 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0061 |
Epoch 3/30 |
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0049 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0060 |
Epoch 4/30 |
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0048 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0060 |
Epoch 5/30 |
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0043 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0060 |
Epoch 6/30 |
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0041 - val_sparse_categorical_accuracy: 0.0108 - val_distillation_loss: 0.0060 |
Epoch 7/30 |
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0038 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0061 |
Epoch 8/30 |
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0040 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0062 |
Epoch 9/30 |
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0039 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0063 |
Epoch 10/30 |
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0035 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0064 |
Epoch 11/30 |
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0041 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0064 |
Epoch 12/30 |
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0039 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0067 |
Epoch 13/30 |
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0039 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0067 |
Epoch 14/30 |
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0036 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0066 |
Epoch 15/30 |
16/16 [==============================] - 37s 2s/step - distillation_loss: 0.0037 - val_sparse_categorical_accuracy: 0.0098 - val_distillation_loss: 0.0065 |
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