User Guide#
Installation#
To use EvoAug-TF, first install it using pip:
pip install evoaug-tf
Example#
Import evoaug:
import os
from evoaug_tf import evoaug, augment
import tensorflow as tf
from tensorflow import keras
Define PyTorch model and modeling choices:
model_func = "DEFINE KERAS MODEL"
Train model with augmentations:
input_shape = (L,A) #<-- DEFINE L, A and input_shape should be first arguments to model_func (eg. model = model_func(input_shape))
augment_list = [
augment.RandomDeletion(delete_min=0, delete_max=30),
augment.RandomRC(rc_prob=0.5),
augment.RandomInsertion(insert_min=0, insert_max=20),
augment.RandomTranslocation(shift_min=0, shift_max=20),
augment.RandomNoise(noise_mean=0, noise_std=0.3),
augment.RandomMutation(mutate_frac=0.05)
]
model = evoaug.RobustModel(model_func, input_shape, augment_list=augment_list, max_augs_per_seq=1, hard_aug=True)
model.compile(keras.optimizers.Adam(learning_rate=0.001, weight_decay=1e-6), #weight_decay
loss='mse',
metrics=[Spearman, pearson_r]) # additional track metric
# set up callbacks
es_callback = keras.callbacks.EarlyStopping(monitor='val_loss',
patience=10,
verbose=1,
mode='min',
restore_best_weights=True)
reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
min_lr=1e-7,
mode='min',
verbose=1)
save_path = os.path.join(output_dir, exp_name+"_aug.h5")
# pre-train model with augmentations
model.fit(x_train, y_train,
epochs=100,
batch_size=100,
shuffle=True,
validation_data=(x_valid, y_valid),
callbacks=[es_callback, reduce_lr])
model.save_weights(save_path)
Fine-tune model without augmentations:
# set up fine-tuning
finetune_optimizer = keras.optimizers.Adam(learning_rate=0.0001, weight_decay=1e-6)
model.finetune_mode(optimizer=finetune_optimizer)
# set up callbacks
es_callback = keras.callbacks.EarlyStopping(monitor='test_pearson_r (Dev)',
patience=5,
verbose=1,
mode='max',
restore_best_weights=True)
save_path = os.path.join(output_dir, exp_name+"_finetune.h5")
# train model
model.fit(x_train, y_train,
epochs=finetune_epochs,
batch_size=batch_size,
shuffle=True,
validation_data=(x_valid, y_valid),
callbacks=[es_callback])
model.save_weights(save_path)
Examples on Google Colab#
Example analysis:
https://colab.research.google.com/drive/1sCYAL133F1PPbn7aGOxeQTFW-6fpLo4r?usp=sharing
Example Ray Tune with Population Based Training:
https://colab.research.google.com/drive/1NG8DrELTdmZPOw0RmaeNky0DZ5m2jpXY?usp=sharing
Example Ray Tune with Asynchronous Hyperband Algorithm:
https://colab.research.google.com/drive/1mzKeXKSfkEfe9o-P-MhqQokLoW7Dv-Jk?usp=sharing