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[tune] Fix linting error (#1777)
This commit is contained in:
Eric Liang
committed by
Robert Nishihara
Robert Nishihara
parent
6b1e592d5c
commit
7c4afa4b04
@@ -5,8 +5,8 @@
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The model comes from: https://zhuanlan.zhihu.com/p/29214791,
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and it gets to about 87% validation accuracy in 100 epochs.
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Note that the scipt requires a machine with 4 GPUs. You
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can set {'gpu': 0} to use CPUs for training, although
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Note that the script requires a machine with 4 GPUs. You
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can set {"gpu": 0} to use CPUs for training, although
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it is less efficient.
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"""
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@@ -16,7 +16,6 @@ import argparse
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import numpy as np
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import tensorflow as tf
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from tensorflow.python.keras.backend import set_session
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from tensorflow.python.keras.datasets import cifar10
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from tensorflow.python.keras.layers import Input, Dense, Dropout, Flatten
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from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D
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@@ -44,9 +43,9 @@ class Cifar10Model(Trainable):
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y_train = tf.keras.utils.to_categorical(y_train, num_classes)
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y_test = tf.keras.utils.to_categorical(y_test, num_classes)
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x_train = x_train.astype('float32')
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x_train = x_train.astype("float32")
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x_train /= 255
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x_test = x_test.astype('float32')
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x_test = x_test.astype("float32")
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x_test /= 255
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return (x_train, y_train), (x_test, y_test)
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@@ -54,29 +53,36 @@ class Cifar10Model(Trainable):
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def _build_model(self, input_shape):
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x = Input(shape=(32, 32, 3))
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y = x
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y = Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu',
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kernel_initializer='he_normal')(y)
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y = Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu',
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kernel_initializer='he_normal')(y)
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y = MaxPooling2D(pool_size=2, strides=2, padding='same')(y)
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y = Convolution2D(
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filters=64, kernel_size=3, strides=1, padding="same",
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activation="relu", kernel_initializer="he_normal")(y)
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y = Convolution2D(
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filters=64, kernel_size=3, strides=1, padding="same",
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activation="relu", kernel_initializer="he_normal")(y)
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y = MaxPooling2D(pool_size=2, strides=2, padding="same")(y)
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y = Convolution2D(filters=128, kernel_size=3, strides=1, padding='same', activation='relu',
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kernel_initializer='he_normal')(y)
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y = Convolution2D(filters=128, kernel_size=3, strides=1, padding='same', activation='relu',
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kernel_initializer='he_normal')(y)
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y = MaxPooling2D(pool_size=2, strides=2, padding='same')(y)
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y = Convolution2D(
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filters=128, kernel_size=3, strides=1, padding="same",
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activation="relu", kernel_initializer="he_normal")(y)
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y = Convolution2D(
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filters=128, kernel_size=3, strides=1, padding="same",
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activation="relu", kernel_initializer="he_normal")(y)
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y = MaxPooling2D(pool_size=2, strides=2, padding="same")(y)
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y = Convolution2D(filters=256, kernel_size=3, strides=1, padding='same', activation='relu',
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kernel_initializer='he_normal')(y)
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y = Convolution2D(filters=256, kernel_size=3, strides=1, padding='same', activation='relu',
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kernel_initializer='he_normal')(y)
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y = MaxPooling2D(pool_size=2, strides=2, padding='same')(y)
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y = Convolution2D(
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filters=256, kernel_size=3, strides=1, padding="same",
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activation="relu", kernel_initializer="he_normal")(y)
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y = Convolution2D(
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filters=256, kernel_size=3, strides=1, padding="same",
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activation="relu", kernel_initializer="he_normal")(y)
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y = MaxPooling2D(pool_size=2, strides=2, padding="same")(y)
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y = Flatten()(y)
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y = Dropout(self.config['dropout'])(y)
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y = Dense(units=10, activation='softmax', kernel_initializer='he_normal')(y)
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y = Dropout(self.config["dropout"])(y)
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y = Dense(
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units=10, activation="softmax", kernel_initializer="he_normal")(y)
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model = Model(inputs=x, outputs=y, name='model1')
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model = Model(inputs=x, outputs=y, name="model1")
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return model
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def _setup(self):
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@@ -85,9 +91,9 @@ class Cifar10Model(Trainable):
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model = self._build_model(x_train.shape[1:])
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opt = tf.keras.optimizers.Adadelta()
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model.compile(loss='categorical_crossentropy',
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model.compile(loss="categorical_crossentropy",
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optimizer=opt,
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metrics=['accuracy'])
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metrics=["accuracy"])
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self.model = model
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def _train(self):
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@@ -95,24 +101,36 @@ class Cifar10Model(Trainable):
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x_test, y_test = self.test_data
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aug_gen = ImageDataGenerator(
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featurewise_center=False, # set input mean to 0 over the dataset
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samplewise_center=False, # set each sample mean to 0
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featurewise_std_normalization=False, # divide inputs by std of the dataset
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samplewise_std_normalization=False, # divide each input by its std
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zca_whitening=False, # apply ZCA whitening
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rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)
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width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
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height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
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horizontal_flip=True, # randomly flip images
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vertical_flip=False, # randomly flip images
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# set input mean to 0 over the dataset
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featurewise_center=False,
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# set each sample mean to 0
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samplewise_center=False,
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# divide inputs by dataset std
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featurewise_std_normalization=False,
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# divide each input by its std
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samplewise_std_normalization=False,
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# apply ZCA whitening
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zca_whitening=False,
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# randomly rotate images in the range (degrees, 0 to 180)
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rotation_range=0,
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# randomly shift images horizontally (fraction of total width)
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width_shift_range=0.1,
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# randomly shift images vertically (fraction of total height)
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height_shift_range=0.1,
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# randomly flip images
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horizontal_flip=True,
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# randomly flip images
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vertical_flip=False,
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)
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aug_gen.fit(x_train)
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gen = aug_gen.flow(x_train, y_train, batch_size=self.config['batch_size'])
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self.model.fit_generator(generator=gen,
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steps_per_epoch=50000 // self.config['batch_size'],
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epochs=self.config['epochs'],
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validation_data=None)
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gen = aug_gen.flow(
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x_train, y_train, batch_size=self.config["batch_size"])
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self.model.fit_generator(
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generator=gen,
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steps_per_epoch=50000 // self.config["batch_size"],
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epochs=self.config["epochs"],
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validation_data=None)
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# loss, accuracy
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_, accuracy = self.model.evaluate(x_test, y_test, verbose=0)
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@@ -120,7 +138,7 @@ class Cifar10Model(Trainable):
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mean_accuracy=accuracy)
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def _save(self, checkpoint_dir):
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file_path = checkpoint_dir + '/model'
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file_path = checkpoint_dir + "/model"
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self.model.save_weights(file_path)
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return file_path
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@@ -130,47 +148,47 @@ class Cifar10Model(Trainable):
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def _stop(self):
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# If need, save your model when exit.
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# saved_path = self.model.save(self.logdir)
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# print('save model at: ', saved_path)
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# print("save model at: ", saved_path)
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pass
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if __name__ == '__main__':
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--smoke-test',
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action='store_true',
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help='Finish quickly for testing')
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parser.add_argument("--smoke-test",
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action="store_true",
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help="Finish quickly for testing")
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args, _ = parser.parse_known_args()
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register_trainable('train_cifar10', Cifar10Model)
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register_trainable("train_cifar10", Cifar10Model)
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train_spec = {
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'run': 'train_cifar10',
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'resources': { 'cpu': 6, 'gpu': 4 },
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'stop': {
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'mean_accuracy': 0.80,
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'timesteps_total': 300,
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"run": "train_cifar10",
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"trial_resources": {"cpu": 1, "gpu": 1},
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"stop": {
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"mean_accuracy": 0.80,
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"timesteps_total": 300,
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},
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'config': {
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'epochs': 1,
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'batch_size': 64,
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'lr': grid_search([10 ** -4, 10 ** -5]),
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'decay': lambda spec: spec.config.lr / 100.0,
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'dropout': grid_search([0.25, 0.5]),
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"config": {
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"epochs": 1,
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"batch_size": 64,
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"lr": grid_search([10 ** -4, 10 ** -5]),
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"decay": lambda spec: spec.config.lr / 100.0,
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"dropout": grid_search([0.25, 0.5]),
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},
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'repeat': 1,
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"repeat": 4,
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}
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if args.smoke_test:
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train_spec['config']['lr'] = 10 ** -4
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train_spec['config']['dropout'] = 0.5
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train_spec["config"]["lr"] = 10 ** -4
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train_spec["config"]["dropout"] = 0.5
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ray.init()
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pbt = PopulationBasedTraining(
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time_attr='timesteps_total', reward_attr='mean_accuracy',
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time_attr="timesteps_total", reward_attr="mean_accuracy",
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perturbation_interval=10,
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hyperparam_mutations={
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'dropout': lambda _: np.random.uniform(0, 1),
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"dropout": lambda _: np.random.uniform(0, 1),
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})
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run_experiments({'pbt_cifar10': train_spec},
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run_experiments({"pbt_cifar10": train_spec},
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scheduler=pbt)
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