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Sven
60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
90 lines
2.6 KiB
Python
90 lines
2.6 KiB
Python
import argparse
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import numpy as np
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from tensorflow.keras.datasets import mnist
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from ray.tune.integration.keras import TuneReporterCallback
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from ray.tune.examples.utils import get_mnist_data
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--smoke-test", action="store_true", help="Finish quickly for testing")
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args, _ = parser.parse_known_args()
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def train_mnist(config, reporter):
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# https://github.com/tensorflow/tensorflow/issues/32159
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import tensorflow as tf
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import (Dense, Dropout, Flatten, Conv2D,
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MaxPooling2D)
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batch_size = 128
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num_classes = 10
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epochs = 12
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x_train, y_train, x_test, y_test, input_shape = get_mnist_data()
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model = Sequential()
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model.add(
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Conv2D(
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32, kernel_size=(3, 3), activation="relu",
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input_shape=input_shape))
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model.add(Conv2D(64, (3, 3), activation="relu"))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.5))
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model.add(Flatten())
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model.add(Dense(config["hidden"], activation="relu"))
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model.add(Dropout(0.5))
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model.add(Dense(num_classes, activation="softmax"))
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model.compile(
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loss=tf.keras.losses.categorical_crossentropy,
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optimizer=tf.keras.optimizers.SGD(
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lr=config["lr"], momentum=config["momentum"]),
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metrics=["accuracy"])
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model.fit(
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x_train,
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y_train,
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batch_size=batch_size,
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epochs=epochs,
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verbose=0,
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validation_data=(x_test, y_test),
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callbacks=[TuneReporterCallback(reporter)])
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if __name__ == "__main__":
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import ray
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from ray import tune
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from ray.tune.schedulers import AsyncHyperBandScheduler
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mnist.load_data() # we do this on the driver because it's not threadsafe
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ray.init()
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sched = AsyncHyperBandScheduler(
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time_attr="training_iteration",
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metric="mean_accuracy",
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mode="max",
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max_t=400,
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grace_period=20)
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tune.run(
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train_mnist,
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name="exp",
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scheduler=sched,
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stop={
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"mean_accuracy": 0.99,
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"training_iteration": 5 if args.smoke_test else 300
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},
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num_samples=10,
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resources_per_trial={
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"cpu": 2,
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"gpu": 0
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},
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config={
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"threads": 2,
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"lr": tune.sample_from(lambda spec: np.random.uniform(0.001, 0.1)),
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"momentum": tune.sample_from(
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lambda spec: np.random.uniform(0.1, 0.9)),
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"hidden": tune.sample_from(
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lambda spec: np.random.randint(32, 512)),
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})
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