def train_mnist(args):
track.init(trial_name="track-example", trial_config=vars(args))
batch_size = 128
num_classes = 10
epochs = 1 if args.smoke_test else 12
mnist.load()
x_train, y_train, x_test, y_test, input_shape = get_mnist_data()
model = Sequential()
model.add(
Conv2D(32,
kernel_size=(3, 3),
activation="relu",
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(args.hidden, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation="softmax"))
model.compile(loss="categorical_crossentropy",
optimizer=keras.optimizers.SGD(lr=args.lr,
momentum=args.momentum),
metrics=["accuracy"])
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
callbacks=[TuneKerasCallback(track.metric)])
track.shutdown()
def train_mnist(args):
track.init(trial_name="track-example", trial_config=vars(args))
batch_size = 128
num_classes = 10
epochs = 1 if args.smoke_test else 12
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(args.hidden, activation="relu"),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(num_classes, activation="softmax")
])
model.compile(loss="sparse_categorical_crossentropy",
optimizer=keras.optimizers.SGD(lr=args.lr,
momentum=args.momentum),
metrics=["accuracy"])
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
callbacks=[TuneReporterCallback()])
track.shutdown()
def testSessionInitShutdown(self):
self.assertTrue(track._session is None)
# Checks that the singleton _session is created/destroyed
# by track.init() and track.shutdown()
for _ in range(2):
# do it twice to see that we can reopen the session
track.init(trial_name="test_init")
self.assertTrue(track._session is not None)
track.shutdown()
self.assertTrue(track._session is None)
def _trainable_func(self, config, reporter):
track.init(_tune_reporter=reporter)
output = train_func(config)
reporter(**{RESULT_DUPLICATE: True})
track.shutdown()
return output
def tearDown(self):
track.shutdown()
ray.shutdown()
