def _input_fn() -> Tuple[tf.Tensor, tf.Tensor]:
data, labels = xor_data()
dataset = tf.data.Dataset.from_tensor_slices((data, labels))
dataset = context.wrap_dataset(dataset)
if shuffle:
dataset = dataset.shuffle(1000)
def map_dataset(x, y):
return {"input": x}, y
dataset = dataset.batch(batch_size)
dataset = dataset.map(map_dataset)
return dataset
def make_dataset() -> tf.data.Dataset:
data, labels = xor_data()
ds = tf.data.Dataset.from_tensor_slices((data, labels))
return ds
"learning_rate": 0.1,
"global_batch_size": 4,
"trial_type": "default",
}
}
context = init(
config=config, local=args.local, test=args.test, context_dir=str(pathlib.Path.cwd())
)
model = Sequential()
model.add(Dense(context.get_hparam("hidden_size"), activation="sigmoid", input_shape=(2,)))
model.add(Dense(1))
if args.use_dataset:
data, labels = utils.xor_data()
train = context.wrap_dataset(tf.data.Dataset.from_tensor_slices((data, labels)))
train = train.batch(context.get_hparam("global_batch_size"))
valid = context.wrap_dataset(tf.data.Dataset.from_tensor_slices((data, labels)))
valid = valid.batch(context.get_hparam("global_batch_size"))
else:
train, valid = utils.make_xor_data_sequences(batch_size=4)
model = context.wrap_model(model)
model.compile(
SGD(lr=context.get_hparam("learning_rate")),
binary_crossentropy,
metrics=[categorical_error],
)
model.fit(x=train, steps_per_epoch=100, validation_data=valid, workers=0)
