def eval_input_fn(params):
data = Data(dataset='WN18', reverse=True)
validation_data = data.get_inputs_and_targets()
ds = validation_data
return ds
def predict_input_fn(params):
batch_size = params["batch_size"]
data = Data(dataset='WN18', reverse=True)
test_data = data.get_inputs_and_targets()
# Take out top 10 samples from test data to make the predictions.
ds = test_data.take(10).batch(batch_size)
return ds
def train_input_fn(params):
"""train_input_fn defines the input pipeline used for training."""
# Retrieves the batch size for the current shard. The # of shards is
# computed according to the input pipeline deployment. See
# `tf.contrib.tpu.RunConfig` for details.
data = Data(dataset='WN18', reverse=True)
train_data = data.get_inputs_and_targets(training=True)
ds = train_data.shuffle(buffer_size=1000).repeat()
return ds
