def create_random_input():
"""
Create a simple dataset where output = input_1 * 2.0
"""
datasets = collections.OrderedDict()
datasets['simple'] = {
'train':
torch.utils.data.DataLoader(utils.NumpyDatasets(input_1=np.random.rand(
10, 100), ),
batch_size=10,
shuffle=False),
'valid':
torch.utils.data.DataLoader(utils.NumpyDatasets(input_1=np.random.rand(
10, 100), ),
batch_size=10,
shuffle=False)
}
return datasets
def inputs_fn():
datasets = collections.OrderedDict()
datasets['dataset_1'] = {
'train':
torch.utils.data.DataLoader(utils.NumpyDatasets(
var_x1=np.asarray([[-1, -1], [1, -1], [-1, 1], [1, 1]]),
var_y=np.asarray([0, 1, 1, 0])),
batch_size=100)
}
return datasets
def create_simple_regression():
"""
Create a simple dataset where output = input_1 * 2.0
"""
datasets = collections.OrderedDict()
datasets['simple'] = {
'train':
torch.utils.data.DataLoader(utils.NumpyDatasets(
input_1=np.array([[1.0], [2.0], [3.0], [4.0], [5.0]]),
output=np.array([[2.0], [4.0], [6.0], [8.0], [10.0]])),
batch_size=100)
}
return datasets