def main():
from mindspore import nn
# dataset
if os.path.exists(args.data_path):
dataset = ems.create_mnist_dataset(args.data_path,
is_train=(not parser.do_eval),
batch_size=args.batch_size)
else:
dataset = ems.create_mocker_dataset()
ems.print_ds_info(dataset)
# network
network = ems.LeNet5()
# loss & opt
loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
optimizer = nn.Momentum(network.trainable_params(), 0.01, 0.9)
# train
ems.Trainer('lenet', parser) \
.set_dataset(dataset) \
.set_network(network) \
.set_loss_fn(loss_fn) \
.set_optimizer(optimizer) \
.set_callbacks([ems.TimeMonitor()]) \
.run()
def test_03_ds_print_func():
ds = create_dataset()
print(">>> print_ds_info:")
print_ds_info(ds)
print(">>> print_ds_performance:")
print_ds_performance(ds)
print(">>> print_ds_data:")
print_ds_data(ds)
self.samples.append([
np.random.rand(3, 4, 5).astype(np.float32),
np.random.randint(10, size=()).astype(np.int32)
])
def __len__(self):
return len(self.samples)
# define dataset
ds = de.GeneratorDataset(source=BaseDataset(),
column_names=['image', 'label'],
num_shards=num_shards,
shard_id=shard_id)
# map ops
ds = ds.map(input_columns=["image"],
operations=lambda img: img,
num_parallel_workers=8)
# batch & repeat
ds = ds.batch(batch_size=batch_size, drop_remainder=False)
ds = ds.repeat(count=1)
return ds
if __name__ == '__main__':
dataset = create_dataset()
ems.print_ds_info(dataset)
ems.print_ds_data(dataset)