def test_MNISTIter():
# prepare data
get_data.GetMNIST_ubyte()
batch_size = 100
train_dataiter = mx.io.MNISTIter(image="data/train-images-idx3-ubyte",
label="data/train-labels-idx1-ubyte",
data_shape=(784, ),
batch_size=batch_size,
shuffle=1,
flat=1,
silent=0,
seed=10)
# test_loop
nbatch = 60000 / batch_size
batch_count = 0
for batch in train_dataiter:
batch_count += 1
assert (nbatch == batch_count)
# test_reset
train_dataiter.reset()
train_dataiter.iter_next()
label_0 = train_dataiter.getlabel().asnumpy().flatten()
train_dataiter.iter_next()
train_dataiter.iter_next()
train_dataiter.iter_next()
train_dataiter.iter_next()
train_dataiter.reset()
train_dataiter.iter_next()
label_1 = train_dataiter.getlabel().asnumpy().flatten()
assert (sum(label_0 - label_1) == 0)
def get_iters():
# check data
get_data.GetMNIST_ubyte()
batch_size = 100
train_dataiter = mx.io.MNISTIter(image="data/train-images-idx3-ubyte",
label="data/train-labels-idx1-ubyte",
data_shape=(1, 28, 28),
label_name='sm_label',
batch_size=batch_size,
shuffle=True,
flat=False,
silent=False,
seed=10)
val_dataiter = mx.io.MNISTIter(image="data/t10k-images-idx3-ubyte",
label="data/t10k-labels-idx1-ubyte",
data_shape=(1, 28, 28),
label_name='sm_label',
batch_size=batch_size,
shuffle=True,
flat=False,
silent=False)
return train_dataiter, val_dataiter
stride=(2, 2),
pool_type='max')
fl = mx.symbol.Flatten(data=mp2, name="flatten")
fc2 = mx.symbol.FullyConnected(data=fl, name='fc2', num_hidden=10)
softmax = mx.symbol.SoftmaxOutput(data=fc2, name='sm')
num_epoch = 1
model = mx.model.FeedForward(softmax,
mx.cpu(),
num_epoch=num_epoch,
learning_rate=0.1,
wd=0.0001,
momentum=0.9)
# check data
get_data.GetMNIST_ubyte()
train_dataiter = mx.io.MNISTIter(image="data/train-images-idx3-ubyte",
label="data/train-labels-idx1-ubyte",
data_shape=(1, 28, 28),
label_name='sm_label',
batch_size=batch_size,
shuffle=True,
flat=False,
silent=False,
seed=10)
val_dataiter = mx.io.MNISTIter(image="data/t10k-images-idx3-ubyte",
label="data/t10k-labels-idx1-ubyte",
data_shape=(1, 28, 28),
label_name='sm_label',
batch_size=batch_size,
