def generate_layers():
neurons = Path('./neuron1024')
for i in range(nneurons):
l = Path('./neuron1024/n1024-l{}.tsv'.format(str(i + 1)))
with l.open() as f:
print('loading layer: ', i + 1)
yield Matrix.from_tsv(f, lib.GrB_FP32, nneurons, nneurons)
def load_images():
images = Path('sparse-images-1024.tsv')
with images.open() as i:
print('loading images.')
Y0 = Matrix.from_tsv(i, lib.GrB_FP32, nfeatures, nneurons)
return Y0
def test_matrix_tsv_read(tmp_path):
mmf = tmp_path / 'tsv_test.mm'
mmf.touch()
with mmf.open('w') as f:
f.writelines(['3\t3\t3\n', '1\t1\t2\n', '2\t2\t3\n', '3\t3\t4\n'])
with mmf.open() as f:
n = Matrix.from_tsv(f, int, 3, 3)
assert n.to_lists() == [[0, 1, 2], [0, 1, 2], [2, 3, 4]]
def load_layer(neurons, dest, i):
fname = "{}/neuron{}/n{}-l{}.{}"
binfile = fname.format(dest, neurons, neurons, str(i + 1), "ssb")
if Path(binfile).exists():
return Matrix.from_binfile(binfile.encode("ascii"))
l = Path(fname.format(dest, neurons, neurons, str(i + 1), "tsv"))
with l.open() as f:
m = Matrix.from_tsv(f, FP32, neurons, neurons)
m.to_binfile(binfile.encode("ascii"))
return m
def load_images(neurons, dest):
fname = "{}/sparse-images-{}.{}"
binfile = fname.format(dest, neurons, "ssb")
if Path(binfile).exists():
return Matrix.from_binfile(binfile.encode("ascii"))
images = Path(fname.format(dest, neurons, "tsv"))
with images.open() as i:
m = Matrix.from_tsv(i, FP32, NFEATURES, neurons)
m.to_binfile(binfile.encode("ascii"))
return m
def load_layer(i, dest):
l = Path('{}/neuron{}/n{}-l{}.tsv'.format(dest, neurons, neurons, str(i+1)))
with l.open() as f:
return Matrix.from_tsv(f, lib.GrB_FP32, neurons, neurons)
def load_images(neurons, dest):
images = Path('{}/sparse-images-{}.tsv'.format(dest, neurons))
with images.open() as i:
return Matrix.from_tsv(i, lib.GrB_FP32, NFEATURES, neurons)
