def load_block_graph(self, suffix):
bhash = self.hash
prefix = self.chain.block_path / bhash[-2] / bhash[-1]
datafile = prefix / f"{self.number}_{bhash}_{suffix}.ssb"
if not datafile.exists():
return maximal_matrix(UINT64)
return Matrix.from_binfile(bytes(datafile))
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
