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