From f94a1d2b28f3805fb8b37c90f969bcfd82a0e1a5 Mon Sep 17 00:00:00 2001 From: YeonwooSung Date: Fri, 23 Apr 2021 20:15:07 +0900 Subject: [PATCH] Update README.md --- README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 8d28695..0b38135 100644 --- a/README.md +++ b/README.md @@ -10,9 +10,9 @@ An implementation of Geoffrey Hinton's paper "How to represent part-whole hierar ```python import torch -from glom_pytorch import Glom +from pyglom import GLOM -model = Glom( +model = GLOM( dim = 512, # dimension levels = 6, # number of levels image_size = 224, # image size @@ -29,9 +29,9 @@ It also gives you access to all the level data across iterations for clustering, ```python import torch -from glom_pytorch import Glom +from pyglom import GLOM -model = Glom( +model = GLOM( dim = 512, # dimension levels = 6, # number of levels image_size = 224, # image size @@ -53,9 +53,9 @@ import torch.nn.functional as F from torch import nn from einops.layers.torch import Rearrange -from glom_pytorch import Glom +from pyglom import GLOM -model = Glom( +model = GLOM( dim = 512, # dimension levels = 6, # number of levels image_size = 224, # image size @@ -85,9 +85,9 @@ You can pass in the state of the column and levels back into the model to contin ```python import torch -from glom_pytorch import Glom +from pyglom import GLOM -model = Glom( +model = GLOM( dim = 512, levels = 6, image_size = 224,