Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import time | |
import mlx.core as mx | |
import mlx.nn as nn | |
from dataclasses import dataclass | |
from typing import Dict, Optional, Tuple, Union | |
@dataclass | |
class ModelArgs: |
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import numpy as np | |
def fleiss_kappa(ratings): | |
""" | |
Args: | |
ratings: An N x R numpy array. N is the number of | |
samples and R is the number of reviewers. Each | |
entry (n, r) is the category assigned to example | |
n by reviewer r. | |
Returns: |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
""" | |
Author: Awni Hannun | |
This is an example CTC decoder written in Python. The code is | |
intended to be a simple example and is not designed to be | |
especially efficient. | |
The algorithm is a prefix beam search for a model trained | |
with the CTC loss function. |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import time | |
import torch | |
import torch.nn as nn | |
from torch.autograd import Variable | |
def attend_bmm(eh, dhx): | |
dhx = dhx.unsqueeze(1) | |
pax = torch.bmm(eh, dhx.transpose(1,2)).squeeze(dim=2) | |
ax = nn.functional.softmax(pax) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import torch | |
import torch.autograd as autograd | |
import numpy as np | |
np.random.seed(11) | |
for size in range(1, 2000, 1): | |
a = np.random.randint(0, 2, size).astype(np.uint8) | |
av = autograd.Variable(torch.ByteTensor(a)) |