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{0: 'tench, Tinca tinca', | |
1: 'goldfish, Carassius auratus', | |
2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', | |
3: 'tiger shark, Galeocerdo cuvieri', | |
4: 'hammerhead, hammerhead shark', | |
5: 'electric ray, crampfish, numbfish, torpedo', | |
6: 'stingray', | |
7: 'cock', | |
8: 'hen', | |
9: 'ostrich, Struthio camelus', |
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class Kernel(Layer): | |
def __init__(self, r, **kwargs): | |
self.r = r | |
super(Kernel, self).__init__(**kwargs) | |
def build(self, input_shape): | |
self.mu = self.add_weight(name='mu', | |
shape=(1, self.r), | |
initializer='uniform', |
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class MultiHeadAttention(nn.Module): | |
def __init__(self, emb_size: int = 768, num_heads: int = 8, dropout: float = 0): | |
super().__init__() | |
self.emb_size = emb_size | |
self.num_heads = num_heads | |
# fuse the queries, keys and values in one matrix | |
self.qkv = nn.Linear(emb_size, emb_size * 3) | |
self.att_drop = nn.Dropout(dropout) | |
self.projection = nn.Linear(emb_size, emb_size) | |
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class PatchEmbedding(nn.Module): | |
def __init__(self, in_channels: int = 3, patch_size: int = 16, emb_size: int = 768): | |
self.patch_size = patch_size | |
super().__init__() | |
self.projection = nn.Sequential( | |
# using a conv layer instead of a linear one -> performance gains | |
nn.Conv2d(in_channels, emb_size, kernel_size=patch_size, stride=patch_size), | |
Rearrange('b e (h) (w) -> b (h w) e'), | |
) | |
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function random_gender() { | |
let roll = Math.random() | |
let gender = roll > 0.5 ? 'He/She' : 'She/He' | |
return gender | |
} | |
function fix_bias() { | |
var biased_node = document.querySelectorAll("#tw-target > div > div > pre > span")[0] | |
var text = biased_node.innerText |
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Test |
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print("Hello world!") |
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for _ in range(5): | |
print("hey!") |
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print("Hello world!") |
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for _ in range(5): | |
print("hey!") |