Feednite | Fortnite news RSS Feed generator | Artificial Intelligence library

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-> images (htype = image)

import torch
from torch import nn, optim
import numpy as np
from matplotlib import pyplot as plt

class Regression(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = nn.Linear(1, 32)
        self.linear2 = nn.Linear(32, 16)
        self.linear3 = nn.Linear(16*2, 1)

    def forward(self, x):
        x1, x2 = torch.tensor_split(x,2)
        x1 = nn.functional.relu(self.linear1(x1))
        x1 = nn.functional.relu(self.linear2(x1))

        x2 = nn.functional.relu(self.linear1(x2))
        x2 = nn.functional.relu(self.linear2(x2))
        cat_x = torch.cat([x1, x2], dim=1)

        cat_x = self.linear3(cat_x)
        return cat_x

def train(model, optimizer, E, iteration, x, y):
    losses = []
    for i in range(iteration):
        optimizer.zero_grad()                   # 勾配情報を0に初期化
        y_pred = model(x)                       # 予測
        loss = E(y_pred.reshape(y.shape), y)    # 損失を計算(shapeを揃える)
        loss.backward()                         # 勾配の計算
        optimizer.step()                        # 勾配の更新
        losses.append(loss.item())              # 損失値の蓄積
        print('epoch=', i+1, 'loss=', loss)
    return model, losses

x = np.random.uniform(0, 10, 100)                                   # x軸をランダムで作成
y = np.random.uniform(0.9, 1.1, 100) * np.sin(2 * np.pi * 0.1 * x)  # 正弦波を作成
x = torch.from_numpy(x.astype(np.float32)).float()                  # xをテンソルに変換
y = torch.from_numpy(y.astype(np.float32)).float()                  # yをテンソルに変換
X = torch.stack([torch.ones(100), x], 1)   

net = Regression()

optimizer = optim.RMSprop(net.parameters(), lr=0.01)                # 最適化にRMSpropを設定
E = nn.MSELoss()                                                    # 損失関数にMSEを設定

net, losses = train(model=net, optimizer=optimizer, E=E, iteration=5000, x=X, y=y)

/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in linear(input, weight, bias)
   1846     if has_torch_function_variadic(input, weight, bias):
   1847         return handle_torch_function(linear, (input, weight, bias), input, weight, bias=bias)
-> 1848     return torch._C._nn.linear(input, weight, bias)
   1849 
   1850 

RuntimeError: mat1 and mat2 shapes cannot be multiplied (50x2 and 1x32)

const Alan = () => {

    useEffect(() => {
        alanBtn({
            key: alanKey,
            onCommand: ({ command }) => {
                if (command === 'testCommand') {
                    alert('This code was executed');
                }
            }
        })
    }, [])

    return (
        

        
    );
};

from feat import Detector

face_model = "retinaface"
landmark_model = "mobilenet"
au_model = "rf"
emotion_model = "resmasknet"
detector = Detector(face_model=face_model, landmark_model=landmark_model, au_model=au_model,
                    emotion_model=emotion_model)

if __name__ == '__main__':
    pass

C:\Users\User\AppData\Roaming\Python\Python39\site-packages\nilearn\input_data\__init__.py:27: FutureWarning: The import path 'nilearn.input_data' is deprecated in version 0.9. Importing from 'nilearn.input_data' will be possible at least until release 0.13.0. Please import from 'nilearn.maskers' instead.
  warnings.warn(message, FutureWarning)
Loading Face Detection model:  retinaface
Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\mobilenet0.25_Final.pth
Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\mobilenet_224_model_best_gdconv_external.pth.tar
Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\hog_pca_all_emotio.joblib
Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\hog_pca_all_emotio.joblib
Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\hog_scalar_aus.joblib
Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\RF_568.joblib
Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\hog_pca_all_emotio.joblib
Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\hog_scalar_aus.joblib
Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\ResMaskNet_Z_resmasking_dropout1_rot30.pth
Loading Face Landmark model:  mobilenet
Loading au model:  rf
Loading emotion model:  resmasknet
Traceback (most recent call last):
  File "C:\Users\User\Desktop\DetectFEXFromVideos\main.py", line 7, in 
    detector = Detector(face_model=face_model, landmark_model=landmark_model, au_model=au_model,
  File "C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\detector.py", line 227, in __init__
    self.emotion_model = ResMaskNet()
  File "C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\emo_detectors\ResMaskNet\resmasknet_test.py", line 748, in __init__
    torch.load(
  File "C:\Users\User\AppData\Roaming\Python\Python39\site-packages\torch\serialization.py", line 713, in load
    return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
  File "C:\Users\User\AppData\Roaming\Python\Python39\site-packages\torch\serialization.py", line 938, in _legacy_load
    typed_storage._storage._set_from_file(
RuntimeError: unexpected EOF, expected 32606425 more bytes. The file might be corrupted.

Process finished with exit code 1

Uncaught (in promise) TypeError: cannot resolve operator 'Cast' with opsets: ai.onnx v11

import spacy
nlp = spacy.load('en_core_web_md')

test_data = "Hermione bought a car, then both Hermione and Hagrid raced it on the track. Tom Brady was very happy with Hagrid this year."

doc = nlp(test_data)
for ent in doc.ents:
        print(ent.text, ent.start_char, ent.end_char, ent.label_)