ntakouris

var cachedToken = string.Empty;

var identityApi = RestService.For<IIdentityApi>("https://localhost:5001");
var tweetbookApi = RestService.For<ITweetbookApi>("https://localhost:5001", new RefitSettings
{
    AuthorizationHeaderValueGetter = () => Task.FromResult(cachedToken)
});

var registerResponse = await identityApi.RegisterAsync(new UserRegistrationRequest
{

ntakouris

public interface IIdentityApi
{
    [Post("/api/v1/identity/register")]
    Task<ApiResponse<AuthSuccessResponse>> RegisterAsync([Body] UserRegistrationRequest registrationRequest);

    [Post("/api/v1/identity/login")]
    Task<ApiResponse<AuthSuccessResponse>> LoginAsync([Body] UserLoginRequest loginRequest);

    [Post("/api/v1/identity/refersh")]
    Task<ApiResponse<AuthSuccessResponse>> RefreshAsync([Body] RefreshTokenRequest refreshRequest);

ntakouris

[Post("/api/v1/identity/register")]
Task<ApiResponse<AuthSuccessResponse>> RegisterAsync([Body] UserRegistrationRequest registrationRequest);

ntakouris

var response = await _tweetBookApi.LoginAsync(new LoginRequest("usrname", "password"))

ntakouris

- [x]  inverted dropout
- [x]  goldilocks ideal rate // bullshit
- [x]  shannons entropy measure // good one
- [x]  harmonic mean
- [x]  beta distribution
- [x]  gamma function
- [x]  bias correction of exponentially weighted average
- [x]  covariate shifting computer vision
- [x]  why use strided convolution
- [x]  selu

ntakouris

Nigger 18
in 10038
the 24574
house 222
"Yes" 2
I 30610
have 6830
a 22865
hard 469
on 6489

ntakouris

class Textgen(Sequence):

        def __init__(self, input_t, target_t, batch_size):
            self.input_t, self.target_t = input_t, target_t
            self.batch_size = batch_size

        def __len__(self):
            return math.floor(len(self.input_t) / self.batch_size) - 1

        def __getitem__(self, idx):

ntakouris

def generator():
    i = 0
    encoder_input_data = np.zeros(
        (batch_size, max_encoder_seq_length, num_encoder_tokens),
        dtype='float32')
    decoder_input_data = np.zeros(
        (batch_size, max_decoder_seq_length, num_decoder_tokens),
        dtype='float32')
    decoder_target_data = np.zeros(
        (batch_size, max_decoder_seq_length, num_decoder_tokens),

ntakouris

public class TagResponseExample : IExamplesProvider<TagResponse>
{
    public TagResponse GetExamples()
    {
        return new TagResponse
        {
            Name = "new tag"
        };
    }
}

ntakouris

public class CreateTagRequestExample : IExamplesProvider<CreateTagRequest>
{
    public CreateTagRequest GetExamples()
    {
        return new CreateTagRequest
        {
            TagName = "new tag"
        };
    }
}