Generative Adversarial Network for MNIST Dataset #11961
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require descriptive names
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| # Creating Generator and Discriminator for GAN | ||
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| class discriminator(nn.Module): |
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Class names should follow the CamelCase naming convention. Please update the following name accordingly: discriminator
| # Creating Generator and Discriminator for GAN | ||
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| class discriminator(nn.Module): | ||
| def __init__(self,input_size,output_size,hidden_dim): |
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Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide type hint for the parameter: input_size
Please provide type hint for the parameter: output_size
Please provide type hint for the parameter: hidden_dim
| #dropout layer | ||
| self.dropout = nn.Dropout(0.2) | ||
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| def forward(self,x): |
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Please provide return type hint for the function: forward. If the function does not return a value, please provide the type hint as: def function() -> None:
As there is no test file in this pull request nor any test function or class in the file computer_vision/Generative_Adversarial_Network_MNIST.py, please provide doctest for the function forward
Please provide type hint for the parameter: x
Please provide descriptive name for the parameter: x
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| return x_out | ||
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| class generator(nn.Module): |
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Class names should follow the CamelCase naming convention. Please update the following name accordingly: generator
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| class generator(nn.Module): | ||
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| def __init__(self, input_size, output_size,hidden_dim): |
There was a problem hiding this comment.
Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide type hint for the parameter: input_size
Please provide type hint for the parameter: output_size
Please provide type hint for the parameter: hidden_dim
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| # Compute the discriminator losses on real images | ||
| # use smoothed labels | ||
| D_real = D(real_images) |
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Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: D_real
| fake_images = G(z) | ||
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| # Compute the discriminator losses on fake images | ||
| D_fake = D(fake_images) |
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Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: D_fake
| fake_images = G(z) | ||
| # Compute the discriminator losses on fake images | ||
| # using flipped labels! | ||
| D_fake = D(fake_images) |
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Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: D_fake
| # Print some loss stats | ||
| if batch_i % print_every == 0: | ||
| # print discriminator and generator loss | ||
| print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.format( |
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As mentioned in the Contributing Guidelines, please do not use printf style formatting or str.format(). Use f-string instead to be more readable and efficient.
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| #Viewing the results of the GAN | ||
| def view_samples(epoch, samples): |
There was a problem hiding this comment.
Please provide return type hint for the function: view_samples. If the function does not return a value, please provide the type hint as: def function() -> None:
As there is no test file in this pull request nor any test function or class in the file computer_vision/Generative_Adversarial_Network_MNIST.py, please provide doctest for the function view_samples
Please provide type hint for the parameter: epoch
Please provide type hint for the parameter: samples
for more information, see https://pre-commit.ci
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Closing require_type_hints PRs to prepare for Hacktoberfest |
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