This discriminator takes in the list of fake and real images as input and returns a single value between 0 and 1. If the

Remember that the two models are programmed to work against each other. During training, the computer will print out the loss for each of the models. Whichever value has the lowest loss is technically winning the competition. This allows you to see when the balance between both of the models is breaking down.

I hope you have learnt something from this article. My results from this project were quite poor because I did not have the resources to train the GAN properly and the dataset is reasonably small. Try applying this model to other datasets or applications, you will get satisfying results!

Although this is simplifying the process a little — in reality, it really is incredibly easy to get up and running with some of the most cutting-edge models out there (think BERT and GPT-2).

I hope you have learnt something from this article. My results from this project were quite poor because I did not have the resources to train the GAN properly and the dataset is reasonably small. Try applying this model to other datasets or applications, you will get satisfying results!

When using Huggingface’s transformers library, we have the option of implementing it via TensorFlow or PyTorch. We will be covering everything you need to know to get started with the TensorFlow flavor in this article.

def generate_real_samples(dataset, n_samples): ix = randint(0, dataset.shape[0], n_samples) X = dataset[ix] y = ones((n_samples, 1)) return X, y def generate_latent_points(latent_dim, n_samples): x_input = randn(latent_dim * n_samples) x_input = x_input.reshape(n_samples, latent_dim) return x_input

To download and begin working with some of the biggest models out there, including (but not limited to) — BERT, RoBERTa, GPT, GPT-2, XLNet, and HuggingFace’s own DistilBERT and DistilGPT-2 — it takes no more than three lines of code, which look like this:

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ry lonely teenager. Although she had friends she went to school with; No one seemed to know what she did outside of school. Her friends knew she talked to people online, but didn’t know many details about who she was talking to or meeting with people she met online.

This script actually runs the program. For perspective on computation time, I use a windows surface pro.100 epochs takes about 2 hours for a batch_size as defined in the code above.

Not only can we access all of these models with incredible ease, but we can even take advantage of prebuilt transformers for question and answering, sentiment analysis, text summarization, and much more.

def generate_fake_samples(g_model, latent_dim, n_samples): x_input = generate_latent_points(latent_dim, n_samples) X = g_model.predict(x_input) y = zeros((n_samples, 1)) return X, y

latent_dim = 100 d_model = define_discriminator() g_model = define_generator(latent_dim) gan_model = define_gan(g_model, d_model) print(pixels.shape) train(g_model, d_model, gan_model, np.array(pixels), latent_dim) print(pixels)

Despite this, there are no built-in implementations of transformer models in the core TensorFlow or PyTorch frameworks. To use them, you either need to apply for the relevant Ph.D. program, and we’ll see you in three years — or you pip install transformers.

The generator takes in a random point from latent space and takes it as an input. It upscales the latent point to the appropriate shape of 100,100,3, that can then be displayed as an image.

HuggingFace is a company building and maintaining the hugely popular Transformers library. We can easily hit the ground running with the majority of the big, most cutting-edge transformer models available today through this library.