<reponame>chris4540/DD2430-ds-proj<filename>train_fashion_mnist.py
# from cuml.manifold import TSNE
from trainer.fashion_mnist import SiameseFashionMNISTTrainer
from utils.plot_fashion_minst import plot_embeddings

# hyper_params = dict()
trainer = SiameseFashionMNISTTrainer(
    log_interval=5, lr=1e-2, epochs=10, batch_size=100)
trainer.run()

# embeddings, labels = trainer.map_val_ds_to_emb_space()

# tsne = TSNE(random_state=1, n_iter=1000, metric="euclidean")

# projected_emb = tsne.fit_transform(embeddings)
# fig = plot_embeddings(projected_emb, labels)

# fig.savefig('fashion_mnist.png', bbox_inches='tight')

# from cuml.manifold import TSNE
from trainer.fashion_mnist import SiameseFashionMNISTTrainer
from utils.plot_fashion_minst import plot_embeddings

# hyper_params = dict()
trainer = SiameseFashionMNISTTrainer(
    log_interval=5, lr=1e-2, epochs=10, batch_size=100)
trainer.run()

# embeddings, labels = trainer.map_val_ds_to_emb_space()

# tsne = TSNE(random_state=1, n_iter=1000, metric="euclidean")

# projected_emb = tsne.fit_transform(embeddings)
# fig = plot_embeddings(projected_emb, labels)

# fig.savefig('fashion_mnist.png', bbox_inches='tight')
# from cuml.manifold import TSNE # hyper_params = dict() # embeddings, labels = trainer.map_val_ds_to_emb_space() # tsne = TSNE(random_state=1, n_iter=1000, metric="euclidean") # projected_emb = tsne.fit_transform(embeddings) # fig = plot_embeddings(projected_emb, labels) # fig.savefig('fashion_mnist.png', bbox_inches='tight')