import numpy as np from vulcanai.net import Network import theano.tensor as T from vulcanai.utils import get_one_hot from vulcanai import mnist_loader from vulcanai.model_tests import run_test (train_images, train_labels, test_images, test_labels) = mnist_loader.load_fashion_mnist() train_labels = get_one_hot(train_labels) input_var = T.fmatrix('input') y = T.fmatrix('truth') network_dense_config = { 'mode': 'dense', 'units': [512], 'dropouts': [0.2], } dense_net = Network(name='3_dense_test', dimensions=[None] + list(train_images.shape[1:]), input_var=input_var, y=y, config=network_dense_config,
label_map = { '0': 'T-shirt/top', '1': 'Trouser', '2': 'Pullover', '3': 'Dress', '4': 'Coat', '5': 'Sandal', '6': 'Shirt', '7': 'Sneaker', '8': 'Bag', '9': 'Ankle boot' } display_tsne(train_images[:1000], train_labels[:1000], label_map) train_labels = get_one_hot(train_labels) test_labels = get_one_hot(test_labels) train_images = np.reshape(train_images, (train_images.shape[0], 28, 28)) test_images = np.reshape(test_images, (test_images.shape[0], 28, 28)) input_var = T.tensor4('input') y = T.fmatrix('truth') network_conv_config = { 'mode': 'conv', 'filters': [16, 32], 'filter_size': [[5, 5], [5, 5]], 'stride': [[1, 1], [1, 1]], 'pool': { 'mode': 'average_exc_pad',