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',