Ejemplo n.º 1
0
import numpy as np
import singularity as S
from singularity.components import layers, optimizers, regularizers, models
from singularity.utils import datasets

batch_size = 128
nb_epoch = 20

# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = datasets.load_mnist()

X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype(S.floatX())
X_test = X_test.astype(S.floatX())
X_train /= 255
X_test /= 255
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
Y_train = S.categorical(y_train, 10)
Y_test = S.categorical(y_test, 10)

model = models.DeepNetwork()

model.add(layers.InputLayer((None, 28 * 28)))
model.add(layers.DenseLayer(512))
model.add(layers.ActivationLayer(S.relu))
model.add(layers.DenseLayer(484, activation=S.relu))
model.add(layers.DropoutLayer(0.2))
Ejemplo n.º 2
0
from singularity.components.models import *
from singularity.utils import datasets
from singularity.utils import hdf5

batch_size = 16
original_dim = 784
latent_dim = 2
intermediate_dim = 128
 #epsilon_std = 0.01
epsilon_std = 0.01
nb_epoch = 1

# train the VAE on MNIST digits
(x_train, y_train), (x_test, y_test) = datasets.load_mnist()

x_train = x_train.astype(S.floatX()) / 255.
x_test = x_test.astype(S.floatX()) / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))

###################
## SAVE HDF5 TEST #
###################

#Save the model into a hdf5 file format
#hdf5.save("test2.hdf", _x_decoded_mean, "root")

# Load the data settings
_x_decoded_mean = hdf5.load("test2.hdf")