def test_bayesian_binary_mnist(self):
# Create a astroNN neural network instance and set the basic parameter
net = MNIST_BCNN()
net.task = 'binary_classification'
net.callbacks = ErrorOnNaN()
net.max_epochs = 1
net.train(x_train, y_train)
pred, pred_err = net.test(x_test)
test_num = y_test.shape[0]
net.save('mnist_binary_bcnn_test')
net_reloaded = load_folder("mnist_binary_bcnn_test")
net_reloaded.mc_num = 3
prediction_loaded, prediction_loaded_err = net_reloaded.test(x_test)
def test_bayesian_binary_mnist(self):
# Create a astroNN neural network instance and set the basic parameter
net = MNIST_BCNN()
net.task = 'binary_classification'
net.callbacks = ErrorOnNaN()
net.max_epochs = 1 # Just use 1 epochs for quick result
# Train the neural network
net.train(self.x_train[:200], self.y_train[:200])
net.save('mnist_binary_bcnn_test')
net_reloaded = load_folder("mnist_binary_bcnn_test")
net_reloaded.mc_num = 3 # prevent memory issue on Tavis CI
prediction_loaded = net_reloaded.test(self.x_test[:200])
def test_bayesian_binary_mnist(self):
(x_train, y_train), (x_test, y_test) = mnist.load_data()
y_train = utils.to_categorical(y_train, 10)
y_train = y_train.astype(np.float32)
x_train = x_train.astype(np.float32)
x_test = x_test.astype(np.float32)
# Create a astroNN neural network instance and set the basic parameter
net = MNIST_BCNN()
net.task = 'binary_classification'
net.callbacks = ErrorOnNaN()
net.max_epochs = 1 # Just use 5 epochs for quick result
# Trian the nerual network
net.train(x_train[:200], y_train[:200])
net.save('mnist_binary_bcnn_test')
net_reloaded = load_folder("mnist_binary_bcnn_test")
net_reloaded.mc_num = 3 # prevent memory issue on Tavis CI
prediction_loaded = net_reloaded.test(x_test[:200])
def test_bayesian_mnist(self):
from astroNN.models import MNIST_BCNN
import pylab as plt
(x_train, y_train), (x_test, y_test) = mnist.load_data()
y_train = utils.to_categorical(y_train, 10)
# To convert to desirable type
y_train = y_train.astype(np.float32)
x_train = x_train.astype(np.float32)
x_test = x_test.astype(np.float32)
# Create a astroNN neural network instance and set the basic parameter
net = MNIST_BCNN()
net.task = 'classification'
net.callbacks = ErrorOnNaN()
net.max_epochs = 1 # Just use 5 epochs for quick result
# Trian the nerual network
net.train(x_train[:200], y_train[:200])
plt.close() # Travis-CI memory error??
# net.plot_model() # disable due to memory issue on Travis CI
net.save('mnist_bcnn_test')
net.plot_dense_stats()
net.evaluate(x_train[:10], y_train[:10])
net_reloaded = load_folder("mnist_bcnn_test")
net_reloaded.mc_num = 3 # prevent memory issue on Tavis CI
prediction_loaded = net_reloaded.test(x_test[:200])
net_reloaded.folder_name = None # set to None so it can be saved
net_reloaded.save()
load_folder(
net_reloaded.folder_name) # ignore pycharm warning, its not None
def test_bayesian_mnist(self):
import pylab as plt
# Create a astroNN neural network instance and set the basic parameter
net = MNIST_BCNN()
net.task = 'classification'
net.callbacks = ErrorOnNaN()
net.max_epochs = 1
# Train the neural network
net.train(x_train, y_train)
net.save('mnist_bcnn_test')
net.plot_dense_stats()
plt.close() # Travis-CI memory error??
net.evaluate(x_test, utils.to_categorical(y_test, 10))
pred, pred_err = net.test(x_test)
test_num = y_test.shape[0]
assert (np.sum(pred == y_test)) / test_num > 0.9 # assert accuracy
net_reloaded = load_folder("mnist_bcnn_test")
net_reloaded.mc_num = 3 # prevent memory issue on Tavis CI
prediction_loaded = net_reloaded.test(x_test[:200])
net_reloaded.folder_name = None # set to None so it can be saved
net_reloaded.save()
load_folder(net_reloaded.folder_name) # ignore pycharm warning, its not None
