np.random.seed(123)
tf.set_random_seed(345)
print("Loading MNIST data . . . ")
(X_train, Y_train), (X_test,
Y_test) = datasets.load_mnist(n_classes=10,
p=0.2,
map_into_unit_range=True)
print("Flattening images . . . ")
X_train = X_train.reshape((-1, 784))
X_test = X_test.reshape((-1, 784))
print("Initializing kernel . . . ")
dim_input = X_train.shape[1]
kernel = kernels.NNGP(dim_input, v_b=1.0, v_w=1.0, depth=1)
print("Initializing NNGP model . . . ")
model = models.GPSGD(X_train, Y_train, kernel, minibatch_size=100)
model.likelihood.variance = 0.01
print("Training model . . . ")
accs = model.train(eval_set=(X_test, Y_test), epochs=10)
print("Saving accuracies . . . ")
np.save("results/mnist_5_accs.npy", accs)
print("Saving model parameters . . . ")
model.save_params("results/mnist_5_params.npz")
print("Done!")
# split into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=456)
# input dimension
dim_input = X_train.shape[1]
# instantiate an NNGP kernel
# and pass the input dimension as an argument
kernel = kernels.NNGP(dim_input, depth=3)
# instantiate GPSGD regressor
model = models.GPSGD(X_train, y_train, kernel, minibatch_size=5)
# set the variance of the Gaussian likelihood (variance of additive noise)
model.likelihood.variance = 0.01
#model.kern.v_b.trainable = False
#model.kern.v_w.trainable = False
# by default, the kernel hyperparameters are optimized during training
# uncommenting the next line fixes the hyperparameters to their initial values
#model.feature.set_trainable(False)
# print hyperparameter shapes and values before training
#print("Model hyperparameters before training:\n")
#print(model)
#print("\n")