def test_mnist():
# Gradient check using MNIST
(train_x, _), (_, _) = mnist.load_data()
train_x = train_x / 255 # Normalizing images
# plotter.plot_mnist(train_x, "original") # Show original mnist images
num_img, img_dim, _ = train_x.shape # Get number of images and # pixels per square img
num_features = 500
mnist_in = np.reshape(
train_x, (img_dim * img_dim,
num_img)) # Reshape images to match autoencoder input
ga = Algorithm(x=mnist_in, num_features=num_features, debug=1, pop_size=20)
w_out, best_cost, logs = ga.run()
print(
f"Average time/generation (sec): {sum(logs['times']) / len(logs['times'])}"
)
print(f"Total time to run GA (sec): {logs['times']}")
ae = AutoEncoder(mnist_in, num_features, random_seed=1234, use_gpu=True)
z, _ = ae.psi(w_out)
phi_w_img = ae.phi(w_out) # Calculate phi(W)
new_mnist = z @ phi_w_img # Recreate original images using Z and phi(W)
new_imgs = np.reshape(
new_mnist, train_x.shape) # Reshape new images have original shape
plotter.plot_mnist(new_imgs,
f"{num_features}_features_ga") # Show new images
# print(loss_values)
plotter.plot_loss(logs['min'], "MNIST_Gradient_Loss_Over_Generations")
def test_mnist(num_epochs=None):
# Gradient check using MNIST
(train_x, _), (_, _) = mnist.load_data()
train_x = train_x / 255 # Normalizing images
# plotter.plot_mnist(train_x, "original") # Show original mnist images
num_img, img_dim, _ = train_x.shape # Get number of images and # pixels per square img
learning_rate = 0.5
num_features = 200
loss_values = [] # Keep track of loss values over epochs
loss_values_less = []
loss_diffs = []
w_in = np.random.normal(
size=(img_dim * img_dim,
num_features)) # Generate random W matrix to test
mnist_in = np.reshape(
train_x, (img_dim * img_dim,
num_img)) # Reshape images to match autoencoder input
ae = AutoEncoder(mnist_in, num_features, random_seed=1234, use_gpu=True)
start_time = time.time()
times = []
if num_epochs:
for epoch in range(num_epochs):
w_in, z_grd = do_epoch(ae, w_in, learning_rate, loss_values, times,
loss_values_less, loss_diffs, epoch,
start_time)
else:
epoch_history_check = 5
epoch = 0
loss_avg = 1000
tol = 0.03
while loss_avg > tol:
w_in, z_grd = do_epoch(ae, w_in, learning_rate, loss_values, times,
loss_values_less, loss_diffs, epoch,
start_time)
loss_check = loss_diffs[-epoch_history_check:]
loss_avg = sum(loss_check) / len(loss_check)
epoch += 1
print(
f"Total time to run gradient decent (sec): {time.time() - start_time}")
phi_w_img = ae.phi(w_in) # Calculate phi(W)
new_mnist = z_grd @ phi_w_img # Recreate original images using Z and phi(W)
new_imgs = np.reshape(
new_mnist, train_x.shape) # Reshape new images have original shape
plotter.plot_mnist(new_imgs,
f"{num_features}_features_gradient") # Show new images
# print(loss_values)
plotter.plot_loss(loss_values, "MNIST_Gradient_Loss_Over_Epochs")
plotter.plot_loss(
loss_values_less,
"MNIST_Gradient_Loss_Over_Epochs_all_epochs_except_zero")
def test_cifar10(num_epochs=None):
# (train_x, _), (_, _) = cifar10.load_data()
(_, _), (train_x, _) = cifar10.load_data()
print(train_x.shape)
plotter.plot_mnist(train_x, "original")
train_x = rgb2gray(train_x)
train_x = train_x / 255
plotter.plot_mnist(train_x, "grayscale")
num_img, img_h, img_w = train_x.shape
print(train_x.shape)
learning_rate = 0.5
num_features = 768;
loss_values = []
loss_values_less = []
loss_diffs = []
w_in = np.random.normal(size=(img_h * img_w, num_features))
cifar_in = np.reshape(train_x, (img_h * img_w, num_img))
# cifar_in = np.reshape(train_x, (img_h, img_w, num_img*img_ch))
print(cifar_in.shape)
ae = AutoEncoder(cifar_in, num_features, random_seed=1234, use_gpu=True)
start_time = time.time()
times = []
if num_epochs:
for epoch in range(num_epochs):
w_in, z_grd = do_epoch(ae, w_in, learning_rate, loss_values, times, loss_values_less, loss_diffs, epoch,
start_time)
else:
epoch_history_check = 5
epoch = 0
loss_avg = 1000
tol = 0.03
while loss_avg > tol:
w_in, z_grd = do_epoch(ae, w_in, learning_rate, loss_values, times, loss_values_less, loss_diffs, epoch,
start_time)
loss_check = loss_diffs[-epoch_history_check:]
loss_avg = sum(loss_check) / len(loss_check)
epoch += 1
print(f"Total time to run gradient decent (sec): {time.time() - start_time}")
phi_w_img = ae.phi(w_in) # Calculate phi(W)
new_cifar = z_grd @ phi_w_img # Recreate original images using Z and phi(W)
print(new_cifar.shape)
new_imgs = np.reshape(new_cifar, train_x.shape) # Reshape new images have original shape
plotter.plot_mnist(new_imgs, f"{num_features}_features_gradient") # Show new images
# print(loss_values)
plotter.plot_loss(loss_values, "CIFAR10_Gradient_Loss_Over_Epochs")
plotter.plot_loss(loss_values_less, "CIFAR10_Gradient_Loss_Over_Epochs_all_epochs_except_zero")
# return train_x
return new_imgs
