def plot_batch_probabilities():
image_size = [28, 28]
train_imgs, train_lbls, test_imgs, test_lbls = read_mnist(dim=image_size,
n_train=60000,
n_test=10000)
rbm = RestrictedBoltzmannMachine(ndim_visible=image_size[0] *
image_size[1],
ndim_hidden=200,
is_bottom=True,
image_size=image_size,
is_top=False,
n_labels=10,
batch_size=20)
rbm.cd1(visible_trainset=train_imgs, n_iterations=10000, calc_err=False)
batch_input = test_imgs[:rbm.batch_size]
p_h_given_v, _ = rbm.get_h_given_v(batch_input)
image = p_h_given_v * 255
image = Image.fromarray(image.astype(np.uint8))
image.save('batch_probs_20_batch.png', format='PNG')
rbm = RestrictedBoltzmannMachine(ndim_visible=image_size[0] *
image_size[1],
ndim_hidden=500,
is_bottom=True,
image_size=image_size,
is_top=False,
n_labels=10,
batch_size=20)
rbm.weight_vh = np.load("trained_rbm/weights_100_20.npy")
rbm.bias_v = np.load("trained_rbm/bias_v_100_20..npy")
rbm.bias_h = np.load("trained_rbm/bias_h_100_20..npy")
fig, axs = plt.subplots(10, 2, figsize=(12, 12))
plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
# print("MIN WEIGHT", weights.min(), "MAX WEIGHT", weights.max())
for i in range(10):
image = train_imgs[i]
v0 = np.random.binomial(1, 0.2, (784, ))
vk = image.copy()
for _ in range(1):
ph0, h0 = rbm.get_h_given_v(vk)
pvk, vk = rbm.get_v_given_h(h0)
axs[i, 0].imshow(image.reshape((28, 28)))
axs[i, 1].imshow(pvk.reshape((28, 28)))
plt.savefig("hist/recon.png")
plt.show()
plt.close('all')