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RBM.py
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RBM.py
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import numpy as np
import torch
import random
from tqdm import trange
class RBM:
def __init__(self, n_visible, n_hidden, lr=0.001, epochs=5, mode='bernoulli', batch_size=32, k=3, optimizer='adam', gpu=False, savefile=None, early_stopping_patience=5):
self.mode = mode # bernoulli or gaussian RBM
self.n_hidden = n_hidden # Number of hidden nodes
self.n_visible = n_visible # Number of visible nodes
self.lr = lr # Learning rate for the CD algorithm
self.epochs = epochs # Number of iterations to run the algorithm for
self.batch_size = batch_size
self.k = k
self.optimizer = optimizer
self.beta_1=0.9
self.beta_2=0.999
self.epsilon=1e-07
self.m = [0, 0, 0]
self.v = [0, 0, 0]
self.m_batches = {0:[], 1:[], 2:[]}
self.v_batches = {0:[], 1:[], 2:[]}
self.savefile = savefile
self.early_stopping_patience = early_stopping_patience
self.stagnation = 0
self.previous_loss_before_stagnation = 0
self.progress = []
if torch.cuda.is_available() and gpu==True:
dev = "cuda:0"
else:
dev = "cpu"
self.device = torch.device(dev)
# Initialize weights and biases
std = 4 * np.sqrt(6. / (self.n_visible + self.n_hidden))
self.W = torch.normal(mean=0, std=std, size=(self.n_hidden, self.n_visible))
self.vb = torch.zeros(size=(1, self.n_visible), dtype=torch.float32)
self.hb = torch.zeros(size=(1, self.n_hidden), dtype=torch.float32)
self.W = self.W.to(self.device)
self.vb = self.vb.to(self.device)
self.hb = self.hb.to(self.device)
def sample_h(self, x):
wx = torch.mm(x, self.W.t())
activation = wx + self.hb
p_h_given_v = torch.sigmoid(activation)
if self.mode == 'bernoulli':
return p_h_given_v, torch.bernoulli(p_h_given_v)
else:
return p_h_given_v, torch.add(p_h_given_v, torch.normal(mean=0, std=1, size=p_h_given_v.shape))
def sample_v(self, y):
wy = torch.mm(y, self.W)
activation = wy + self.vb
p_v_given_h =torch.sigmoid(activation)
if self.mode == 'bernoulli':
return p_v_given_h, torch.bernoulli(p_v_given_h)
else:
return p_v_given_h, torch.add(p_v_given_h, torch.normal(mean=0, std=1, size=p_v_given_h.shape))
def adam(self, g, epoch, index):
self.m[index] = self.beta_1 * self.m[index] + (1 - self.beta_1) * g
self.v[index] = self.beta_2 * self.v[index] + (1 - self.beta_2) * torch.pow(g, 2)
m_hat = self.m[index] / (1 - np.power(self.beta_1, epoch)) + (1 - self.beta_1) * g / (1 - np.power(self.beta_1, epoch))
v_hat = self.v[index] / (1 - np.power(self.beta_2, epoch))
return m_hat / (torch.sqrt(v_hat) + self.epsilon)
def update(self, v0, vk, ph0, phk, epoch):
dW = (torch.mm(v0.t(), ph0) - torch.mm(vk.t(), phk)).t()
dvb = torch.sum((v0 - vk), 0)
dhb = torch.sum((ph0 - phk), 0)
if self.optimizer == 'adam':
dW = self.adam(dW, epoch, 0)
dvb = self.adam(dvb, epoch, 1)
dhb = self.adam(dhb, epoch, 2)
self.W += self.lr * dW
self.vb += self.lr * dvb
self.hb += self.lr * dhb
def train(self, dataset):
dataset = dataset.to(self.device)
learning = trange(self.epochs, desc=str('Starting...'))
for epoch in learning:
train_loss = 0
counter = 0
for batch_start_index in range(0, dataset.shape[0]-self.batch_size, self.batch_size):
vk = dataset[batch_start_index:batch_start_index+self.batch_size]
v0 = dataset[batch_start_index:batch_start_index+self.batch_size]
ph0, _ = self.sample_h(v0)
for k in range(self.k):
_, hk = self.sample_h(vk)
_, vk = self.sample_v(hk)
phk, _ = self.sample_h(vk)
self.update(v0, vk, ph0, phk, epoch+1)
train_loss += torch.mean(torch.abs(v0-vk))
counter += 1
self.progress.append(train_loss.item()/counter)
details = {'epoch': epoch+1, 'loss': round(train_loss.item()/counter, 4)}
learning.set_description(str(details))
learning.refresh()
if train_loss.item()/counter > self.previous_loss_before_stagnation and epoch>self.early_stopping_patience+1:
self.stagnation += 1
if self.stagnation == self.early_stopping_patience-1:
learning.close()
print("Not Improving the stopping training loop.")
break
else:
self.previous_loss_before_stagnation = train_loss.item()/counter
self.stagnation = 0
learning.close()
if self.savefile is not None:
model = {'W':self.W, 'vb':self.vb, 'hb':self.hb}
torch.save(model, self.savefile)
def load_rbm(self, savefile):
loaded = torch.load(savefile)
self.W = loaded['W']
self.vb = loaded['vb']
self.hb = loaded['hb']
self.W = self.W.to(self.device)
self.vb = self.vb.to(self.device)
self.hb = self.hb.to(self.device)
def trial_dataset():
dataset = []
for _ in range(1000):
t = []
for _ in range(10):
if random.random()>0.75:
t.append(0)
else:
t.append(1)
dataset.append(t)
for _ in range(1000):
t = []
for _ in range(10):
if random.random()>0.75:
t.append(1)
else:
t.append(0)
dataset.append(t)
dataset = np.array(dataset, dtype=np.float32)
np.random.shuffle(dataset)
dataset = torch.from_numpy(dataset)
return dataset
if __name__ == '__main__':
dataset = trial_dataset()
rbm = RBM(10, 100, epochs=50, mode='bernoulli', lr=0.001, optimizer='adam', gpu=True, savefile='save_example.pt', early_stopping_patience=50)
print("Before Training:", rbm.vb)
rbm.train(dataset)
print("After Training:", rbm.vb)
rbm.load_rbm('save_example.pt')
print("After Loading:", rbm.vb)