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RBM.py
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RBM.py
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from __future__ import division
import numpy as np
import math
import matplotlib.pyplot as plt
def binarize_data(input):
threshold, upper, lower = 0.5, 1, 0
input = np.where(input >= threshold, upper, lower)
return input
def load_data(file):
data = np.loadtxt(file, dtype='float', delimiter=',')
X = data[:, :-1]
X = binarize_data(X) # 3000*784
return X
def sigmoid(X):
return 1 / (1+np.exp(-X))
def init_w(insize, outsize):
a = math.sqrt(6.0 / (insize + outsize))
return np.random.uniform(-a, a, size=(insize, outsize))
def init_b(size):
return np.zeros((size, 1))
def gibbs_sampling(v, model, gs_steps):
W, vbias, hbias = model['W'], model['vbias'], model['hbias']
h = None
for i in xrange(gs_steps):
h = sigmoid(W.T.dot(v) + hbias)
h = np.random.binomial(1, h)
v = sigmoid(W.dot(h) + vbias)
v = np.random.binomial(1, v)
return [h, v]
def get_cross_entropy(X, model):
W, vbias = model['W'], model['vbias']
h, _ = gibbs_sampling(X, model, 1)
v = sigmoid(W.dot(h) + vbias)
cross_entropy = -np.mean(np.sum(X * np.log(v) + (1 - X) * np.log(1 - v), axis=0))
return cross_entropy
def train_RBM(trainset=None, devset=None, seed=2, num_hidden=100, epochs=100, learning_rate=0.01,
batch_size=10, K=100, gs_step=1):
np.random.seed(seed)
X_train = load_data(trainset)
X_dev = load_data(devset)
# transpose input
X_train = X_train.T # 784 * 3000
X_dev = X_dev.T
insize = X_train.shape[0]
model = {}
W = init_w(insize, num_hidden) # 784 * 100
vbias = init_b(insize).reshape(-1, 1)
hbias = init_b(num_hidden)
model['W'], model['vbias'], model['hbias'] = W, vbias, hbias
# persistent chains
v = np.random.binomial(1, 0.5, (insize, K))
train_error, dev_error = [], []
for epoch in xrange(epochs):
train_cross_entropy = get_cross_entropy(X_train, model)
dev_cross_entropy = get_cross_entropy(X_dev, model)
print 'Epoch %d: train %f dev %f' % (epoch, train_cross_entropy, dev_cross_entropy)
train_error.append(train_cross_entropy)
dev_error.append(dev_cross_entropy)
for i in xrange(int(X_train.shape[1]/batch_size)):
rows = np.random.permutation(X_train.shape[1])[:batch_size]
X_batch = X_train[:, rows] # mini-batch
_, v = gibbs_sampling(v, model, gs_step)
# CD-k
# _, v = gibbs_sampling(X_batch, model, gs_step)
h_X_batch = sigmoid(np.dot(W.T, X_batch) + hbias)
h_v = sigmoid(hbias + np.dot(W.T, v) + hbias)
# update model
W += learning_rate * (X_batch.dot(h_X_batch.T)/X_batch.shape[1] - v.dot(h_v.T)/v.shape[1])
vbias += learning_rate * (np.sum(X_batch, axis=1, keepdims=True)/X_batch.shape[1] - np.sum(v, axis=1, keepdims=True)/v.shape[1])
hbias += learning_rate * (np.sum(h_X_batch, axis=1, keepdims=True)/h_X_batch.shape[1] - np.sum(h_v, axis=1, keepdims=True)/h_v.shape[1])
# draw cross entropy of train and dev
draw_error_fig(epochs, train_error, dev_error)
draw_weights(W)
draw_samples(insize, K, model)
def draw_error_fig(epochs, train_error, dev_error):
fig, ax = plt.subplots()
epochs = [i for i in xrange(epochs)]
ax.plot(epochs, train_error, 'g', label='Train Cross Entropy')
ax.plot(epochs, dev_error, 'b', label='Validation Cross Entropy')
ax.set_xlabel("number of epoches")
ax.set_ylabel("avg cross-entropy error")
legend = ax.legend(loc='upper right', shadow=False)
for label in legend.get_texts():
label.set_fontsize('small')
plt.show()
def draw_weights(weight):
W = np.transpose(weight)
W = np.reshape(W, (-1,28,28))
fig = plt.figure()
for i in xrange(W.shape[0]):
plt.subplot(10, 10, i+1)
plt.axis('off')
plt.imshow(W[i],cmap=plt.cm.binary)
plt.show()
def draw_samples(insize, K, model):
v = np.random.binomial(1, 0.5, (insize, K))
h, v = gibbs_sampling(v, model, 1000)
draw_weights(v)
if __name__ == "__main__":
train_file = "digitstrain.txt"
val_file = "digitsvalid.txt"
test_file = "digitstest.txt"
train_RBM(trainset=train_file, devset=val_file, batch_size=10, num_hidden=100)