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Autoencoder.py
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Autoencoder.py
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import numpy as np
import math
class Autoencoder(object):
def __init__(self, input, n_visible=784, n_hidden=100, W=None, bhid=None, bvis=None, rng=None):
self.n_visible = n_visible
self.hidden = n_hidden
np.random.seed(0)
if W is None:
W = np.random.uniform(-math.sqrt(6.0 / (n_visible + n_hidden)), math.sqrt(6.0 / (n_visible + n_hidden)), \
[n_visible, n_hidden])
if bhid is None:
bhid = np.zeros((1, n_hidden))
if bvis is None:
bvis = np.zeros((1, n_visible))
if rng is None:
rng = np.random.RandomState(1234)
self.X = input
self.W1 = W
self.W2 = self.W1.T
self.b1= bhid
self.b2 = bvis
self.rng = rng
def sigmoid(self, x):
return 1. / (1 + np.exp(-x))
def get_corrupted_input(self, input, dropout):
return np.random.choice(2, input.shape, p=[dropout, 1-dropout])*input
def get_cost_update(self, lr=0.01, batch_size=10, dropout=0.25):
indice = np.random.permutation(3000)
for j in xrange(1, (3000 / batch_size) + 1):
row = indice[(j - 1) * batch_size:j * batch_size]
X_batch = self.X[row]
# forward propagation
# by setting dropout = 0: autoencoder
# by setting dropout > 0: denoising autoencoder
tilde_x = self.get_corrupted_input(X_batch, dropout)
a1 = np.dot(tilde_x, self.W1) + self.b1
h1 = self.sigmoid(a1)
a2 = np.dot(h1, self.W2) + self.b2
o = self.sigmoid(a2)
# backpropagation
a2_grad = -(self.sigmoid(1-a2)*X_batch - self.sigmoid(a2)*(1-X_batch))/batch_size
W2_grad = np.dot(h1.T, a2_grad)
b2_grad = np.sum(a2_grad, axis=0, keepdims=True)
h1_grad = np.dot(a2_grad, self.W2.T)
a1_grad = h1_grad * h1 * (1-h1)
W_grad = np.dot(X_batch.T, a1_grad)
b_grad = np.sum(a1_grad, axis=0, keepdims=True)
self.W2 -= lr * W2_grad
self.b2 -= lr * b2_grad
self.W1 -= lr * W_grad
self.b1 -= lr * b_grad
def get_reconstruction_cost(self, input):
a1 = np.dot(input, self.W1) + self.b1
h1 = self.sigmoid(a1)
a2 = np.dot(h1, self.W2) + self.b2
o = self.sigmoid(a2)
L = -(np.sum(input * np.log(o)) + np.sum((1 - input) * np.log(1 - o)))/input.shape[0]
return L
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*874
return X
def draw_weights(weight):
import matplotlib.pyplot as plt
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_multi_error_fig(epochs, train_error1, dev_error1, train_error5, dev_error5, train_error10, dev_error10, train_error20, dev_error20):
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot(epochs, train_error1, 'orange', label='Train h=50')
ax.plot(epochs, dev_error1, 'brown', label='Val h=50')
ax.plot(epochs, train_error5, 'g', label='Train h=100')
ax.plot(epochs, dev_error5, 'b', label='Val h=100')
ax.plot(epochs, train_error10, 'r', label='Train h=200')
ax.plot(epochs, dev_error10, 'c', label='Val h=200')
ax.plot(epochs, train_error20, 'y', label='Train h=500')
ax.plot(epochs, dev_error20, 'k', label='Val h=500')
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 train_autoencoder(learning_rate=0.1, max_epochs=50, batch_size=10, n_hidden=100, dropout=0):
X_train = load_data("digitstrain.txt")
X_dev = load_data("digitsvalid.txt")
rng = np.random.RandomState(123)
epochs, train_error, dev_error = [], [], []
autoencoder = Autoencoder(X_train, rng=rng, n_hidden=n_hidden)
epochs = []
train_error, dev_error = [], []
for epoch in xrange(1, max_epochs + 1):
autoencoder.get_cost_update(dropout=dropout)
epochs.append(epoch)
train_cost = autoencoder.get_reconstruction_cost(X_train)
dev_cost = autoencoder.get_reconstruction_cost(X_dev)
train_error.append(train_cost)
dev_error.append(dev_cost)
print "Train epoch %d, cross-entropy %f" % (epoch, train_cost)
#draw_weights(autoencoder.W1)
# np.savetxt('dauto_weight.txt', autoencoder.W1)
return [epochs, train_error, dev_error]
if __name__ == "__main__":
train_autoencoder(dropout=0)
# epochs, train_error_h50, dev_error_h50 = train_autoencoder(n_hidden=50)
# epochs, train_error_h100, dev_error_h100 = train_autoencoder(n_hidden=100)
# epochs, train_error_h200, dev_error_h200 = train_autoencoder(n_hidden=200)
# epochs, train_error_h500, dev_error_h500 = train_autoencoder(n_hidden=500)
# draw_multi_error_fig(epochs, train_error_h50, dev_error_h50, train_error_h100, dev_error_h100, train_error_h200, \
# dev_error_h200, train_error_h500, dev_error_h500)