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ufb.py
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ufb.py
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import tensorflow as tf
import numpy as np
import math, random
import matplotlib.pyplot as plt
# (ipython) %matplotlib inline
np.random.seed(1000)
function_to_learn = lambda x: np.sin(x) + 0.1*np.random.randn(*x.shape)
NUM_HIDDEN_NODES = 20
NUM_EXAMPLES = 1000
TRAIN_SPLIT = .8
MINI_BATCH_SIZE = 100
NUM_EPOCHS = 1000
all_x = np.float32(
np.random.uniform(-2*math.pi, 2*math.pi, (1, NUM_EXAMPLES))).T
np.random.shuffle(all_x)
train_size = int(NUM_EXAMPLES*TRAIN_SPLIT)
trainx = all_x[:train_size]
validx = all_x[train_size:]
trainy = function_to_learn(trainx)
validy = function_to_learn(validx)
plt.figure(1)
plt.scatter(trainx, trainy, c='green', label='train')
plt.scatter(validx, validy, c='red', label='validation')
plt.legend()
X = tf.placeholder(tf.float32, [None, 1], name="X")
Y = tf.placeholder(tf.float32, [None, 1], name="Y")
def init_weights(shape, init_method='xavier', xavier_params = (None, None)):
if init_method == 'zeros':
return tf.Variable(tf.zeros(shape, dtype=tf.float32))
elif init_method == 'uniform':
return tf.Variable(tf.random_normal(shape, stddev=0.01, dtype=tf.float32))
else: #xavier
(fan_in, fan_out) = xavier_params
low = -4*np.sqrt(6.0/(fan_in + fan_out)) # {sigmoid:4, tanh:1}
high = 4*np.sqrt(6.0/(fan_in + fan_out))
return tf.Variable(tf.random_uniform(shape, minval=low, maxval=high, dtype=tf.float32))
def model(X, num_hidden=10):
w_h = init_weights([1, num_hidden], 'xavier', xavier_params=(1, num_hidden))
b_h = init_weights([1, num_hidden], 'zeros')
h = tf.nn.sigmoid(tf.matmul(X, w_h) + b_h)
w_o = init_weights([num_hidden, 1], 'xavier', xavier_params=(num_hidden, 1))
b_o = init_weights([1, 1], 'zeros')
return tf.matmul(h, w_o) + b_o
yhat = model(X, NUM_HIDDEN_NODES)
train_op = tf.train.AdamOptimizer().minimize(tf.nn.l2_loss(yhat - Y))
sess = tf.Session()
sess.run(tf.initialize_all_variables())
errors = []
for i in range(NUM_EPOCHS):
for start, end in zip(range(0, len(trainx), MINI_BATCH_SIZE), range(MINI_BATCH_SIZE, len(trainx), MINI_BATCH_SIZE)):
sess.run(train_op, feed_dict={X: trainx[start:end], Y: trainy[start:end]})
mse = sess.run(tf.nn.l2_loss(yhat - validy), feed_dict={X:validx})
errors.append(mse)
if i%100 == 0: print "epoch %d, validation MSE %g" % (i, mse)
plt.plot(errors)
plt.xlabel('#epochs')
plt.ylabel('MSE')
plt.show()