Tensorflow: Deep Multi-Layer Pecptron with Xavier Initializer

Xavier initializer

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data

hidden_layer_size = [256]*4
input_layer_size = 784
output_layer_size = 10

mnist = input_data.read_data_sets('data/', one_hot=True)
train_img = mnist.train.images
train_lbl = mnist.train.labels
test_img  = mnist.test.images
test_lbl  = mnist.test.labels

def xavier_init(n_inputs, n_outputs, uniform=True):
if uniform:
init_range = tf.sqrt(6.0 / (n_inputs+ n_outputs))
return tf.random_uniform_initializer(-init_range, init_range)
else:
stddev = tf.sqrt(3.0 / (n_inputs + n_outputs))
#stddev = tf.sqrt(1.0 / (2 * n_inputs))
return tf.truncated_normal_initializer(stddev=stddev)

def initial_weights(ils, hls, ols):
weights,bias = {}, {}

for i in xrange(len(hls)+1):
fan_in = ils if i==0 else hls[i-1]
fan_out = ols if i==len(hls) else hls[i]
print fan_in, fan_out
#stddev = np.sqrt(1.0 / (1 * fan_in)) # Glorot et al. 2010
stddev = np.sqrt(1.0 / (2 * fan_in)) # He et al. 2015 for relu
weights[i] = tf.Variable(tf.random_normal([fan_in, fan_out], stddev=stddev))
#         weights[i] = tf.get_variable(name=str(i), shape=[fan_in, fan_out], dtype=tf.float32,
#                                      initializer=xavier_init(fan_in, fan_out, uniform=False))
bias[i] = tf.Variable(tf.random_normal([fan_out]))
return weights, bias

def mlp(_x, _w, _b, _keep_prob):
layers = {}
for i in xrange(len(_w)):
if i == 0:
layers[i] = tf.nn.dropout(tf.nn.relu(tf.add(tf.matmul(_x, _w[i]), _b[i])), _keep_prob)
elif i < len(_w)-1:
layers[i] = tf.nn.dropout(tf.nn.relu(tf.add(tf.matmul(layers[i-1], _w[i]), _b[i])), _keep_prob)
else:
layers[i] = tf.add(tf.matmul(layers[i-1], _w[i]), _b[i])
return layers[len(_w) - 1]

weights, bias = initial_weights(input_layer_size, hidden_layer_size, output_layer_size)
x = tf.placeholder(tf.float32, [None, input_layer_size], name='input')
y = tf.placeholder(tf.float32, [None, output_layer_size], name='output')
dropout_keep_prob = tf.placeholder(tf.float32)

score = mlp(x, weights, bias, dropout_keep_prob)
prob = tf.nn.softmax(score)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(score, y))

lr = 0.001
optimizer = tf.train.AdamOptimizer(lr).minimize(loss)
# optimizer = tf.train.GradientDescentOptimizer(lr).minimize(loss)
pred = tf.equal(tf.argmax(prob, 1), tf.argmax(y,1))
acc = tf.reduce_mean(tf.cast(pred, tf.float32))

init = tf.initialize_all_variables()

epoch = 100
batch_size = 200
snapshot = 5

sess = tf.Session()
with tf.Session() as sess:
sess.run(init)
loss_cache = []
acc_cache = []
for ep in xrange(epoch):
num_batch = mnist.train.num_examples/batch_size
avg_loss, avg_acc = 0, 0
for nb in xrange(num_batch):
batch_x, batch_y = mnist.train.next_batch(batch_size)
out = sess.run([optimizer, acc, loss], feed_dict={x:batch_x, y:batch_y, dropout_keep_prob:0.7})
avg_loss += out[2]/num_batch
avg_acc  += out[1]/num_batch

loss_cache.append(avg_loss)
acc_cache.append(avg_acc)
if ep % snapshot ==0:
print 'Epoch: %d, loss: %.4f, acc: %.4f'%(ep, avg_loss, acc_cache[-1])

print 'test accuracy:' , acc.eval({x:test_img, y:test_lbl, dropout_keep_prob:1.0})

plt.figure(1)
plt.plot(range(len(loss_cache)), loss_cache, 'b-', label='loss')
plt.legend(loc = 'upper right')

plt.figure(2)
plt.plot(range(len(acc_cache)), acc_cache, 'o-', label='acc')
plt.legend(loc = 'lower right')
plt.show()

# Epoch: 0, loss: 0.7072, acc: 0.7610
# Epoch: 5, loss: 0.1153, acc: 0.9661
# Epoch: 10, loss: 0.0751, acc: 0.9776
# Epoch: 15, loss: 0.0595, acc: 0.9819
# Epoch: 20, loss: 0.0473, acc: 0.9849
# Epoch: 25, loss: 0.0410, acc: 0.9876
# Epoch: 30, loss: 0.0394, acc: 0.9881
# Epoch: 35, loss: 0.0342, acc: 0.9896
# Epoch: 40, loss: 0.0303, acc: 0.9904
# Epoch: 45, loss: 0.0284, acc: 0.9910
# Epoch: 50, loss: 0.0258, acc: 0.9916
# Epoch: 55, loss: 0.0292, acc: 0.9911
# Epoch: 60, loss: 0.0238, acc: 0.9928
# Epoch: 65, loss: 0.0259, acc: 0.9924
# Epoch: 70, loss: 0.0223, acc: 0.9931
# Epoch: 75, loss: 0.0212, acc: 0.9937
# Epoch: 80, loss: 0.0225, acc: 0.9937
# Epoch: 85, loss: 0.0200, acc: 0.9939
# Epoch: 90, loss: 0.0208, acc: 0.9940
# Epoch: 95, loss: 0.0206, acc: 0.9936
# test accuracy: 0.9823