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lab-10-8-mnist_nn_selu(wip).py
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lab-10-8-mnist_nn_selu(wip).py
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# Lab 10 MNIST and Dropout
# SELU implementation from https://github.com/bioinf-jku/SNNs/blob/master/selu.py
import tensorflow as tf
import random
# import matplotlib.pyplot as plt
# -*- coding: utf-8 -*-
'''
Tensorflow Implementation of the Scaled ELU function and Dropout
'''
import numbers
from tensorflow.contrib import layers
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import array_ops
from tensorflow.python.layers import utils
from tensorflow.examples.tutorials.mnist import input_data
tf.set_random_seed(777) # reproducibility
def selu(x):
with ops.name_scope('elu') as scope:
alpha = 1.6732632423543772848170429916717
scale = 1.0507009873554804934193349852946
return scale*tf.where(x>=0.0, x, alpha*tf.nn.elu(x))
def dropout_selu(x, keep_prob, alpha= -1.7580993408473766, fixedPointMean=0.0, fixedPointVar=1.0,
noise_shape=None, seed=None, name=None, training=False):
"""Dropout to a value with rescaling."""
def dropout_selu_impl(x, rate, alpha, noise_shape, seed, name):
keep_prob = 1.0 - rate
x = ops.convert_to_tensor(x, name="x")
if isinstance(keep_prob, numbers.Real) and not 0 < keep_prob <= 1:
raise ValueError("keep_prob must be a scalar tensor or a float in the "
"range (0, 1], got %g" % keep_prob)
keep_prob = ops.convert_to_tensor(keep_prob, dtype=x.dtype, name="keep_prob")
keep_prob.get_shape().assert_is_compatible_with(tensor_shape.scalar())
alpha = ops.convert_to_tensor(alpha, dtype=x.dtype, name="alpha")
keep_prob.get_shape().assert_is_compatible_with(tensor_shape.scalar())
if tensor_util.constant_value(keep_prob) == 1:
return x
noise_shape = noise_shape if noise_shape is not None else array_ops.shape(x)
random_tensor = keep_prob
random_tensor += random_ops.random_uniform(noise_shape, seed=seed, dtype=x.dtype)
binary_tensor = math_ops.floor(random_tensor)
ret = x * binary_tensor + alpha * (1-binary_tensor)
a = tf.sqrt(fixedPointVar / (keep_prob *((1-keep_prob) * tf.pow(alpha-fixedPointMean,2) + fixedPointVar)))
b = fixedPointMean - a * (keep_prob * fixedPointMean + (1 - keep_prob) * alpha)
ret = a * ret + b
ret.set_shape(x.get_shape())
return ret
with ops.name_scope(name, "dropout", [x]) as name:
return utils.smart_cond(training,
lambda: dropout_selu_impl(x, keep_prob, alpha, noise_shape, seed, name),
lambda: array_ops.identity(x))
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# Check out https://www.tensorflow.org/get_started/mnist/beginners for
# more information about the mnist dataset
# parameters
learning_rate = 0.001
training_epochs = 50
batch_size = 100
# input place holders
X = tf.placeholder(tf.float32, [None, 784])
Y = tf.placeholder(tf.float32, [None, 10])
# dropout (keep_prob) rate 0.7 on training, but should be 1 for testing
keep_prob = tf.placeholder(tf.float32)
# weights & bias for nn layers
# http://stackoverflow.com/questions/33640581/how-to-do-xavier-initialization-on-tensorflow
W1 = tf.get_variable("W1", shape=[784, 512],
initializer=tf.contrib.layers.xavier_initializer())
b1 = tf.Variable(tf.random_normal([512]))
L1 = selu(tf.matmul(X, W1) + b1)
L1 = dropout_selu(L1, keep_prob=keep_prob)
W2 = tf.get_variable("W2", shape=[512, 512],
initializer=tf.contrib.layers.xavier_initializer())
b2 = tf.Variable(tf.random_normal([512]))
L2 = selu(tf.matmul(L1, W2) + b2)
L2 = dropout_selu(L2, keep_prob=keep_prob)
W3 = tf.get_variable("W3", shape=[512, 512],
initializer=tf.contrib.layers.xavier_initializer())
b3 = tf.Variable(tf.random_normal([512]))
L3 = selu(tf.matmul(L2, W3) + b3)
L3 = dropout_selu(L3, keep_prob=keep_prob)
W4 = tf.get_variable("W4", shape=[512, 512],
initializer=tf.contrib.layers.xavier_initializer())
b4 = tf.Variable(tf.random_normal([512]))
L4 = selu(tf.matmul(L3, W4) + b4)
L4 = dropout_selu(L4, keep_prob=keep_prob)
W5 = tf.get_variable("W5", shape=[512, 10],
initializer=tf.contrib.layers.xavier_initializer())
b5 = tf.Variable(tf.random_normal([10]))
hypothesis = tf.matmul(L4, W5) + b5
# define cost/loss & optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
logits=hypothesis, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# initialize
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# train my model
for epoch in range(training_epochs):
avg_cost = 0
total_batch = int(mnist.train.num_examples / batch_size)
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
feed_dict = {X: batch_xs, Y: batch_ys, keep_prob: 0.7}
c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)
avg_cost += c / total_batch
print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))
print('Learning Finished!')
# Test model and check accuracy
correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print('Accuracy:', sess.run(accuracy, feed_dict={
X: mnist.test.images, Y: mnist.test.labels, keep_prob: 1}))
# Get one and predict
r = random.randint(0, mnist.test.num_examples - 1)
print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
print("Prediction: ", sess.run(
tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1], keep_prob: 1}))
# plt.imshow(mnist.test.images[r:r + 1].
# reshape(28, 28), cmap='Greys', interpolation='nearest')
# plt.show()
'''
Epoch: 0001 cost = 0.447322626
Epoch: 0002 cost = 0.157285590
Epoch: 0003 cost = 0.121884535
Epoch: 0004 cost = 0.098128681
Epoch: 0005 cost = 0.082901778
Epoch: 0006 cost = 0.075337573
Epoch: 0007 cost = 0.069752543
Epoch: 0008 cost = 0.060884363
Epoch: 0009 cost = 0.055276413
Epoch: 0010 cost = 0.054631256
Epoch: 0011 cost = 0.049675195
Epoch: 0012 cost = 0.049125314
Epoch: 0013 cost = 0.047231930
Epoch: 0014 cost = 0.041290121
Epoch: 0015 cost = 0.043621063
Learning Finished!
Accuracy: 0.9804
'''