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svhn.py
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# coding: utf-8
from __future__ import division, print_function, unicode_literals, absolute_import
import tensorflow as tf
import numpy as np
import math
FLAGS = tf.flags.FLAGS
def get_weights(name, shape, stddev):
weights = tf.get_variable(name, shape=shape, initializer=tf.truncated_normal_initializer(stddev=stddev))
return weights
def get_biases(name, shape, init):
biases = tf.get_variable(name, shape=shape, initializer=tf.constant_initializer(init))
return biases
def _variable_on_cpu(name, shape, initializer):
"""Helper to create a Variable stored on CPU memory.
Args:
name: name of the variable
shape: list of ints
initializer: initializer for Variable
Returns:
Variable Tensor
"""
with tf.device('/cpu:0'):
var = tf.get_variable(name, shape, initializer=initializer)
return var
def _variable_with_weight_decay(name, shape, stddev, wd):
"""Helper to create an initialized Variable with weight decay.
Note that the Variable is initialized with a truncated normal distribution.
A weight decay is added only if one is specified.
Args:
name: name of the variable
shape: list of ints
stddev: standard deviation of a truncated Gaussian
wd: add L2Loss weight decay multiplied by this float. If None, weight
decay is not added for this Variable.
Returns:
Variable Tensor
"""
var = _variable_on_cpu(name, shape,
tf.truncated_normal_initializer(stddev=stddev))
if wd is not None:
weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
return var
def inference(images, dropout=True):
"""Build the CNN model.
Args:
images: Images returned from distorted_inputs() or inputs().
dropout: Boolean controlling whether to use dropout or not
Returns:
Logits
"""
if FLAGS.dataset == 'mnist':
first_conv_shape = [5, 5, 1, 64]
else:
first_conv_shape = [5, 5, 3, 64]
# conv1
with tf.variable_scope('conv1') as scope:
kernel = get_weights('kernel', first_conv_shape, 1e-4)
conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
# biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))
biases = get_biases('biases', [64], 0.0)
bias = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(bias, name=scope.name)
# if dropout:
# conv1 = tf.nn.dropout(conv1, 0.3, seed=42)
# pool1
pool1 = tf.nn.max_pool(conv1,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool1')
# norm1
norm1 = tf.nn.lrn(pool1,
4,
bias=1.0,
alpha=0.001 / 9.0,
beta=0.75,
name='norm1')
# conv2
with tf.variable_scope('conv2') as scope:
kernel = get_weights('weights', shape=[5, 5, 64, 128], stddev=1e-4)
# kernel = _variable_with_weight_decay('weights',
# shape=[5, 5, 64, 128],
# stddev=1e-4,
# wd=0.0)
conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME')
# biases = _variable_on_cpu('biases', [128], tf.constant_initializer(0.1))
biases = get_biases('biases', [128], 0.1)
bias = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(bias, name=scope.name)
# if dropout:
#conv2 = tf.nn.dropout(conv2, 0.3, seed=42)
# norm2
norm2 = tf.nn.lrn(conv2,
4,
bias=1.0,
alpha=0.001 / 9.0,
beta=0.75,
name='norm2')
# pool2
pool2 = tf.nn.max_pool(norm2,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool2')
# local3
with tf.variable_scope('local3') as scope:
# Move everything into depth so we can perform a single matrix multiply.
# reshape = tf.reshape(pool2, [FLAGS.batch_size, -1])
reshape = tf.contrib.layers.flatten(pool2)
dim = reshape.get_shape()[1].value
weights = get_weights('weights', shape=[dim, 384], stddev=0.04)
# weights = _variable_with_weight_decay('weights',
# shape=[dim, 384],
# stddev=0.04,
# wd=0.004)
# biases = _variable_on_cpu('biases', [384], tf.constant_initializer(0.1))
biases = get_biases('biases', [384], 0.1)
local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
if dropout:
local3 = tf.nn.dropout(local3, 0.5, seed=42)
# local4
with tf.variable_scope('local4') as scope:
# weights = _variable_with_weight_decay('weights',
# shape=[384, 192],
# stddev=0.04,
# wd=0.004)
weights = get_weights('weights', shape=[384, 192], stddev=0.04)
# biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1))
biases = get_biases('biases', [192], 0.1)
local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name=scope.name)
if dropout:
local4 = tf.nn.dropout(local4, 0.5, seed=42)
# compute logits
with tf.variable_scope('softmax_linear') as scope:
# weights = _variable_with_weight_decay('weights',
# [192, FLAGS.nb_labels],
# stddev=1/192.0,
# wd=0.0)
weights = get_weights('weights', shape=[192, FLAGS.nb_labels], stddev=1/192.0)
# biases = _variable_on_cpu('biases',
# [FLAGS.nb_labels],
# tf.constant_initializer(0.0))
biases = get_biases('biases', [FLAGS.nb_labels], 0.0)
logits = tf.add(tf.matmul(local4, weights), biases, name=scope.name)
return logits
def loss_fun(logits, y):
'''
y is one-hot vector
'''
zeros = tf.zeros_like(logits, dtype=logits.dtype)
cond = (logits >= zeros)
relu_logits = tf.where(cond, logits, zeros)
neg_abs_logits = tf.where(cond, -logits, logits)
# hk = 25
# deltaf = 10 * 2
# epsilon = FLAGS.epsilon
# batch_size = FLAGS.batch_size
# scale = deltaf / (epsilon * batch_size)
# noise = np.random.laplace(0.0, scale, 10)
# noise = np.reshape(noise, [10])
# y = y + noise
# y = (1-FLAGS.label_ratio)/10 + FLAGS.label_ratio*y
# loss = tf.add(relu_logits - logits * y, math.log(2.0) + 0.5*neg_abs_logits + 1.0 / 8.0 * neg_abs_logits**2, name='noise_loss')
loss = tf.add(relu_logits - logits * y, tf.log(1 + tf.exp(neg_abs_logits)))
return loss