# coding: utf-8
import tensorflow as tf
import utils
from tensorflow.python.training import moving_averages
FLAGS = utils.FLAGS
num_classes = utils.num_classes
class LSTMOCR(object):
def __init__(self, mode):
self.mode = mode
# 图像输入
self.inputs = tf.placeholder(tf.float32, [None, FLAGS.image_height, FLAGS.image_width, FLAGS.image_channel])
# ctc_loss 需要的是稀疏矩阵
self.labels = tf.sparse_placeholder(tf.int32)
# 一维数组，大小[batch_size]
self.seq_len = tf.placeholder(tf.int32, [None])
# l2
self._extra_train_ops = [] # 存储调整平滑均值，平滑方差的操作
def build_graph(self):
self._build_model()
self._build_train_op()
self.merged_summay = tf.summary.merge_all()
def _build_model(self):
"""
构建模型，前两个卷积的卷积核size 分别是7，5 是很重要的，换成其他的效果会差很多
"""
filters = [32, 64, 128, 128, FLAGS.max_stepsize]
strides = [1, 2]
with tf.variable_scope('cnn'):
with tf.variable_scope('unit-0'):
x = self._conv2d(self.inputs, 'cnn-0', 7, 1, filters[0], strides[0]) # 卷积
x = self._batch_norm('bn0', x) # 批标准化
x = self._leaky_relu(x, 0.01) # 非线性激活
x = self._max_pool(x, 2, strides[0]) # 池化
with tf.variable_scope('unit-1'):
x = self._conv2d(x, 'cnn-1', 5, filters[0], filters[1], strides[0])
x = self._batch_norm('bn1', x)
x = self._leaky_relu(x, 0.01)
x = self._max_pool(x, 2, strides[1])
with tf.variable_scope('unit-2'):
x = self._conv2d(x, 'cnn-2', 3, filters[1], filters[2], strides[0])
x = self._batch_norm('bn2', x)
x = self._leaky_relu(x, 0.01)
x = self._max_pool(x, 2, strides[1])
with tf.variable_scope('unit-3'):
x = self._conv2d(x, 'cnn-3', 3, filters[2], filters[3], strides[0])
x = self._batch_norm('bn3', x)
x = self._leaky_relu(x, 0.01)
x = self._max_pool(x, 2, strides[1])
with tf.variable_scope('unit-4'):
x = self._conv2d(x, 'cnn-4', 3, filters[3], filters[4], strides[0])
x = self._batch_norm('bn4', x)
x = self._leaky_relu(x, 0.01)
x = self._max_pool(x, 2, strides[1])
with tf.variable_scope('lstm'):
shp = x.get_shape().as_list()
x = tf.reshape(x, [-1, filters[4], shp[1]*shp[2]])
# tf.nn.rnn_cell.RNNCell, tf.nn.rnn_cell.GRUCell
cell = tf.contrib.rnn.LSTMCell(FLAGS.num_hidden, state_is_tuple=True)
if self.mode == 'train':
cell = tf.contrib.rnn.DropoutWrapper(cell=cell, output_keep_prob=0.8)
cell1 = tf.contrib.rnn.LSTMCell(FLAGS.num_hidden, state_is_tuple=True)
if self.mode == 'train':
cell1 = tf.contrib.rnn.DropoutWrapper(cell=cell1, output_keep_prob=0.8)
# 将rnn堆成2层深度
stack = tf.contrib.rnn.MultiRNNCell([cell, cell1], state_is_tuple=True)
# outputs是所有step的结果， state 是最后一个step的结果这里不需要
outputs, _ = tf.nn.dynamic_rnn(stack, x, self.seq_len, dtype=tf.float32)
# reshape 使其满足模型的step长度
outputs = tf.reshape(outputs, [-1, FLAGS.num_hidden])
W = tf.get_variable(name='W',
shape=[FLAGS.num_hidden, num_classes],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
b = tf.get_variable(name='b',
shape=[num_classes],
dtype=tf.float32,
initializer=tf.constant_initializer())
self.logits = tf.matmul(outputs, W) + b
# reshape 使得最后一个维度是 num_classes
shape = tf.shape(x)
self.logits = tf.reshape(self.logits, [shape[0], -1, num_classes])
# Time major
self.logits = tf.transpose(self.logits, (1, 0, 2))
def _build_train_op(self):
self.global_step = tf.Variable(0, trainable=False)
# ctc 损失函数，使用前后向算法和最大似然
self.loss = tf.nn.ctc_loss(labels=self.labels,
inputs=self.logits,
sequence_length=self.seq_len)
self.cost = tf.reduce_mean(self.loss)
tf.summary.scalar('cost', self.cost)
self.lrn_rate = tf.train.exponential_decay(FLAGS.initial_learning_rate,
self.global_step,
FLAGS.decay_steps,
FLAGS.decay_rate,
staircase=True)
# self.optimizer = tf.train.RMSPropOptimizer(learning_rate=self.lrn_rate,
# momentum=FLAGS.momentum).minimize(self.cost,
# global_step=self.global_step)
# self.optimizer = tf.train.MomentumOptimizer(learning_rate=self.lrn_rate,
# momentum=FLAGS.momentum,
# use_nesterov=True).minimize(self.cost,
# global_step=self.global_step)
self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.initial_learning_rate,
beta1=FLAGS.beta1,
beta2=FLAGS.beta2).minimize(self.loss,
global_step=self.global_step)
train_ops = [self.optimizer] + self._extra_train_ops
self.train_op = tf.group(*train_ops)
# Option 2: tf.contrib.ctc.ctc_beam_search_decoder
# (it's slower but you'll get better results)
# decoded, log_prob = tf.nn.ctc_greedy_decoder(logits, seq_len,merge_repeated=False)
self.decoded, self.log_prob = tf.nn.ctc_beam_search_decoder(self.logits,
self.seq_len,
merge_repeated=False) # 寻找最优路径
self.dense_decoded = tf.sparse_tensor_to_dense(self.decoded[0], default_value=-1) # 解码
# 卷积
def _conv2d(self, x, name, filter_size, in_channels, out_channels, strides):
with tf.variable_scope(name):
kernel = tf.get_variable(name='DW',
shape=[filter_size, filter_size, in_channels, out_channels],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
b = tf.get_variable(name='bais',
shape=[out_channels],
dtype=tf.float32,
initializer=tf.constant_initializer())
con2d_op = tf.nn.conv2d(x, kernel, [1, strides, strides, 1], padding='SAME')
return tf.nn.bias_add(con2d_op, b) #加上偏置，然后返回
# 批标准化
def _batch_norm(self, name, x):
"""批标准化."""
with tf.variable_scope(name):
params_shape = [x.get_shape()[-1]] #获取tensor的最后一个维度，后面的均值，方差都是这个维度
# 标准化数据为均值为0方差为1之后，还有一个x=x*gamma+beta的调整
# 这个会随着训练不断调整
beta = tf.get_variable(
'beta', params_shape, tf.float32,
initializer=tf.constant_initializer(0.0, tf.float32))
gamma = tf.get_variable(
'gamma', params_shape, tf.float32,
initializer=tf.constant_initializer(1.0, tf.float32))
# 训练的时候不断调整平滑均值，平滑方差
# 预测的时候，回复权重使用的是训练过程中调整出来的平滑方差均值去做标准化
if self.mode == 'train':
mean, variance = tf.nn.moments(x, [0, 1, 2], name='moments') #获取批均值和方差，size[最后一个维度]
# moving_mean, moving_variance 这两个name一定要让训练和预测的时候都相等，不然就没法恢复训练好的值了
moving_mean = tf.get_variable(
'moving_mean', params_shape, tf.float32,
initializer=tf.constant_initializer(0.0, tf.float32),
trainable=False)
moving_variance = tf.get_variable(
'moving_variance', params_shape, tf.float32,
initializer=tf.constant_initializer(1.0, tf.float32),
trainable=False)
self._extra_train_ops.append(moving_averages.assign_moving_average(
moving_mean, mean, 0.9))
self._extra_train_ops.append(moving_averages.assign_moving_average(
moving_variance, variance, 0.9))
else:
# mean的name一定要跟train的时候的一样 moving_mean
mean = tf.get_variable(
'moving_mean', params_shape, tf.float32,
initializer=tf.constant_initializer(0.0, tf.float32),
trainable=False)
# variance的name一定要跟train的时候的一样 moving_variance
variance = tf.get_variable(
'moving_variance', params_shape, tf.float32,
initializer=tf.constant_initializer(1.0, tf.float32),
trainable=False)
# 可视化
tf.summary.histogram(mean.op.name, mean)
tf.summary.histogram(variance.op.name, variance)
# 计算，标准化，最后一个值为误差，一般设置很小即可
x_bn = tf.nn.batch_normalization(x, mean, variance, beta, gamma, 0.001)
x_bn.set_shape(x.get_shape())
return x_bn
# 变种Relu
# Relu 简单而强大，方便求导
# 非负区间的梯度为常数，一定程度上能够防止梯度消失问题
def _leaky_relu(self, x, leakiness=0.0):
return tf.where(tf.less(x, 0.0), leakiness * x, x, name='leaky_relu')
def _max_pool(self, x, ksize, strides):
return tf.nn.max_pool(x,
ksize=[1, ksize, ksize, 1],
strides=[1, strides, strides, 1],
padding='SAME',
name='max_pool')
