自顶向下分析一个简单的语音识别系统（五）

本回我们主要分析run_model中的configuration过程的相关函数。

1.run_model函数

第二回我们简单介绍了run_model函数的结构，现在我们贴出代码如下所示：

def run_model(self):
self.graph = tf.Graph()
with self.graph.as_default(), tf.device('/cpu:0'):

with tf.device(self.tf_device):
# Run multiple functions on the specificed tf_device
# tf_device GPU set in configs, but is overridden if not available
# __init__函数中调用gpu_tool.check_if_gpu_available函数，如果设备中有gpu，则self.tf_device=/gpu:0
self.setup_network_and_graph()
self.load_placeholder_into_network()
self.setup_loss_function()
self.setup_optimizer()
self.setup_decoder()

self.setup_summary_statistics()

# create the configuration for the session
tf_config = tf.ConfigProto()
tf_config.allow_soft_placement = True
tf_config.gpu_options.per_process_gpu_memory_fraction = \
(1.0 / self.simultaneous_users_count)
#设置gpu中的内存最大占用率，self.simultaneous_users_count=4

# create the session
self.sess = tf.Session(config=tf_config)

# initialize the summary writer
self.writer = tf.summary.FileWriter(
self.SUMMARY_DIR, graph=self.sess.graph)

# Add ops to save and restore all the variables
self.saver = tf.train.Saver()

# For printing out section headers
section = '\n{0:=^40}\n'

# If there is a model_path declared, then restore the model
#前述self.model_path=None
if self.model_path is not None:
self.saver.restore(self.sess, self.model_path)
# If there is NOT a model_path declared, build the model from scratch
else:
# Op to initialize the variables
init_op = tf.global_variables_initializer()

# Initializate the weights and biases
self.sess.run(init_op)

# MAIN LOGIC for running the training epochs
logger.info(section.format('Run training epoch'))
self.run_training_epochs()

logger.info(section.format('Decoding test data'))
# make the assumption for working on the test data, that the epoch here is the last epoch
_, self.test_ler = self.run_batches(self.data_sets.test, is_training=False,
decode=True, write_to_file=False, epoch=self.epochs)

# Add the final test data to the summary writer
# (single point on the graph for end of training run)
summary_line = self.sess.run(
self.test_ler_op, {self.ler_placeholder: self.test_ler})
self.writer.add_summary(summary_line, self.epochs)

logger.info('Test Label Error Rate: {}'.format(self.test_ler))

# save train summaries to disk
self.writer.flush()

self.sess.close()

2.setup_network_and_graph函数

本函数主要定义网络模型的输入输出placeholder，代码如下：

def setup_network_and_graph(self):
# e.g: log filter bank or MFCC features
# shape = [batch_size, max_stepsize, n_input + (2 * n_input * n_context)]
# the batch_size and max_stepsize can vary along each step
self.input_tensor = tf.placeholder(
tf.float32, [None, None, self.n_input + (2 * self.n_input * self.n_context)], name='input')

# Use sparse_placeholder; will generate a SparseTensor, required by ctc_loss op.
self.targets = tf.sparse_placeholder(tf.int32, name='targets')
# 1d array of size [batch_size]
self.seq_length = tf.placeholder(tf.int32, [None], name='seq_length')

其中，n_input=26表示MFCC倒谱系数为26位，n_context=9表示当前25ms声音片段往前和往后分别9个声音片段做输入。MFCC将在后面详细分析。

3.load_placeholder_into_network函数

该函数调用rnn.py中的SimpleLSTM/BiRNN函数构建网路的基本结构，代码如下：

def load_placeholder_into_network(self):
# logits is the non-normalized output/activations from the last layer.
# logits will be input for the loss function.
# nn_model is from the import statement in the load_model function
# summary_op variables are for tensorboard
if self.network_type == 'SimpleLSTM':
self.logits, summary_op = SimpleLSTM_model(
self.conf_path,
self.input_tensor,
tf.to_int64(self.seq_length)
)
elif self.network_type == 'BiRNN':
self.logits, summary_op = BiRNN_model(
self.conf_path,
self.input_tensor,
tf.to_int64(self.seq_length),
self.n_input,
self.n_context
)
else:
raise ValueError('network_type must be SimpleLSTM or BiRNN')
self.summary_op = tf.summary.merge([summary_op])

由前面可知，neural_network.ini中network_type=BIRNN，下回我们将详细分析该网络。

4.setup_loss_function函数

本函数设置语音识别模型的loss函数为ctc_loss，代码如下：

def setup_loss_function(self):
with tf.name_scope("loss"):
self.total_loss = ctc_ops.ctc_loss(
self.targets, self.logits, self.seq_length)
self.avg_loss = tf.reduce_mean(self.total_loss)
self.loss_summary = tf.summary.scalar("avg_loss", self.avg_loss)

self.cost_placeholder = tf.placeholder(dtype=tf.float32, shape=[])

self.train_cost_op = tf.summary.scalar(
"train_avg_loss", self.cost_placeholder)

后面将详细分析CTC损失函数。

5.setup_optimizer函数

本函数调用utils.py中的create_optimizer函数，使用AdamOptimizer对网络进行优化，代码如下：

def setup_optimizer(self):
# Note: The optimizer is created in models/RNN/utils.py
with tf.name_scope("train"):
self.optimizer = create_optimizer()
self.optimizer = self.optimizer.minimize(self.avg_loss)

6.setup_decoder函数

本函数使用ctc中的两种策略对输出结果进行解码，代码如下：

def setup_decoder(self):
with tf.name_scope("decode"):
if self.beam_search_decoder == 'default':
self.decoded, self.log_prob = ctc_ops.ctc_beam_search_decoder(
self.logits, self.seq_length, merge_repeated=False)
elif self.beam_search_decoder == 'greedy':
self.decoded, self.log_prob = ctc_ops.ctc_greedy_decoder(
self.logits, self.seq_length, merge_repeated=False)
else:
logging.warning("Invalid beam search decoder option selected!")

7.setup_summary_statistics函数

本函数主要用于设置运行过程中产生的summary的收集点，代码如下：

def setup_summary_statistics(self):
# Create a placholder for the summary statistics
with tf.name_scope("accuracy"):
# Compute the edit (Levenshtein) distance of the top path
distance = tf.edit_distance(
tf.cast(self.decoded[0], tf.int32), self.targets)

# Compute the label error rate (accuracy)
self.ler = tf.reduce_mean(distance, name='label_error_rate')
self.ler_placeholder = tf.placeholder(dtype=tf.float32, shape=[])
self.train_ler_op = tf.summary.scalar(
"train_label_error_rate", self.ler_placeholder)
self.dev_ler_op = tf.summary.scalar(
"validation_label_error_rate", self.ler_placeholder)
self.test_ler_op = tf.summary.scalar(
"test_label_error_rate", self.ler_placeholder)

本回简要分析了网络的configuration过程，下回将仔细分析网络的基本结构。