tensorflow学习笔记（二十六）：构建TF代码

如何构建TF代码

batch_size

: batch的大小

mini_batch

: 将训练样本以

batch_size

分组

epoch_size

: 样本分为几个

min_batch

num_epoch

: 训练几轮

读代码的时候应该关注的几部分

如何处理数据

如何构建计算图

如何计算梯度

如何

Summary

，如何

save

模型参数

如何执行计算图

写一个将数据分成训练集,验证集和测试集的函数

train_set, valid_set, test_set = split_set(data)

最好写一个管理数据的对象，将原始数据转化成

mini_batch

class DataManager(object):
#raw_data为train_set, valid_data或test_set
def __init__(self, raw_data, batch_size):
self.raw_data = raw_data
self.batch_size = batch_size
self.epoch_size = len(raw_data)/batch_size
self.counter = 0 #监测batch index
def next_batch(self):
...
self.counter += 1
return batched_x, batched_label, ...

计算图

的构建在

Model

类中的

__init__()

中完成,并设置is_training参数

优点：

1. 因为如果我们在训练的时候加

dropout

的话，那么在测试的时候是需要把这个

dropout

层去掉的。这样的话，在写代码的时候，你就可以创建两个对象。这就相当于建了两个

模型

，然后让这两个

模型

参数共享，就可以达到

训练

和

测试

一起运行的效果了。具体看下面代码。

class Model(object):
def __init__(self, is_training, config, scope,...):#scope可以使你正确的summary
self.is_training = is_training
self.config = config
#placeholder:用于feed数据
# 一个train op
self.graph(self.is_training) #构建图
self.merge_op = tf.summary.merge(tf.get_collection(tf.GraphKeys.SUMMARIES,scope))
def graph(self,is_training):
...
#定义计算图
self.predict = ...
self.loss = ...

写个

run_epoch

函数

batch_size

: batch的大小

mini_batch

: 将训练样本以

batch_size

分组

epoch_size

: 样本分为几个

min_batch

num_epoch

: 训练几轮

如何编写run_epoch函数

#eval_op是用来指定是否需要训练模型，需要的话，传入模型的eval_op
#draw_ata用于接收 train_data,valid_data或test_data
def run_epoch(raw_data ，session, model, is_training_set, ...):
data_manager = DataManager(raw_data, model.config.batch_size)

#通过is_training_set来决定fetch哪些Tensor
#add_summary, saver.save(....)

如何组织main函数

分解原始数据为train，valid，test

设置默认图

建图 trian, test 分别建图

一个或多个

Saver

对象，用来保存模型参数

创建session， 初始化变量

一个

summary.FileWriter

对象，用来将

summary

写入到硬盘中

run epoch

FileWriter

和

Saver

对象，一个

计算图

只需要一个就够了，所以放在

Model

类的外面

附录

本篇博文总结下面代码写成， 有些地方和源码之间有不同。

下面是截取自官方代码：

class PTBInput(object):
"""The input data."""

def __init__(self, config, data, name=None):
self.batch_size = batch_size = config.batch_size
self.num_steps = num_steps = config.num_steps
self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
self.input_data, self.targets = reader.ptb_producer(
data, batch_size, num_steps, name=name)

class PTBModel(object):
"""The PTB model."""

def __init__(self, is_training, config, input_):
self._input = input_

batch_size = input_.batch_size
num_steps = input_.num_steps
size = config.hidden_size
vocab_size = config.vocab_size

# Slightly better results can be obtained with forget gate biases
# initialized to 1 but the hyperparameters of the model would need to be
# different than reported in the paper.
lstm_cell = tf.contrib.rnn.BasicLSTMCell(
size, forget_bias=0.0, state_is_tuple=True)
if is_training and config.keep_prob < 1:
lstm_cell = tf.contrib.rnn.DropoutWrapper(
lstm_cell, output_keep_prob=config.keep_prob)
cell = tf.contrib.rnn.MultiRNNCell(
[lstm_cell] * config.num_layers, state_is_tuple=True)

self._initial_state = cell.zero_state(batch_size, data_type())

with tf.device("/cpu:0"):
embedding = tf.get_variable(
"embedding", [vocab_size, size], dtype=data_type())
inputs = tf.nn.embedding_lookup(embedding, input_.input_data)

if is_training and config.keep_prob < 1:
inputs = tf.nn.dropout(inputs, config.keep_prob)

# Simplified version of models/tutorials/rnn/rnn.py's rnn().
# This builds an unrolled LSTM for tutorial purposes only.
# In general, use the rnn() or state_saving_rnn() from rnn.py.
#
# The alternative version of the code below is:
#
# inputs = tf.unstack(inputs, num=num_steps, axis=1)
# outputs, state = tf.nn.rnn(cell, inputs,
#                            initial_state=self._initial_state)
outputs = []
state = self._initial_state
with tf.variable_scope("RNN"):
for time_step in range(num_steps):
if time_step > 0: tf.get_variable_scope().reuse_variables()
(cell_output, state) = cell(inputs[:, time_step, :], state)
outputs.append(cell_output)

output = tf.reshape(tf.concat_v2(outputs, 1), [-1, size])
softmax_w = tf.get_variable(
"softmax_w", [size, vocab_size], dtype=data_type())
softmax_b = tf.get_variable("softmax_b", [vocab_size], dtype=data_type())
logits = tf.matmul(output, softmax_w) + softmax_b
loss = tf.contrib.legacy_seq2seq.sequence_loss_by_example(
[logits],
[tf.reshape(input_.targets, [-1])],
[tf.ones([batch_size * num_steps], dtype=data_type())])
self._cost = cost = tf.reduce_sum(loss) / batch_size
self._final_state = state

if not is_training:
return

self._lr = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(cost, tvars),
config.max_grad_norm)
optimizer = tf.train.GradientDescentOptimizer(self._lr)
self._train_op = optimizer.apply_gradients(
zip(grads, tvars),
global_step=tf.contrib.framework.get_or_create_global_step())

self._new_lr = tf.placeholder(
tf.float32, shape=[], name="new_learning_rate")
self._lr_update = tf.assign(self._lr, self._new_lr)

def assign_lr(self, session, lr_value):
session.run(self._lr_update, feed_dict={self._new_lr: lr_value})

def run_epoch(session, model, eval_op=None, verbose=False):
"""Runs the model on the given data."""
start_time = time.time()
costs = 0.0
iters = 0
state = session.run(model.initial_state)

fetches = {
"cost": model.cost,
"final_state": model.final_state,
}
if eval_op is not None:
fetches["eval_op"] = eval_op

for step in range(model.input.epoch_size):
feed_dict = {}
for i, (c, h) in enumerate(model.initial_state):
feed_dict[c] = state[i].c
feed_dict[h] = state[i].h

vals = session.run(fetches, feed_dict)
cost = vals["cost"]
state = vals["final_state"]

costs += cost
iters += model.input.num_steps

if verbose and step % (model.input.epoch_size // 10) == 10:
print("%.3f perplexity: %.3f speed: %.0f wps" %
(step * 1.0 / model.input.epoch_size, np.exp(costs / iters),
iters * model.input.batch_size / (time.time() - start_time)))

return np.exp(costs / iters)

def main(_):
if not FLAGS.data_path:
raise ValueError("Must set --data_path to PTB data directory")

raw_data = reader.ptb_raw_data(FLAGS.data_path)
train_data, valid_data, test_data, _ = raw_data

config = get_config()
eval_config = get_config()
eval_config.batch_size = 1
eval_config.num_steps = 1

with tf.Graph().as_default():
initializer = tf.random_uniform_initializer(-config.init_scale,
config.init_scale)

with tf.name_scope("Train"):
train_input = PTBInput(config=config, data=train_data, name="TrainInput")
with tf.variable_scope("Model", reuse=None, initializer=initializer):
m = PTBModel(is_training=True, config=config, input_=train_input)
tf.contrib.deprecated.scalar_summary("Training Loss", m.cost)
tf.contrib.deprecated.scalar_summary("Learning Rate", m.lr)

with tf.name_scope("Valid"):
valid_input = PTBInput(config=config, data=valid_data, name="ValidInput")
with tf.variable_scope("Model", reuse=True, initializer=initializer):
mvalid = PTBModel(is_training=False, config=config, input_=valid_input)
tf.contrib.deprecated.scalar_summary("Validation Loss", mvalid.cost)

with tf.name_scope("Test"):
test_input = PTBInput(config=eval_config, data=test_data, name="TestInput")
with tf.variable_scope("Model", reuse=True, initializer=initializer):
mtest = PTBModel(is_training=False, config=eval_config,
input_=test_input)

sv = tf.train.Supervisor(logdir=FLAGS.save_path)
with sv.managed_session() as session:
for i in range(config.max_max_epoch):
lr_decay = config.lr_decay ** max(i + 1 - config.max_epoch, 0.0)
m.assign_lr(session, config.learning_rate * lr_decay)

print("Epoch: %d Learning rate: %.3f" % (i + 1, session.run(m.lr)))
train_perplexity = run_epoch(session, m, eval_op=m.train_op,
verbose=True)
print("Epoch: %d Train Perplexity: %.3f" % (i + 1, train_perplexity))
valid_perplexity = run_epoch(session, mvalid)
print("Epoch: %d Valid Perplexity: %.3f" % (i + 1, valid_perplexity))

test_perplexity = run_epoch(session, mtest)
print("Test Perplexity: %.3f" % test_perplexity)

if FLAGS.save_path:
print("Saving model to %s." % FLAGS.save_path)
sv.saver.save(session, FLAGS.save_path, global_step=sv.global_step)

if __name__ == "__main__":
tf.app.run()

参考资料

源码地址https://github.com/tensorflow/models/blob/master/tutorials/rnn/ptb/ptb_word_lm.py