Question

有人已经问过similar question，但是那里给出的解决方案对我不起作用。

我正在尝试在tensorflow中使用Adam优化器。以下是我的代码的一部分：

adamOptimizer = tf.train.AdamOptimizer(learning_rate=0.001, beta1=0.9,
           beta2=0.999, epsilon=1e-08, use_locking=False, name='Adam')

print('Optimizer was created!')

# Create a variable to track the global step.
global_step = tf.Variable(0, name='global_step', trainable=False)

#Initialize variables
vars_to_init = ae.get_variables_to_init(n)
vars_to_init.append(global_step)
vars_to_init.append

sess.run(tf.variables_initializer(vars_to_init))

# create an optimizer
train_op = adamOptimizer.minimize(loss, global_step=global_step)

首次使用train_op后引发以下错误：

FailedPreconditionError（参见上面的回溯）：尝试使用未初始化的值pretrain_1 / beta2_power [[节点：pretrain_1 / beta2_power /读取= IdentityT = DT_FLOAT，_class = [“loc：@ autoencoder_variables / weights1”]，_ device =“/ job：localhost / replica：0 / task：0 / cpu：0”]] < / p>

如果我尝试添加一行

vars_to_init.append(beta2_power)

我收到以下错误：

NameError：未定义全局名称'beta2_power'

如果我遵循similar question的建议并用sess.run（tf.initialize_all_variables（））替换sess.run（tf.variables_initializer（vars_to_init）），运行此行后，我收到以下错误：

FailedPreconditionError：尝试使用未初始化的值autoencoder_variables / biases1 [[节点：autoencoder_variables / biases1 / read = IdentityT = DT_FLOAT，_class = [“loc：@ autoencoder_variables / biases1”]，_ device =“/ job：localhost / replica：0 / task：0 / cpu：0”]] < / p>

当我使用Gradient Descent优化器时，我没有遇到任何问题......

我做错了什么？使用此优化程序的正确方法是什么？

EDIT 有关该类的更多详细信息，以阐明autoencoder_variables：

class AutoEncoder(object):

_weights_str = "weights{0}"
_biases_str = "biases{0}"

def __init__(self, shape, sess):

  self.__shape = shape 
  self.__num_hidden_layers = len(self.__shape) - 2

  self.__variables = {}
  self.__sess = sess

  self._setup_variables()

@property
def shape(self):
  return self.__shape

@property
def num_hidden_layers(self):
   return self.__num_hidden_layers

@property
def session(self):
   return self.__sess

def __getitem__(self, item):

return self.__variables[item]

def __setitem__(self, key, value):

self.__variables[key] = value

def _setup_variables(self):
with tf.name_scope("autoencoder_variables"):
  for i in xrange(self.__num_hidden_layers + 1):
    # Train weights
    name_w = self._weights_str.format(i + 1)
    w_shape = (self.__shape[i], self.__shape[i + 1])
    a = tf.mul(4.0, tf.sqrt(6.0 / (w_shape[0] + w_shape[1])))
    w_init = tf.random_uniform(w_shape, -1 * a, a)
    self[name_w] = tf.Variable(w_init,
                               name=name_w,
                               trainable=True)
    # Train biases
    name_b = self._biases_str.format(i + 1)
    b_shape = (self.__shape[i + 1],)
    b_init = tf.zeros(b_shape)
    self[name_b] = tf.Variable(b_init, trainable=True, name=name_b)

    if i <= self.__num_hidden_layers:

      # Hidden layer fixed weights (after pretraining before fine tuning)
      self[name_w + "_fixed"] = tf.Variable(tf.identity(self[name_w]),
                                            name=name_w + "_fixed",
                                            trainable=False)

      # Hidden layer fixed biases
      self[name_b + "_fixed"] = tf.Variable(tf.identity(self[name_b]),
                                            name=name_b + "_fixed",
                                            trainable=False)

      # Pretraining output training biases
      name_b_out = self._biases_str.format(i + 1) + "_out"
      b_shape = (self.__shape[i],)
      b_init = tf.zeros(b_shape)
      self[name_b_out] = tf.Variable(b_init,
                                     trainable=True,
                                     name=name_b_out)

def _w(self, n, suffix=""):
  return self[self._weights_str.format(n) + suffix]

def _b(self, n, suffix=""):
  return self[self._biases_str.format(n) + suffix]

def get_variables_to_init(self, n):
  assert n > 0
  assert n <= self.__num_hidden_layers + 1

  vars_to_init = [self._w(n), self._b(n)]

  if n <= self.__num_hidden_layers:
    vars_to_init.append(self._b(n, "_out"))

  if 1 < n <= self.__num_hidden_layers+1:
    # Fixed matrices for learning of deeper layers
    vars_to_init.append(self._w(n - 1, "_fixed"))
    vars_to_init.append(self._b(n - 1, "_fixed"))

  return vars_to_init

Answer 1

问题是我使用一个变量值来初始化其他变量（它引发了在初始化期间使用未初始化变量的错误）。

而不是在初始化期间使用另一个变量

self[name_b + "_fixed"] = tf.Variable(tf.identity(self[name_b]),
                                            name=name_b + "_fixed",
                                            trainable=False)

我随机初始化

self[name_b + "_fixed"] = tf.Variable(init_b,
                                            name=name_b + "_fixed",
                                            trainable=False)

在训练后将其分配给另一个变量：

 ae[name_w + "_fixed"] = tf.identity(ae[name_w])

Tensorflow：如何正确使用Adam优化器

1 个答案: