我正在学习TensorFlow。我正在尝试tf.train.MomentumOptimizer，但我收到以下错误：
Traceback (most recent call last):
File "relu.py", line 98, in <module>
learner.run(stop=0.01, print_epoch=True)
File "relu.py", line 70, in run
self.sess.run(train_step, feed_dict={self.x: batch_xs, self.y_: batch_ys})
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 767, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 965, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1015, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1035, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value Variable_2/Momentum
[[Node: Momentum/update_Variable_2/ApplyMomentum = ApplyMomentum[T=DT_FLOAT, _class=["loc:@Variable_2"], use_locking=false, use_nesterov=false, _device="/job:localhost/replica:0/task:0/cpu:0"](Variable_2, Variable_2/Momentum, Momentum/learning_rate, gradients/add_1_grad/tuple/control_dependency_1, Momentum/momentum)]]
Caused by op u'Momentum/update_Variable_2/ApplyMomentum', defined at:
File "relu.py", line 98, in <module>
learner.run(stop=0.01, print_epoch=True)
File "relu.py", line 55, in run
train_step = self.optimizer.minimize(self.cross_entropy)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 289, in minimize
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 413, in apply_gradients
update_ops.append(processor.update_op(self, grad))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 61, in update_op
return optimizer._apply_dense(g, self._v) # pylint: disable=protected-access
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/momentum.py", line 69, in _apply_dense
use_nesterov=self._use_nesterov).op
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/gen_training_ops.py", line 348, in apply_momentum
use_nesterov=use_nesterov, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2327, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1226, in __init__
self._traceback = _extract_stack()
FailedPreconditionError (see above for traceback): Attempting to use uninitialized value Variable_2/Momentum
[[Node: Momentum/update_Variable_2/ApplyMomentum = ApplyMomentum[T=DT_FLOAT, _class=["loc:@Variable_2"], use_locking=false, use_nesterov=false, _device="/job:localhost/replica:0/task:0/cpu:0"](Variable_2, Variable_2/Momentum, Momentum/learning_rate, gradients/add_1_grad/tuple/control_dependency_1, Momentum/momentum)]]
以下是我的代码：
import time
import numpy as np
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
class ReluMnistNet:
def __init__(self, optimizer=None):
self.varlist = []
self.optimizer = optimizer or tf.train.GradientDescentOptimizer(0.01)
# fetch dataset
self.mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# prepare environment
layers = [ 100 ]
input_layer = 784
output_layer = 10
self.x = tf.placeholder(tf.float32, [None, input_layer])
last_layer = input_layer
y = self.x
for layer in layers:
b = tf.Variable(tf.zeros([layer]))
self.varlist.append(b)
W = tf.Variable(tf.random_normal([last_layer,layer], stddev=0.01))
self.varlist.append(W)
y = tf.nn.relu( tf.matmul(y,W) ) + b
last_layer = layer
b = tf.Variable(tf.zeros([output_layer]))
self.varlist.append(b)
W = tf.Variable(tf.random_normal([last_layer,output_layer], stddev=0.01))
self.varlist.append(W)
self.y = tf.matmul(y,W) + b
self.y_ = tf.placeholder(tf.float32, [None, 10])
self.cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=self.y, labels=self.y_) )
def prepare(self):
# init = tf.initialize_variables(self.varlist)
init = tf.initialize_all_variables()
self.sess = tf.Session()
self.sess.run(init)
def run(self, batch_size=100, stop=0.001, print_epoch=False):
mnist = self.mnist
data_size = mnist.train.images.shape[0]
last_accuracy = 0
accuracy_history = []
train_step = self.optimizer.minimize(self.cross_entropy)
time1 = time.time()
for i in range(10000):
for j in range(data_size/batch_size):
# random batch
batch_idx = np.arange(data_size)
np.random.shuffle(batch_idx)
batch_idx = batch_idx[0:batch_size]
batch_xs = mnist.train.images[batch_idx]
batch_ys = mnist.train.labels[batch_idx]
# ordered batch
# start = j * batch_size
# end = (j+1) * batch_size
# batch_xs, batch_ys = mnist.train.images[start:end], mnist.train.labels[start:end]
self.sess.run(train_step, feed_dict={self.x: batch_xs, self.y_: batch_ys})
# test the accuracy
correct_prediction = tf.equal( tf.argmax(self.y,1), tf.argmax(self.y_,1) )
accuracy = tf.reduce_mean( tf.cast(correct_prediction, tf.float32) )
accuracy = self.sess.run(accuracy, feed_dict = {self.x: mnist.test.images, self.y_: mnist.test.labels})
accuracy_history.append(accuracy)
if print_epoch:
print i, accuracy
if last_accuracy != 0 and abs(last_accuracy-accuracy) < stop:
break
last_accuracy = accuracy
time2 = time.time()
return accuracy_history, (time2-time1)
def close(self):
if not (self.sess is None):
self.sess.close()
self.sess = None
if __name__ == '__main__':
learner = ReluMnistNet()
# learner.optimizer = tf.train.GradientDescentOptimizer(0.01)
learner.optimizer = tf.train.MomentumOptimizer(0.01, momentum=0.9)
for i in range(10):
learner.prepare()
learner.run(stop=0.01, print_epoch=True)
learner.close()
似乎名为Momentum的变量未初始化？但是，通过致电learner.prepare()，我已拨打tf.initialize_all_variables()。更重要的是，我没有名为Momentum的变量。为什么会这样？
答案 0 :(得分：3)
在您的代码中，您在初始化全局变量后调用minimize
而你必须这样做：
self.cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=self.y, labels=self.y_) )
self.optimize = self.optimizer.minimize(self.cross_entropy)
并在运行函数而不是
train_step = self.optimizer.minimize(self.cross_entropy)
你应该致电
train_step = self.optimize
P.S Momentun是MomentumOptimizer的默认名称