我在使用导出的tensorflow模型时遇到了问题。它不允许我评估我提供的数据集。如果我在与培训相同的会话中运行评估，那么，如果我必须重新训练我的模型以便用另一个数据集进行测试，则没有任何问题会导致保存模型的目的失败。用于生成模型的python文件是这样的：
x = tf.placeholder(tf.float32, shape=[None, 1024], name = "x")
y_ = tf.placeholder(tf.float32, shape=[None, 10], name = "y_")
#===Model===
#Train
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name= "accuracy")
#Create Saver
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
for i in range(40000):
batch = shvn_data.nextbatch(100)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0})
print("step %d, training accuracy %f"%(i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
#Save
saver.save(sess,'svhn_model1')
我保存了输入变量x和y_，通过函数＆＃39;准确度＆＃39;，这样我就可以运行accuracy.eval（）来获得预测的准确性。我以100个图像的批量评估数据集，然后总结最终预测。在另一个会话中评估模型的python文件是这样的：
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config = config)
shvn_data = DataLoader()
saver = tf.train.import_meta_graph('svhn_model1.meta')
saver.restore(sess,tf.train.latest_checkpoint('./'))
#sess.run(tf.global_variables_initializer())
#Variables to use with model
graph = tf.get_default_graph()
x = graph.get_tensor_by_name("x:0")
y_ = graph.get_tensor_by_name("y_:0")
accuracy = graph.get_tensor_by_name("accuracy:0")
keep_prob = tf.placeholder(tf.float32)
img_whole = np.reshape(shvn_data.test_images,(-1,1024))
batch_whole = np.asarray(shvn_data.test_label.eval(), dtype = np.float32)
total_accuracy = 0
test_count = shvn_data.TEST_COUNT
batch_size = 100
steps = int(math.ceil(test_count/float(batch_size)))
for j in range(steps):
start = j*batch_size
if (j+1)*batch_size > shvn_data.TEST_COUNT:
end = test_count
else:
end = (j+1)*batch_size
img_batch = img_whole[start:end]
label_batch = batch_whole[start:end]
batch_accuracy = accuracy.eval(session = sess, feed_dict={ x: img_batch, y_: label_batch, keep_prob: 1.0}) #ISSUE LIES HERE
print("Test batch %d:%d accuracy %g"%(start,end,batch_accuracy))
total_accuracy += batch_accuracy
print ("Total Accuracy: %f" %(total_accuracy/steps))
错误如下。
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1052, in _do_call
raise type(e)(node_def, op, message)
InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
Caused by op u'Placeholder', defined at:
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/spyder/utils/ipython/start_kernel.py", line 227, in <module>
main()
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/spyder/utils/ipython/start_kernel.py", line 223, in main
kernel.start()
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/ipykernel/kernelapp.py", line 474, in start
ioloop.IOLoop.instance().start()
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/zmq/eventloop/ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tornado/ioloop.py", line 887, in start
handler_func(fd_obj, events)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 276, in dispatcher
return self.dispatch_shell(stream, msg)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell
handler(stream, idents, msg)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 390, in execute_request
user_expressions, allow_stdin)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/ipykernel/ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/ipykernel/zmqshell.py", line 501, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2717, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2827, in run_ast_nodes
if self.run_code(code, result):
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-1-33e4fce19d34>", line 1, in <module>
runfile('/home/lwenyao/Desktop/Python/Import_Model.py', wdir='/home/lwenyao/Desktop/Python')
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 866, in runfile
execfile(filename, namespace)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 94, in execfile
builtins.execfile(filename, *where)
File "/home/lwenyao/Desktop/Python/Import_Model.py", line 63, in <module>
saver = tf.train.import_meta_graph('svhn_model1.meta')
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1595, in import_meta_graph
**kwargs)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/meta_graph.py", line 499, in import_scoped_meta_graph
producer_op_list=producer_op_list)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/importer.py", line 308, in import_graph_def
op_def=op_def)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2336, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1228, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype float
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
如前所述，如果我在训练模型时在同一会话中运行评估，则评估没有问题。我做的唯一更改是每次在使用导入的模型时调用.eval（）时都添加了参数session = sess。对不起，很长的帖子！
答案 0 :(得分：1)
好的，似乎错误是由于在导入模型后尝试在测试脚本中创建和使用另一个public View onCreateView(LayoutInflater inflater, @Nullable ViewGroup container, @Nullable Bundle savedInstanceState) {
View v = inflater.inflate(R.layout.fragment_item_master, container, false);
viewPager = (ViewPager)v. findViewById(R.id.viewpager);
setupViewPager(viewPager);
tabLayout = (TabLayout)v. findViewById(R.id.tabs);
tabLayout.setupWithViewPager(viewPager);
return v;
}
private void setupViewPager(ViewPager viewPager) {
// ViewPagerAdapter adapter = new ViewPagerAdapter(mActivity.getSupportFragmentManager());
ViewPagerAdapter adapter = new ViewPagerAdapter(getChildFragmentManager());
adapter.addFragment(new ItemInfoFragment(), "Item Info");
adapter.addFragment(new ItemExtraInfoFragment(), "Extra Info");
adapter.addFragment(new OrderHistoryFragment(), "Order History");
viewPager.setAdapter(adapter);
}
class ViewPagerAdapter extends FragmentStatePagerAdapter {
private final List<Fragment> mFragmentList = new ArrayList<>();
private final List<String> mFragmentTitleList = new ArrayList<>();
public ViewPagerAdapter(FragmentManager manager) {
super(manager);
}
@Override
public Fragment getItem(int position) {
return mFragmentList.get(position);
}
@Override
public int getCount() {
return mFragmentList.size();
}
public void addFragment(Fragment fragment, String title) {
mFragmentList.add(fragment);
mFragmentTitleList.add(title);
}
变量引起的。即我在培训文件中创建了keep_prob。但是，测试文件中的keep_prob = tf.placeholder(tf.float32,)试图从模型中专门查找accuracy.eval()。我在测试文件中创建了另一个keep_prob，认为它是相同的，但事实并非如此。
我通过添加标签修改了培训文件中的代码：
keep_prob = tf.placeholder(tf.float32,)
在我的测试文件中，调用了模型的变量：
keep_prob = tf.placeholder(tf.float32, name="keep_prob")现在它工作正常。我的代码是从tensorflow的 Deep MNIST for Experts 修改的。