ValueError:无法为Tensor'Plandholder_4:0'提供形状值(128,),其形状为'(?,1161)'

时间:2017-07-03 16:13:36

标签: python-3.x tensorflow

我在Tensorflow的占位符张量中遇到了值错误。我已将其声明为[None,n_classes],以便它可以接受任何大小的批量。然而,我面临的是ValueError,它与批量大小和张量标签Feed不匹配。

以下是代码:

n_inputs = 5000
n_classes = 1161
features = tf.placeholder(tf.float32, [None, n_inputs])
labels = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32)

h_layer = 256

weights = {
'hidden_weights' : tf.Variable(tf.random_normal([n_inputs, h_layer])),
'out_weights' : tf.Variable(tf.random_normal([h_layer, n_classes]))
}

bias = {
'hidden_bias' : tf.Variable(tf.random_normal([h_layer])),
'out_bias' : tf.Variable(tf.random_normal([n_classes]))
}

hidden_output1 = tf.add(tf.matmul(features, weights['hidden_weights']),bias['hidden_bias'])
hidden_relu1 = tf.nn.relu(hidden_output1)
hidden_out = tf.nn.dropout(hidden_relu1, keep_prob)

hidden_output2 = tf.add(tf.matmul(hidden_out, weights['out_weights']),bias['out_bias'])
logits = tf.nn.relu(hidden_output2)
logits = tf.nn.dropout(logits, keep_prob)
learn_rate = 0.001


cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = labels))

optimizer = tf.train.GradientDescentOptimizer(learning_rate = learn_rate).minimize(cost)

correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(labels,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

batchSize =  128 

epochs = 1000
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init) 
    total_batches = batches(batchSize, train_features, train_labels)

    for epoch in range(epochs): 
        for batch_features, batch_labels in total_batches: 
            train_data = {features: batch_features, labels : batch_labels, keep_prob : 0.7}
            sess.run(optimizer, feed_dict = train_data)
        # Print status for every 100 epochs
        if epoch % 1000 == 0:
            valid_accuracy = sess.run(
                accuracy,
                feed_dict={
                    features: val_features,
                    labels: val_labels,
                    keep_prob : 0.7})
            print('Epoch {:<3} - Validation Accuracy: {}'.format(
                epoch,
                valid_accuracy))
    Accuracy = sess.run(accuracy, feed_dict={features : test_features, labels :test_labels, keep_prob : 0.7})

    print('Trained Model Saved.')
print("Accuracy value is {}".format(Accuracy))

添加代码的堆栈跟踪:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-14-6e6a72faba19> in <module>()
     45         for batch_features, batch_labels in total_batches:
     46             train_data = {features: batch_features, labels : batch_labels, keep_prob : 0.7}
---> 47             sess.run(optimizer, feed_dict = train_data)
     48         # Print status for every 100 epochs
     49         if epoch % 1000 == 0:

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
    765     try:
    766       result = self._run(None, fetches, feed_dict, options_ptr,
--> 767                          run_metadata_ptr)
    768       if run_metadata:
    769         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    942                 'Cannot feed value of shape %r for Tensor %r, '
    943                 'which has shape %r'
--> 944                 % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
    945           if not self.graph.is_feedable(subfeed_t):
    946             raise ValueError('Tensor %s may not be fed.' % subfeed_t)

ValueError: Cannot feed value of shape (128,) for Tensor 'Placeholder_4:0', which has shape '(?, 1161)'

我在语法中遗漏了什么吗?

**编辑**

更改

labels = tf.placeholder(tf.int32, [None]) and 
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = tf.one_hot(labels, num_classes)))

堆栈跟踪是:

---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
   1021     try:
-> 1022       return fn(*args)
   1023     except errors.OpError as e:

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1003                                  feed_dict, fetch_list, target_list,
-> 1004                                  status, run_metadata)
   1005 

C:\Anaconda\envs\tensorflow\lib\contextlib.py in __exit__(self, type, value, traceback)
     65             try:
---> 66                 next(self.gen)
     67             except StopIteration:

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\framework\errors_impl.py in raise_exception_on_not_ok_status()
    465           compat.as_text(pywrap_tensorflow.TF_Message(status)),
--> 466           pywrap_tensorflow.TF_GetCode(status))
    467   finally:

InvalidArgumentError: Expected dimension in the range [-1, 1), but got 1
     [[Node: ArgMax_1 = ArgMax[T=DT_INT32, Tidx=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_Placeholder_4_0, ArgMax_1/dimension)]]

During handling of the above exception, another exception occurred:

InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-12-8e96f1dbdfec> in <module>()
     53                     features: val_features,
     54                     labels: val_labels,
---> 55                     keep_prob : 0.7})
     56             print('Epoch {:<3} - Validation Accuracy: {}'.format(
     57                 epoch,

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
    765     try:
    766       result = self._run(None, fetches, feed_dict, options_ptr,
--> 767                          run_metadata_ptr)
    768       if run_metadata:
    769         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    963     if final_fetches or final_targets:
    964       results = self._do_run(handle, final_targets, final_fetches,
--> 965                              feed_dict_string, options, run_metadata)
    966     else:
    967       results = []

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1013     if handle is None:
   1014       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1015                            target_list, options, run_metadata)
   1016     else:
   1017       return self._do_call(_prun_fn, self._session, handle, feed_dict,

C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
   1033         except KeyError:
   1034           pass
-> 1035       raise type(e)(node_def, op, message)
   1036 
   1037   def _extend_graph(self):

InvalidArgumentError: Expected dimension in the range [-1, 1), but got 1
     [[Node: ArgMax_1 = ArgMax[T=DT_INT32, Tidx=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_Placeholder_4_0, ArgMax_1/dimension)]]

Caused by op 'ArgMax_1', defined at:
  File "C:\Anaconda\envs\tensorflow\lib\runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "C:\Anaconda\envs\tensorflow\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\ipykernel\__main__.py", line 3, in <module>
    app.launch_new_instance()
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\traitlets\config\application.py", line 658, in launch_instance
    app.start()
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\ipykernel\kernelapp.py", line 474, in start
    ioloop.IOLoop.instance().start()
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\zmq\eventloop\ioloop.py", line 177, in start
    super(ZMQIOLoop, self).start()
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\tornado\ioloop.py", line 887, in start
    handler_func(fd_obj, events)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\tornado\stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events
    self._handle_recv()
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv
    self._run_callback(callback, msg)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback
    callback(*args, **kwargs)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\tornado\stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\ipykernel\kernelbase.py", line 276, in dispatcher
    return self.dispatch_shell(stream, msg)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\ipykernel\kernelbase.py", line 228, in dispatch_shell
    handler(stream, idents, msg)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\ipykernel\kernelbase.py", line 390, in execute_request
    user_expressions, allow_stdin)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\ipykernel\ipkernel.py", line 196, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\ipykernel\zmqshell.py", line 501, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\IPython\core\interactiveshell.py", line 2717, in run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\IPython\core\interactiveshell.py", line 2821, in run_ast_nodes
    if self.run_code(code, result):
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\IPython\core\interactiveshell.py", line 2881, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-12-8e96f1dbdfec>", line 33, in <module>
    correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(labels,1))
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\ops\math_ops.py", line 173, in argmax
    return gen_math_ops.arg_max(input, axis, name)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 168, in arg_max
    name=name)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 763, in apply_op
    op_def=op_def)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 2327, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 1226, in __init__
    self._traceback = _extract_stack()

InvalidArgumentError (see above for traceback): Expected dimension in the range [-1, 1), but got 1
     [[Node: ArgMax_1 = ArgMax[T=DT_INT32, Tidx=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_Placeholder_4_0, ArgMax_1/dimension)]]

1 个答案:

答案 0 :(得分:1)

正如错误所说,你向张量提供了错误的大小:labelslabels期望输入为[batch_size, num_classes],但您正在为其输入[batch_size]。将labels = tf.placeholder(tf.int32, [None])功能传递给tf.one_hot(labels, num_classes)时,请更改为tf.nn.softmax_cross_entropy_with_logits()并使用geocoder = new google.maps.Geocoder(); var j = 0; var area = []; var time_limit = 1000; var decoding = setInterval(function() { console.log("time is " + time_limit); time_limit = Math.floor((Math.random() * 1000) + 1000); if (j > (employee_name.length - 1)) { for (var k = 0; k < employee_name.length; k++) { document.getElementById(k).innerHTML = area[k]; } clearInterval(decoding); } if (isNaN(employee_address[j].lat) || isNaN(employee_address[j].lng)) { console.log("Found At " + j); area[j] = "Unknown Location"; j++; } else { var point = new google.maps.LatLng(employee_address[j].lat, employee_address[j].lng); geocoder.geocode({ 'latLng': point }, function(results, status) { if (status !== google.maps.GeocoderStatus.OK) { console.log(status); } // This is checking to see if the Geoeode Status is OK before proceeding if (status == google.maps.GeocoderStatus.OK) { var address = (results[0].formatted_address); area.push(address); console.log(j); j++; } }); } }, time_limit);