I am facing a Value Error in Tensorflow's placeholder tensor. I have declared it as [None, n_classes] so that it can accept batch of any size. Yet I am facing a ValueError that there is a mismatch with the batch size and the tensor label feed.
我在Tensorflow的占位张量中遇到一个值错误。我已经声明它为[None, n_classes]，以便它可以接受任意大小的批处理。然而，我正面临一个ValueError，它与批处理大小和张量标签提要不匹配。
Following is the code:
下面是代码:
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))
Adding the stack trace of the code:
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)'
Am I missing anything in the syntax ?
我在语法中漏掉了什么吗?
**EDIT **
* *编辑* *
After changing the
后改变了
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)))
stack trace is :
堆栈跟踪:
---------------------------------------------------------------------------
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 个解决方案
#1
1
As the error says, you are feeding the wrong size to the tensor: labels. labels expects the input to be [batch_size, num_classes] but you are feeding it [batch_size]. Change to labels = tf.placeholder(tf.int32, [None]) and use tf.one_hot(labels, num_classes) when you pass it to the tf.nn.softmax_cross_entropy_with_logits() function.
正如错误所说，您给张量提供了错误的大小:标签。标签期望输入是[batch_size, num_classes]，但是您正在输入它[batch_size]。更改为label = tf.placeholder(tf.int32， [None])并使用tf。当您将它传递给tf.nn.softmax_cross_entropy_with_logits()函数时，one_hot(标签、num_classes)。
#1
1
As the error says, you are feeding the wrong size to the tensor: labels. labels expects the input to be [batch_size, num_classes] but you are feeding it [batch_size]. Change to labels = tf.placeholder(tf.int32, [None]) and use tf.one_hot(labels, num_classes) when you pass it to the tf.nn.softmax_cross_entropy_with_logits() function.
正如错误所说，您给张量提供了错误的大小:标签。标签期望输入是[batch_size, num_classes]，但是您正在输入它[batch_size]。更改为label = tf.placeholder(tf.int32， [None])并使用tf。当您将它传递给tf.nn.softmax_cross_entropy_with_logits()函数时，one_hot(标签、num_classes)。