本文介绍了Tensorflow 2.0 InvalidArgumentError:断言失败:[条件x == y不按元素进行:]的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在训练mnist的CNN.当我运行我的代码时，问题就来了.我尝试了其他答案，但它们不起作用.我是TensorFlow的新手，所以有人可以向我解释此错误.这是我的代码.我正在使用Pycharm 2020.2.和Anaconda中的Python 3.6.我找不到任何帮助.import tensorflow as tf
from tensorflow.keras.models import Sequential
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_train, axis=1)
model = Sequential()
model.add(tf.keras.layers.Dense(256))
model.add(tf.keras.layers.Conv1D(kernel_size=4, strides=1, filters=4, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=3, strides=1, activation="relu", filters=3))
model.add(tf.keras.layers.Dense(128, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=2, filters=2, strides=1, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=1, filters=1, strides=1, activation="relu"))
model.add(tf.keras.layers.Dense(64, activation="relu"))
model.add(tf.keras.layers.MaxPool1D(pool_size=2, strides=1))
model.add(tf.keras.layers.Dense(256, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=4, filters=4, strides=1, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=3, filters=3, strides=1, activation="relu"))
model.add(tf.keras.layers.MaxPool1D(pool_size=2, strides=1))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(128, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=2, filters=2, strides=1, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=1, filters=1, strides=1, activation="relu"))
model.add(tf.keras.layers.Dense(64, activation="relu"))
model.add(tf.keras.layers.Dense(16, activation="softmax"))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x=x_train, y=y_train, batch_size=64, epochs=5, shuffle=True, validation_split=0.1)
model.summary()
出现错误:
Train on 54000 samples, validate on 6000 samples
Epoch 1/5
2020-09-09 15:16:16.953428: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2020-09-09 15:16:17.146701: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-09-09 15:16:17.741916: W tensorflow/stream_executor/gpu/redzone_allocator.cc:312] Internal: Invoking GPU asm compilation is supported on Cuda non-Windows platforms only
Relying on driver to perform ptx compilation. This message will be only logged once.
2020-09-09 15:16:18.085250: W tensorflow/core/common_runtime/base_collective_executor.cc:217] BaseCollectiveExecutor::StartAbort Invalid argument: assertion failed: [Condition x == y did not hold element-wise:] [x (loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/Shape_1:0) = ] [64 1] [y (loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/strided_slice:0) = ] [64 14]
[[{{node loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/assert_equal_1/Assert/Assert}}]]
64/54000 [..............................] - ETA: 39:34Traceback (most recent call last):
File "F:\anaconda\envs\tensorflow1\lib\site-packages\IPython\core\interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-2-d2317d03e1c1>", line 1, in <module>
runfile('F:/Pycharm_projects/my_fun_project/Fake or real news/fake-or-real-news/bitcoin.py', wdir='F:/Pycharm_projects/my_fun_project/Fake or real news/fake-or-real-news')
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "F:/Pycharm_projects/my_fun_project/Fake or real news/fake-or-real-news/bitcoin.py", line 41, in <module>
model.fit(x=x_train, y=y_train, batch_size=64, epochs=5, shuffle=True, validation_split=0.1)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 819, in fit
use_multiprocessing=use_multiprocessing)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 342, in fit
total_epochs=epochs)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 128, in run_one_epoch
batch_outs = execution_function(iterator)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 98, in execution_function
distributed_function(input_fn))
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 568, in __call__
result = self._call(*args, **kwds)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 632, in _call
return self._stateless_fn(*args, **kwds)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\eager\function.py", line 2363, in __call__
return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\eager\function.py", line 1611, in _filtered_call
self.captured_inputs)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\eager\function.py", line 1692, in _call_flat
ctx, args, cancellation_manager=cancellation_manager))
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\eager\function.py", line 545, in call
ctx=ctx)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\eager\execute.py", line 67, in quick_execute
six.raise_from(core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.InvalidArgumentError: assertion failed: [Condition x == y did not hold element-wise:] [x (loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/Shape_1:0) = ] [64 1] [y (loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/strided_slice:0) = ] [64 14]
[[node loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/assert_equal_1/Assert/Assert (defined at F:/Pycharm_projects/my_fun_project/Fake or real news/fake-or-real-news/bitcoin.py:41) ]] [Op:__inference_distributed_function_2970]
Function call stack:
distributed_function
解决方案
该错误是因为您的output_shape和label_shape不匹配.这是您创建的模型的体系结构:
.
如您所见，模型输出(batch_size, 14, 16)，但是您提供的标签的形状为(batch_size, 16).
为解决此问题，请尝试在最终的Dense层之前添加Flatten层.
代码:
model = Sequential()
model.add(tf.keras.layers.Dense(256, input_shape = (28,28)))
model.add(tf.keras.layers.Conv1D(kernel_size=4, strides=1, filters=4, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=3, strides=1, activation="relu", filters=3))
model.add(tf.keras.layers.Dense(128, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=2, filters=2, strides=1, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=1, filters=1, strides=1, activation="relu"))
model.add(tf.keras.layers.Dense(64, activation="relu"))
model.add(tf.keras.layers.MaxPool1D(pool_size=2, strides=1))
model.add(tf.keras.layers.Dense(256, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=4, filters=4, strides=1, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=3, filters=3, strides=1, activation="relu"))
model.add(tf.keras.layers.MaxPool1D(pool_size=2, strides=1))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(128, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=2, filters=2, strides=1, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=1, filters=1, strides=1, activation="relu"))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(64, activation="relu"))
model.add(tf.keras.layers.Dense(16, activation="softmax"))
现在您的模型架构如下:
现在，您的模型具有匹配的形状，并且将进行训练而没有任何问题.
i am training a mnist CNN. When i ran my code the problem is coming . I tried other answers but they do not work. I am a new to TensorFlow so can someone explain me this error. Here is my code. i am using Pycharm 2020.2. and Python 3.6 in anaconda. There is no help i could find.
import tensorflow as tf
from tensorflow.keras.models import Sequential
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_train, axis=1)
model = Sequential()
model.add(tf.keras.layers.Dense(256))
model.add(tf.keras.layers.Conv1D(kernel_size=4, strides=1, filters=4, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=3, strides=1, activation="relu", filters=3))
model.add(tf.keras.layers.Dense(128, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=2, filters=2, strides=1, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=1, filters=1, strides=1, activation="relu"))
model.add(tf.keras.layers.Dense(64, activation="relu"))
model.add(tf.keras.layers.MaxPool1D(pool_size=2, strides=1))
model.add(tf.keras.layers.Dense(256, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=4, filters=4, strides=1, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=3, filters=3, strides=1, activation="relu"))
model.add(tf.keras.layers.MaxPool1D(pool_size=2, strides=1))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(128, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=2, filters=2, strides=1, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=1, filters=1, strides=1, activation="relu"))
model.add(tf.keras.layers.Dense(64, activation="relu"))
model.add(tf.keras.layers.Dense(16, activation="softmax"))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x=x_train, y=y_train, batch_size=64, epochs=5, shuffle=True, validation_split=0.1)
model.summary()
it is giving the error:
Train on 54000 samples, validate on 6000 samples
Epoch 1/5
2020-09-09 15:16:16.953428: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2020-09-09 15:16:17.146701: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-09-09 15:16:17.741916: W tensorflow/stream_executor/gpu/redzone_allocator.cc:312] Internal: Invoking GPU asm compilation is supported on Cuda non-Windows platforms only
Relying on driver to perform ptx compilation. This message will be only logged once.
2020-09-09 15:16:18.085250: W tensorflow/core/common_runtime/base_collective_executor.cc:217] BaseCollectiveExecutor::StartAbort Invalid argument: assertion failed: [Condition x == y did not hold element-wise:] [x (loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/Shape_1:0) = ] [64 1] [y (loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/strided_slice:0) = ] [64 14]
[[{{node loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/assert_equal_1/Assert/Assert}}]]
64/54000 [..............................] - ETA: 39:34Traceback (most recent call last):
File "F:\anaconda\envs\tensorflow1\lib\site-packages\IPython\core\interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-2-d2317d03e1c1>", line 1, in <module>
runfile('F:/Pycharm_projects/my_fun_project/Fake or real news/fake-or-real-news/bitcoin.py', wdir='F:/Pycharm_projects/my_fun_project/Fake or real news/fake-or-real-news')
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Program Files\JetBrains\PyCharm Community Edition 2019.3.3\plugins\python-ce\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "F:/Pycharm_projects/my_fun_project/Fake or real news/fake-or-real-news/bitcoin.py", line 41, in <module>
model.fit(x=x_train, y=y_train, batch_size=64, epochs=5, shuffle=True, validation_split=0.1)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 819, in fit
use_multiprocessing=use_multiprocessing)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 342, in fit
total_epochs=epochs)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 128, in run_one_epoch
batch_outs = execution_function(iterator)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 98, in execution_function
distributed_function(input_fn))
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 568, in __call__
result = self._call(*args, **kwds)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 632, in _call
return self._stateless_fn(*args, **kwds)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\eager\function.py", line 2363, in __call__
return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\eager\function.py", line 1611, in _filtered_call
self.captured_inputs)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\eager\function.py", line 1692, in _call_flat
ctx, args, cancellation_manager=cancellation_manager))
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\eager\function.py", line 545, in call
ctx=ctx)
File "F:\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\eager\execute.py", line 67, in quick_execute
six.raise_from(core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.InvalidArgumentError: assertion failed: [Condition x == y did not hold element-wise:] [x (loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/Shape_1:0) = ] [64 1] [y (loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/strided_slice:0) = ] [64 14]
[[node loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/assert_equal_1/Assert/Assert (defined at F:/Pycharm_projects/my_fun_project/Fake or real news/fake-or-real-news/bitcoin.py:41) ]] [Op:__inference_distributed_function_2970]
Function call stack:
distributed_function
解决方案
The error is because your output_shape and label_shape don't match.This is the architecture of the model you created:
.
As you can see, your model outputs (batch_size, 14, 16) but the labels you provide have a shape of (batch_size, 16).
In order to fix this try adding the Flatten layer before your final Dense layers.
Code:
model = Sequential()
model.add(tf.keras.layers.Dense(256, input_shape = (28,28)))
model.add(tf.keras.layers.Conv1D(kernel_size=4, strides=1, filters=4, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=3, strides=1, activation="relu", filters=3))
model.add(tf.keras.layers.Dense(128, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=2, filters=2, strides=1, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=1, filters=1, strides=1, activation="relu"))
model.add(tf.keras.layers.Dense(64, activation="relu"))
model.add(tf.keras.layers.MaxPool1D(pool_size=2, strides=1))
model.add(tf.keras.layers.Dense(256, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=4, filters=4, strides=1, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=3, filters=3, strides=1, activation="relu"))
model.add(tf.keras.layers.MaxPool1D(pool_size=2, strides=1))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(128, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=2, filters=2, strides=1, activation="relu"))
model.add(tf.keras.layers.Conv1D(kernel_size=1, filters=1, strides=1, activation="relu"))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(64, activation="relu"))
model.add(tf.keras.layers.Dense(16, activation="softmax"))
Now your model architecture looks like this:
Now, your model has matching shapes and will train without any issues.
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