ValueError: Dimensions must be equal, but are 32 and 64 for '{{node conv2/SparseTensorDenseMatMul/SparseTensorDenseMatMul}} = SparseTensorDenseMatMul[T=DT_FLOAT, Tindices=DT_INT64, adjoint_a=false, adjoint_b=false](conv2/SparseReorder, conv2/SparseReorder:1, conv2/SparseTensor_1/dense_shape, conv2/Reshape)' with input shapes: [992,2], [992], [2], [64,16384] and with input tensors computed as partial shapes: input[2] = [32,32].
时间: 2024-02-19 20:02:07 浏览: 39
这个错误通常出现在使用神经网络时,因为某些张量的维度不匹配导致无法进行矩阵乘法运算。在这个例子中,矩阵乘法节点 '{{node conv2/SparseTensorDenseMatMul/SparseTensorDenseMatMul}}' 的输入张量维度不匹配,有一个张量的维度是32,另一个是64。你需要检查你的代码,找出哪些张量的维度不匹配,并确保它们具有相同的维度或者进行相应的维度调整。
相关问题
ValueError: Dimensions must be equal, but are 3 and 6 for '{{node mean_squared_error/SquaredDifference}} = SquaredDifference[T=DT_FLOAT](sequential/dense/BiasAdd, IteratorGetNext:1)' with input shapes: [?,3], [?,6].
这个错误通常是由于输入数据的维度不匹配引起的。在这种情况下,您正在使用一个具有3个特征的模型来预测一个具有6个特征的目标变量,从而导致了维度不匹配的错误。
要解决这个问题,您可以检查以下几点:
1. 确保您的输入数据和目标数据的维度是一致的。如果您的模型需要预测6个特征的目标变量,那么您的训练集和测试集中的目标数据也应该是具有6个特征的。
2. 检查您的模型架构,确保模型的输出层与目标数据的维度相匹配。如果您的模型输出层的维度与目标数据不匹配,可以尝试调整模型架构或者重新定义目标数据。
3. 如果您正在使用某个损失函数(例如均方误差)来计算模型的损失,确保损失函数的输入维度与模型输出和目标数据的维度相匹配。如果不匹配,可以尝试使用适当的损失函数或者重新定义输入数据。
总之,这个错误通常是由维度不匹配引起的,需要检查输入数据、模型架构和损失函数等方面来解决问题。
ValueError: Dimensions must be equal, but are 2 and 3 for '{{node mean_squared_error/SquaredDifference}} = SquaredDifference[T=DT_FLOAT](sequential_6/dense_4/BiasAdd, IteratorGetNext:1)' with input shapes: [?,2], [?,2,3].
This error message indicates that there is a dimension mismatch between the two input tensors of the SquaredDifference operation. The first tensor has a shape of (?,2) which means that it has an unknown number of rows and 2 columns, while the second tensor has a shape of (?,2,3) which means that it has an unknown number of rows, 2 columns, and 3 channels.
To solve this issue, you need to make sure that the dimensions of the two tensors match. One way to do this is to reshape the first tensor to have a shape of (?, 2, 1) before passing it to the SquaredDifference operation. This will make the two tensors have the same dimensions, and the operation can be performed without any issues.
Here's an example code snippet that demonstrates how to reshape the tensor:
```
import tensorflow as tf
# Define the two input tensors
a = tf.placeholder(tf.float32, [None, 2])
b = tf.placeholder(tf.float32, [None, 2, 3])
# Reshape tensor a to have a shape of (?, 2, 1)
a_reshaped = tf.reshape(a, [-1, 2, 1])
# Perform the SquaredDifference operation
squared_diff = tf.square(tf.subtract(a_reshaped, b))
# Define the mean squared error loss
mse_loss = tf.reduce_mean(squared_diff)
# Define the optimizer and training operation
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(mse_loss)
```
I hope this helps! Let me know if you have any more questions.