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.