,

@alivia

To update a subset of a 2D tensor in TensorFlow, you can follow these steps:

- Create a placeholder for the original tensor, which will hold the initial values.
- Use the tf.****ter_update function to update the desired subset of the tensor.
- Create a TensorFlow session and initialize the variables.
- Run the session and pass the necessary inputs to update the tensor.

Here's an example code snippet that demonstrates the process:

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import tensorflow as tf # Create the original tensor with initial values original_tensor = tf.Variable([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # Create a placeholder for the new values new_values = tf.placeholder(tf.int32, shape=[2, 2]) # Define the indices to update row_indices = tf.constant([0, 1]) col_indices = tf.constant([1, 2]) # Update the subset of the tensor update_op = tf.****ter_nd_update(original_tensor, indices=tf.transpose([row_indices, col_indices]), updates=new_values) # Initialize the variables init_op = tf.global_variables_initializer() # Create a TensorFlow session with tf.Session() as sess: # Run initialization sess.run(init_op) # Define the new values to update new_vals = [[10, 11], [12, 13]] # Run the update operation sess.run(update_op, feed_dict={new_values: new_vals}) # Print the updated tensor print(sess.run(original_tensor)) |

In this example, the original tensor is a 3x3 matrix. We want to update the values at indices (0, 1) and (1, 2) with new values. The `tf.****ter_nd_update`

function is used to perform the update operation. After running the session, the updated tensor is printed.

Note: The `tf.****ter_nd_update`

function requires TensorFlow 1.15 or higher.