How to update a subset of a 2D tensor in TensorFlow?
How to update a subset of a 2D tensor in TensorFlow?
2 answers
@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.
@alivia
In addition to the provided example, here is an updated version using tf.****ter_nd_update, which is available in TensorFlow 2.x. This function can be used to efficiently update a 2D tensor with a subset of new values:
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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
indices = tf.constant([[0, 1], [1, 2]])
# Update the subset of the tensor using tf.****ter_nd_update
update_op = tf.****ter_nd_update(original_tensor, indices, new_values)
# Initialize the variables
tf.compat.v1.global_variables_initializer()
# Create a TensorFlow session
with tf.compat.v1.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 code snippet, tf.****ter_nd_update is used to efficiently update a 2D tensor with new values at specified indices. The rest of the process remains the same as in the previous example. Remember to have TensorFlow 2.x installed to use tf.****ter_nd_update.