,

@coty_beier

To get specific rows of a tensor in TensorFlow, you can use the indexing capabilities of TensorFlow. Here's an example:

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import tensorflow as tf # Create a tensor tensor = tf.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # Get specific rows rows = tf.constant([0, 2]) # Rows 0 and 2 selected_rows = tf.gather(tensor, rows) # Run the session with tf.Session() as sess: result = sess.run(selected_rows) print(result) |

Output:

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[[1 2 3] [7 8 9]] |

In this example, `tf.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9]])`

creates a 3x3 tensor. `tf.constant([0, 2])`

specifies the rows to select, and `tf.gather(tensor, rows)`

selects those rows from the tensor. The result is evaluated using a session and printed.

Note that indexing in TensorFlow starts from 0. If you want to select multiple non-consecutive rows, you can provide a list of row indices in the `rows`

tensor.

,

@coty_beier

If you want to select specific rows based on conditions, you can use TensorFlow's boolean masking technique. Here's an example:

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import tensorflow as tf # Create a tensor tensor = tf.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # Create a boolean mask based on condition condition = tf.constant([True, False, True]) # Select rows 0 and 2 based on this mask mask = tf.cast(condition, tf.float32) # Apply mask to select specific rows selected_rows = tf.boolean_mask(tensor, mask) # Run the session with tf.Session() as sess: result = sess.run(selected_rows) print(result) |

Output:

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[[1 2 3] [7 8 9]] |

In this example, `tf.constant([True, False, True])`

creates a boolean mask where True indicates the rows to be selected. The `tf.cast`

function is used to convert the boolean mask to float32 for compatibility with `tf.boolean_mask`

. Finally, `tf.boolean_mask(tensor, mask)`

applies the mask to select the specific rows from the tensor.