tensorflow Creating a custom operation with tf.py_func (CPU only) Why to use tf.py_func
Example
The tf.py_func() operator enables you to run arbitrary Python code in the middle of a TensorFlow graph. It is particularly convenient for wrapping custom NumPy operators for which no equivalent TensorFlow operator (yet) exists. Adding tf.py_func() is an alternative to using sess.run() calls inside the graph.
Another way of doing that is to cut the graph in two parts:
# Part 1 of the graph
inputs = ... # in the TF graph
# Get the numpy array and apply func
val = sess.run(inputs) # get the value of inputs
output_val = func(val) # numpy array
# Part 2 of the graph
output = tf.placeholder(tf.float32, shape=...)
train_op = ...
# We feed the output_val to the tensor output
sess.run(train_op, feed_dict={output: output_val})
With tf.py_func this is much easier:
# Part 1 of the graph
inputs = ...
# call to tf.py_func
output = tf.py_func(func, [inputs], [tf.float32])[0]
# Part 2 of the graph
train_op = ...
# Only one call to sess.run, no need of a intermediate placeholder
sess.run(train_op)
