This document is relevant for: Inf1

TensorFlow 2.x (tensorflow-neuron) analyze_model API#

Method#

tensorflow.neuron.analyze_model

Description#

Analyzes a keras.Model or a Python callable that can be decorated by tf.function for it’s compatibility with Neuron. It displays supported vs. unsupported operators in the model as well as percentages and counts of each operator and returns a dictionary with operator statistics.

Arguments#

  • func: The keras.Model or function to be analyzed.

  • example_inputs: A tf.Tensor or a tuple/list/dict of tf.Tensor objects for tracing the function. When example_inputs is a tf.Tensor or a list of tf.Tensor objects, we expect func to have calling signature func(example_inputs). Otherwise, the expectation is that inference on func is done by calling func(*example_inputs) when example_inputs is a tuple, or func(**example_inputs) when example_inputs is a dict. The case where func accepts mixed positional and keyword arguments is currently unsupported.

Returns#

  • A results dict with these keys: ``’percent_supported’, ‘supported_count’,

‘total_count’, ‘supported_operators’, ‘unsupported_operators’, ‘operators’, ‘operator_count’``.

Example Usage#

import tensorflow as tf
import tensorflow.neuron as tfn

input0 = tf.keras.layers.Input(3)
dense0 = tf.keras.layers.Dense(3)(input0)
model = tf.keras.Model(inputs=[input0], outputs=[dense0])
example_inputs = tf.random.uniform([1, 3])
results = tfn.analyze_model(model, example_inputs)
print(results)

# expected output
'''
BiasAdd
    MatMul
    100.00% of all operations (2 of 2) are supported
    {'percent_supported': 100.0, 'supported_count': 2, 'total_count': 2,
    'supported_operators': {'BiasAdd', 'MatMul'}, 'unsupported_operators': [],
    'operators': ['BiasAdd', 'MatMul'], 'operator_count': {'MatMul': 1, 'BiasAdd': 1}}
    '''

This document is relevant for: Inf1