Is there an easy way to get something like Keras model.summary in Tensorflow?

Question

I have been working with Keras and really liked the model.summary() It gives a good overview of the size of the different layers and especially an overview of the number of parameters the model has.

Is there a similar function in Tensorflow? I could find nothing on Stackoverflow or the Tensorflow API documentation.

Answer 1

Looks like you can use Slim

Example:

import numpy as np  from tensorflow.python.layers import base import tensorflow as tf import tensorflow.contrib.slim as slim  x = np.zeros((1,4,4,3)) x_tf = tf.convert_to_tensor(x, np.float32) z_tf = tf.layers.conv2d(x_tf, filters=32, kernel_size=(3,3))  def model_summary():     model_vars = tf.trainable_variables()     slim.model_analyzer.analyze_vars(model_vars, print_info=True)  model_summary() 

Output:

--------- Variables: name (type shape) [size] --------- conv2d/kernel:0 (float32_ref 3x3x3x32) [864, bytes: 3456] conv2d/bias:0 (float32_ref 32) [32, bytes: 128] Total size of variables: 896 Total bytes of variables: 3584 

Also here is an example of custom function to print model summary: https://github.com/NVlabs/stylegan/blob/f3a044621e2ab802d40940c16cc86042ae87e100/dnnlib/tflib/network.py#L507

If you already have .pb tensorflow model you can use: inspect_pb.py to print model info or use tensorflow summarize_graph tool with --print_structure flag, also it's nice that it can detect input and output names.