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

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.