Simple method for ensuring that the gradient flows through all your variables in TensorFlow.

ensure_gradient_flow.py

import tensorflow as tf # 1.3.0
import numpy as np

# model + input
w = tf.Variable(1, name="w", dtype=tf.float32 ) # parameter to optimize for
x = tf.placeholder(shape=(),  dtype=tf.float32, name="x") # input

# graph operations
diff_op = tf.multiply(w, x) # an operation that is differentiable
non_diff_op = tf.cast(tf.equal(diff_op, 0), dtype=tf.float32) # operation that is non differentiable


# check whether some gradient can flow to your trainable parameters
# rudimentary check for gradient flow
def ensure_gradient_flow(operations):
    tf_params = tf.trainable_variables()

    no_flows = 0
    for op in operations:
        gradients = tf.gradients(op, tf_params)
        at_least_one_gradient_flows = False
        for c in zip(gradients, tf_params):
            if not type(c[0]).__name__=="NoneType":
                at_least_one_gradient_flows = True
                break
        if not at_least_one_gradient_flows:
            print("No gradient flow for operation '%s'"%op.name)
            no_flows += 1
    if no_flows==0:
        print("Operations [%s] have at least 1 gradient for at least 1 parameter"%([o.name for o in operations]))

print(tf.global_variables())
ensure_gradient_flow([diff_op])
ensure_gradient_flow([non_diff_op])