tf.compat.v1.train.create_global_step

Migrate to TF2

With the deprecation of global graphs, TF no longer tracks variables in collections. In other words, there are no global variables in TF2. Thus, the global step functions have been removed (get_or_create_global_step, create_global_step, get_global_step) . You have two options for migrating:

1. Create a Keras optimizer, which generates an iterations variable. This variable is automatically incremented when calling apply_gradients.
2. Manually create and increment a tf.Variable.

Below is an example of migrating away from using a global step to using a Keras optimizer:

Define a dummy model and loss:

def compute_loss(x):
  v = tf.Variable(3.0)
  y = x * v
  loss = x * 5 - x * v
  return loss, [v]

Before migrating:

g = tf.Graph()
with g.as_default():
  x = tf.compat.v1.placeholder(tf.float32, [])
  loss, var_list = compute_loss(x)
  global_step = tf.compat.v1.train.create_global_step()
  global_init = tf.compat.v1.global_variables_initializer()
  optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.1)
  train_op = optimizer.minimize(loss, global_step, var_list)
sess = tf.compat.v1.Session(graph=g)
sess.run(global_init)
print("before training:", sess.run(global_step))
before training: 0
sess.run(train_op, feed_dict={x: 3})
print("after training:", sess.run(global_step))
after training: 1

Migrating to a Keras optimizer:

optimizer = tf.keras.optimizers.SGD(.01)
print("before training:", optimizer.iterations.numpy())
before training: 0
with tf.GradientTape() as tape:
  loss, var_list = compute_loss(3)
  grads = tape.gradient(loss, var_list)
  optimizer.apply_gradients(zip(grads, var_list))
print("after training:", optimizer.iterations.numpy())
after training: 1