tf.compat.v1.train.get_global_step

Get the global step tensor.

tf.compat.v1.train.get_global_step(
    graph=None
)

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.get_or_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

Using get_global_step:

with g.as_default():
  print(sess.run(tf.compat.v1.train.get_global_step()))
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

Description

Used in the notebooks

Used in the guide Used in the tutorials

The global step tensor must be an integer variable. We first try to find it in the collection GLOBAL_STEP, or by name global_step:0.

graph The graph to find the global step in. If missing, use default graph.

The global step variable, or None if none was found.

TypeError If the global step tensor has a non-integer type, or if it is not a Variable.