Effective TensorFlow 2.0

There are multiple changes in TensorFlow 2.0 to make TensorFlow users more productive. TensorFlow 2.0 removes redundant APIs, makes APIs more consistent (Unified RNNs, Unified Optimizers), and better integrates with the Python runtime with Eager execution.

Many RFCs have explained the changes that have gone into making TensorFlow 2.0. This guide presents a vision for what development in TensorFlow 2.0 should look like. It's assumed you have some familiarity with TensorFlow 1.x.

A brief summary of major changes

API Cleanup

Many APIs are either gone or moved in TF 2.0. Some of the major changes include removing tf.app, tf.flags, and tf.logging in favor of the now open-source absl-py, rehoming projects that lived in tf.contrib, and cleaning up the main tf.* namespace by moving lesser used functions into subpackages like tf.math. Some APIs have been replaced with their 2.0 equivalents - tf.summary, tf.keras.metrics, and tf.keras.optimizers. The easiest way to automatically apply these renames is to use the v2 upgrade script.

Eager execution

TensorFlow 1.X requires users to manually stitch together an abstract syntax tree (the graph) by making tf.* API calls. It then requires users to manually compile the abstract syntax tree by passing a set of output tensors and input tensors to a session.run() call. TensorFlow 2.0 executes eagerly (like Python normally does) and in 2.0, graphs and sessions should feel like implementation details.

One notable byproduct of eager execution is that tf.control_dependencies() is no longer required, as all lines of code execute in order (within a tf.function, code with side effects execute in the order written).

No more globals

TensorFlow 1.X relied heavily on implicitly global namespaces. When you called tf.Variable(), it would be put into the default graph, and it would remain there, even if you lost track of the Python variable pointing to it. You could then recover that tf.Variable, but only if you knew the name that it had been created with. This was difficult to do if you were not in control of the variable's creation. As a result, all sorts of mechanisms proliferated to attempt to help users find their variables again, and for frameworks to find user-created variables: Variable scopes, global collections, helper methods like tf.get_global_step(), tf.global_variables_initializer(), optimizers implicitly computing gradients over all trainable variables, and so on. TensorFlow 2.0 eliminates all of these mechanisms (Variables 2.0 RFC) in favor of the default mechanism: Keep track of your variables! If you lose track of a tf.Variable, it gets garbage collected.

The requirement to track variables creates some extra work for the user, but with Keras objects (see below), the burden is minimized.

Functions, not sessions

A session.run() call is almost like a function call: You specify the inputs and the function to be called, and you get back a set of outputs. In TensorFlow 2.0, you can decorate a Python function using tf.function() to mark it for JIT compilation so that TensorFlow runs it as a single graph (Functions 2.0 RFC). This mechanism allows TensorFlow 2.0 to gain all of the benefits of graph mode:

  • Performance: The function can be optimized (node pruning, kernel fusion, etc.)
  • Portability: The function can be exported/reimported (SavedModel 2.0 RFC), allowing users to reuse and share modular TensorFlow functions.
# TensorFlow 1.X
outputs = session.run(f(placeholder), feed_dict={placeholder: input})
# TensorFlow 2.0
outputs = f(input)

With the power to freely intersperse Python and TensorFlow code, users can take advantage of Python's expressiveness. But portable TensorFlow executes in contexts without a Python interpreter, such as mobile, C++, and JavaScript. To help users avoid having to rewrite their code when adding @tf.function, AutoGraph converts a subset of Python constructs into their TensorFlow equivalents:

  • for/while -> tf.while_loop (break and continue are supported)
  • if -> tf.cond
  • for _ in dataset -> dataset.reduce

AutoGraph supports arbitrary nestings of control flow, which makes it possible to performantly and concisely implement many complex ML programs such as sequence models, reinforcement learning, custom training loops, and more.

Recommendations for idiomatic TensorFlow 2.0

Refactor your code into smaller functions

A common usage pattern in TensorFlow 1.X was the "kitchen sink" strategy, where the union of all possible computations was preemptively laid out, and then selected tensors were evaluated via session.run(). In TensorFlow 2.0, users should refactor their code into smaller functions that are called as needed. In general, it's not necessary to decorate each of these smaller functions with tf.function; only use tf.function to decorate high-level computations - for example, one step of training or the forward pass of your model.

Use Keras layers and models to manage variables

Keras models and layers offer the convenient variables and trainable_variables properties, which recursively gather up all dependent variables. This makes it easy to manage variables locally to where they are being used.


def dense(x, W, b):
  return tf.nn.sigmoid(tf.matmul(x, W) + b)

def multilayer_perceptron(x, w0, b0, w1, b1, w2, b2 ...):
  x = dense(x, w0, b0)
  x = dense(x, w1, b1)
  x = dense(x, w2, b2)

# You still have to manage w_i and b_i, and their shapes are defined far away from the code.

with the Keras version:

# Each layer can be called, with a signature equivalent to linear(x)
layers = [tf.keras.layers.Dense(hidden_size, activation=tf.nn.sigmoid) for _ in range(n)]
perceptron = tf.keras.Sequential(layers)

# layers[3].trainable_variables => returns [w3, b3]
# perceptron.trainable_variables => returns [w0, b0, ...]

Keras layers/models inherit from tf.train.Checkpointable and are integrated with @tf.function, which makes it possible to directly checkpoint or export SavedModels from Keras objects. You do not necessarily have to use Keras's .fit() API to take advantage of these integrations.

Here's a transfer learning example that demonstrates how Keras makes it easy to collect a subset of relevant variables. Let's say you're training a multi-headed model with a shared trunk:

trunk = tf.keras.Sequential([...])
head1 = tf.keras.Sequential([...])
head2 = tf.keras.Sequential([...])

path1 = tf.keras.Sequential([trunk, head1])
path2 = tf.keras.Sequential([trunk, head2])

# Train on primary dataset
for x, y in main_dataset:
  with tf.GradientTape() as tape:
    prediction = path1(x)
    loss = loss_fn_head1(prediction, y)
  # Simultaneously optimize trunk and head1 weights.
  gradients = tape.gradient(loss, path1.trainable_variables)
  optimizer.apply_gradients(zip(gradients, path1.trainable_variables))

# Fine-tune second head, reusing the trunk
for x, y in small_dataset:
  with tf.GradientTape() as tape:
    prediction = path2(x)
    loss = loss_fn_head2(prediction, y)
  # Only optimize head2 weights, not trunk weights
  gradients = tape.gradient(loss, head2.trainable_variables)
  optimizer.apply_gradients(zip(gradients, head2.trainable_variables))

# You can publish just the trunk computation for other people to reuse.
tf.saved_model.save(trunk, output_path)

Combine tf.data.Datasets and @tf.function

When iterating over training data that fits in memory, feel free to use regular Python iteration. Otherwise, tf.data.Dataset is the best way to stream training data from disk. Datasets are iterables (not iterators), and work just like other Python iterables in Eager mode. You can fully utilize dataset async prefetching/streaming features by wrapping your code in tf.function(), which replaces Python iteration with the equivalent graph operations using AutoGraph.

def train(model, dataset, optimizer):
  for x, y in dataset:
    with tf.GradientTape() as tape:
      prediction = model(x)
      loss = loss_fn(prediction, y)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

If you use the Keras .fit() API, you won't have to worry about dataset iteration.

model.compile(optimizer=optimizer, loss=loss_fn)

Take advantage of AutoGraph with Python control flow

AutoGraph provides a way to convert data-dependent control flow into graph-mode equivalents like tf.cond and tf.while_loop.

One common place where data-dependent control flow appears is in sequence models. tf.keras.layers.RNN wraps an RNN cell, allowing you to either statically or dynamically unroll the recurrence. For demonstration's sake, you could reimplement dynamic unroll as follows:

class DynamicRNN(tf.keras.Model):

  def __init__(self, rnn_cell):
    super(DynamicRNN, self).__init__(self)
    self.cell = rnn_cell

  def call(self, input_data):
    # [batch, time, features] -> [time, batch, features]
    input_data = tf.transpose(input_data, [1, 0, 2])
    outputs = tf.TensorArray(tf.float32, input_data.shape[0])
    state = self.cell.zero_state(input_data.shape[1], dtype=tf.float32)
    for i in tf.range(input_data.shape[0]):
      output, state = self.cell(input_data[i], state)
      outputs = outputs.write(i, output)
    return tf.transpose(outputs.stack(), [1, 0, 2]), state

For a more detailed overview of AutoGraph's features, see the guide.

tf.metrics aggregates data and tf.summary logs them

To log summaries, use tf.summary.(scalar|histogram|...) and redirect it to a writer using a context manager. (If you omit the context manager, nothing happens.) Unlike TF 1.x, the summaries are emitted directly to the writer; there is no separate "merge" op and no separate add_summary() call, which means that the step value must be provided at the callsite.

summary_writer = tf.summary.create_file_writer('/tmp/summaries')
with summary_writer.as_default():
  tf.summary.scalar('loss', 0.1, step=42)

To aggregate data before logging them as summaries, use tf.metrics. Metrics are stateful: They accumulate values and return a cumulative result when you call .result(). Clear accumulated values with .reset_states().

def train(model, optimizer, dataset, log_freq=10):
  avg_loss = tf.keras.metrics.Mean(name='loss', dtype=tf.float32)
  for images, labels in dataset:
    loss = train_step(model, optimizer, images, labels)
    if tf.equal(optimizer.iterations % log_freq, 0):
      tf.summary.scalar('loss', avg_loss.result(), step=optimizer.iterations)

def test(model, test_x, test_y, step_num):
  loss = loss_fn(model(test_x), test_y)
  tf.summary.scalar('loss', loss, step=step_num)

train_summary_writer = tf.summary.create_file_writer('/tmp/summaries/train')
test_summary_writer = tf.summary.create_file_writer('/tmp/summaries/test')

with train_summary_writer.as_default():
  train(model, optimizer, dataset)

with test_summary_writer.as_default():
  test(model, test_x, test_y, optimizer.iterations)

Visualize the generated summaries by pointing TensorBoard at the summary log directory:

tensorboard --logdir /tmp/summaries