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Eager essentials

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TensorFlow's eager execution is an imperative programming environment that evaluates operations immediately, without building graphs: operations return concrete values instead of constructing a computational graph to run later. This makes it easy to get started with TensorFlow and debug models, and it reduces boilerplate as well. To follow along with this guide, run the code samples below in an interactive python interpreter.

Eager execution is a flexible machine learning platform for research and experimentation, providing:

  • An intuitive interface—Structure your code naturally and use Python data structures. Quickly iterate on small models and small data.
  • Easier debugging—Call ops directly to inspect running models and test changes. Use standard Python debugging tools for immediate error reporting.
  • Natural control flow—Use Python control flow instead of graph control flow, simplifying the specification of dynamic models.

Eager execution supports most TensorFlow operations and GPU acceleration.

Setup and basic usage

from __future__ import absolute_import, division, print_function, unicode_literals

  # %tensorflow_version only exists in Colab.
  %tensorflow_version 2.x  #gpu
except Exception:
import tensorflow as tf

import cProfile

In Tensorflow 2.0, eager execution is enabled by default.


Now you can run TensorFlow operations and the results will return immediately:

x = [[2.]]
m = tf.matmul(x, x)
print("hello, {}".format(m))
hello, [[4.]]

Enabling eager execution changes how TensorFlow operations behave—now they immediately evaluate and return their values to Python. tf.Tensor objects reference concrete values instead of symbolic handles to nodes in a computational graph. Since there isn't a computational graph to build and run later in a session, it's easy to inspect results using print() or a debugger. Evaluating, printing, and checking tensor values does not break the flow for computing gradients.

Eager execution works nicely with NumPy. NumPy operations accept tf.Tensor arguments. TensorFlow math operations convert Python objects and NumPy arrays to tf.Tensor objects. The tf.Tensor.numpy method returns the object's value as a NumPy ndarray.

a = tf.constant([[1, 2],
                 [3, 4]])
[[1 2]
 [3 4]], shape=(2, 2), dtype=int32)
# Broadcasting support
b = tf.add(a, 1)
[[2 3]
 [4 5]], shape=(2, 2), dtype=int32)
# Operator overloading is supported
print(a * b)
[[ 2  6]
 [12 20]], shape=(2, 2), dtype=int32)
# Use NumPy values
import numpy as np

c = np.multiply(a, b)
[[ 2  6]
 [12 20]]
# Obtain numpy value from a tensor:
# => [[1 2]
#     [3 4]]
[[1 2]
 [3 4]]

Dynamic control flow

A major benefit of eager execution is that all the functionality of the host language is available while your model is executing. So, for example, it is easy to write fizzbuzz:

def fizzbuzz(max_num):
  counter = tf.constant(0)
  max_num = tf.convert_to_tensor(max_num)
  for num in range(1, max_num.numpy()+1):
    num = tf.constant(num)
    if int(num % 3) == 0 and int(num % 5) == 0:
    elif int(num % 3) == 0:
    elif int(num % 5) == 0:
    counter += 1

This has conditionals that depend on tensor values and it prints these values at runtime.

Eager training

Computing gradients

Automatic differentiation is useful for implementing machine learning algorithms such as backpropagation for training neural networks. During eager execution, use tf.GradientTape to trace operations for computing gradients later.

You can use tf.GradientTape to train and/or compute gradients in eager. It is especially useful for complicated training loops.

Since different operations can occur during each call, all forward-pass operations get recorded to a "tape". To compute the gradient, play the tape backwards and then discard. A particular tf.GradientTape can only compute one gradient; subsequent calls throw a runtime error.

w = tf.Variable([[1.0]])
with tf.GradientTape() as tape:
  loss = w * w

grad = tape.gradient(loss, w)
print(grad)  # => tf.Tensor([[ 2.]], shape=(1, 1), dtype=float32)
tf.Tensor([[2.]], shape=(1, 1), dtype=float32)

Train a model

The following example creates a multi-layer model that classifies the standard MNIST handwritten digits. It demonstrates the optimizer and layer APIs to build trainable graphs in an eager execution environment.

# Fetch and format the mnist data
(mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data()

dataset =
  (tf.cast(mnist_images[...,tf.newaxis]/255, tf.float32),
dataset = dataset.shuffle(1000).batch(32)
# Build the model
mnist_model = tf.keras.Sequential([
  tf.keras.layers.Conv2D(16,[3,3], activation='relu',
                         input_shape=(None, None, 1)),
  tf.keras.layers.Conv2D(16,[3,3], activation='relu'),

Even without training, call the model and inspect the output in eager execution:

for images,labels in dataset.take(1):
  print("Logits: ", mnist_model(images[0:1]).numpy())
Logits:  [[ 0.01285976 -0.00608379  0.07428933 -0.02349363 -0.05245905 -0.04613611
   0.04961142 -0.0794637  -0.04409777 -0.07776338]]

While keras models have a builtin training loop (using the fit method), sometimes you need more customization. Here's an example, of a training loop implemented with eager:

optimizer = tf.keras.optimizers.Adam()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

loss_history = []
def train_step(images, labels):
  with tf.GradientTape() as tape:
    logits = mnist_model(images, training=True)
    # Add asserts to check the shape of the output.
    tf.debugging.assert_equal(logits.shape, (32, 10))
    loss_value = loss_object(labels, logits)

  grads = tape.gradient(loss_value, mnist_model.trainable_variables)
  optimizer.apply_gradients(zip(grads, mnist_model.trainable_variables))
def train():
  for epoch in range(3):
    for (batch, (images, labels)) in enumerate(dataset):
      train_step(images, labels)
    print ('Epoch {} finished'.format(epoch))
Epoch 0 finished
Epoch 1 finished
Epoch 2 finished
import matplotlib.pyplot as plt

plt.xlabel('Batch #')
plt.ylabel('Loss [entropy]')
Text(0, 0.5, 'Loss [entropy]')

Variables and optimizers

tf.Variable objects store mutable tf.Tensor values accessed during training to make automatic differentiation easier. The parameters of a model can be encapsulated in classes as variables.

Better encapsulate model parameters by using tf.Variable with tf.GradientTape. For example, the automatic differentiation example above can be rewritten:

class Model(tf.keras.Model):
  def __init__(self):
    super(Model, self).__init__()
    self.W = tf.Variable(5., name='weight')
    self.B = tf.Variable(10., name='bias')
  def call(self, inputs):
    return inputs * self.W + self.B

# A toy dataset of points around 3 * x + 2
training_inputs = tf.random.normal([NUM_EXAMPLES])
noise = tf.random.normal([NUM_EXAMPLES])
training_outputs = training_inputs * 3 + 2 + noise

# The loss function to be optimized
def loss(model, inputs, targets):
  error = model(inputs) - targets
  return tf.reduce_mean(tf.square(error))

def grad(model, inputs, targets):
  with tf.GradientTape() as tape:
    loss_value = loss(model, inputs, targets)
  return tape.gradient(loss_value, [model.W, model.B])

# Define:
# 1. A model.
# 2. Derivatives of a loss function with respect to model parameters.
# 3. A strategy for updating the variables based on the derivatives.
model = Model()
optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)

print("Initial loss: {:.3f}".format(loss(model, training_inputs, training_outputs)))

# Training loop
for i in range(300):
  grads = grad(model, training_inputs, training_outputs)
  optimizer.apply_gradients(zip(grads, [model.W, model.B]))
  if i % 20 == 0:
    print("Loss at step {:03d}: {:.3f}".format(i, loss(model, training_inputs, training_outputs)))

print("Final loss: {:.3f}".format(loss(model, training_inputs, training_outputs)))
print("W = {}, B = {}".format(model.W.numpy(), model.B.numpy()))
Initial loss: 69.001
Loss at step 000: 66.323
Loss at step 020: 30.255
Loss at step 040: 14.113
Loss at step 060: 6.889
Loss at step 080: 3.655
Loss at step 100: 2.208
Loss at step 120: 1.560
Loss at step 140: 1.269
Loss at step 160: 1.140
Loss at step 180: 1.081
Loss at step 200: 1.055
Loss at step 220: 1.044
Loss at step 240: 1.038
Loss at step 260: 1.036
Loss at step 280: 1.035
Final loss: 1.035
W = 2.971409559249878, B = 2.0077877044677734

Use objects for state during eager execution

With TF 1.x graph execution, program state (such as the variables) is stored in global collections and their lifetime is managed by the tf.Session object. In contrast, during eager execution the lifetime of state objects is determined by the lifetime of their corresponding Python object.

Variables are objects

During eager execution, variables persist until the last reference to the object is removed, and is then deleted.

if tf.test.is_gpu_available():
  with tf.device("gpu:0"):
    print("GPU enabled")
    v = tf.Variable(tf.random.normal([1000, 1000]))
    v = None  # v no longer takes up GPU memory
GPU enabled

Object-based saving

This section is an abbreviated version of the guide to training checkpoints.

tf.train.Checkpoint can save and restore tf.Variables to and from checkpoints:

x = tf.Variable(10.)
checkpoint = tf.train.Checkpoint(x=x)
x.assign(2.)   # Assign a new value to the variables and save.
checkpoint_path = './ckpt/''./ckpt/')
x.assign(11.)  # Change the variable after saving.

# Restore values from the checkpoint

print(x)  # => 2.0
<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=2.0>

To save and load models, tf.train.Checkpoint stores the internal state of objects, without requiring hidden variables. To record the state of a model, an optimizer, and a global step, pass them to a tf.train.Checkpoint:

import os

model = tf.keras.Sequential([
  tf.keras.layers.Conv2D(16,[3,3], activation='relu'),
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
checkpoint_dir = 'path/to/model_dir'
if not os.path.exists(checkpoint_dir):
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
root = tf.train.Checkpoint(optimizer=optimizer,
< at 0x7f951010e5c0>

Object-oriented metrics

tf.keras.metrics are stored as objects. Update a metric by passing the new data to the callable, and retrieve the result using the tf.keras.metrics.result method, for example:

m = tf.keras.metrics.Mean("loss")
m.result()  # => 2.5
m([8, 9])
m.result()  # => 5.5
<tf.Tensor: id=1008501, shape=(), dtype=float32, numpy=5.5>

Advanced automatic differentiation topics

Dynamic models

tf.GradientTape can also be used in dynamic models. This example for a backtracking line search algorithm looks like normal NumPy code, except there are gradients and is differentiable, despite the complex control flow:

def line_search_step(fn, init_x, rate=1.0):
  with tf.GradientTape() as tape:
    # Variables are automatically recorded, but manually watch a tensor
    value = fn(init_x)
  grad = tape.gradient(value, init_x)
  grad_norm = tf.reduce_sum(grad * grad)
  init_value = value
  while value > init_value - rate * grad_norm:
    x = init_x - rate * grad
    value = fn(x)
    rate /= 2.0
  return x, value

Custom gradients

Custom gradients are an easy way to override gradients. Within the forward function, define the gradient with respect to the inputs, outputs, or intermediate results. For example, here's an easy way to clip the norm of the gradients in the backward pass:

def clip_gradient_by_norm(x, norm):
  y = tf.identity(x)
  def grad_fn(dresult):
    return [tf.clip_by_norm(dresult, norm), None]
  return y, grad_fn

Custom gradients are commonly used to provide a numerically stable gradient for a sequence of operations:

def log1pexp(x):
  return tf.math.log(1 + tf.exp(x))

def grad_log1pexp(x):
  with tf.GradientTape() as tape:
    value = log1pexp(x)
  return tape.gradient(value, x)
# The gradient computation works fine at x = 0.
# However, x = 100 fails because of numerical instability.

Here, the log1pexp function can be analytically simplified with a custom gradient. The implementation below reuses the value for tf.exp(x) that is computed during the forward pass—making it more efficient by eliminating redundant calculations:

def log1pexp(x):
  e = tf.exp(x)
  def grad(dy):
    return dy * (1 - 1 / (1 + e))
  return tf.math.log(1 + e), grad

def grad_log1pexp(x):
  with tf.GradientTape() as tape:
    value = log1pexp(x)
  return tape.gradient(value, x)
# As before, the gradient computation works fine at x = 0.
# And the gradient computation also works at x = 100.


Computation is automatically offloaded to GPUs during eager execution. If you want control over where a computation runs you can enclose it in a tf.device('/gpu:0') block (or the CPU equivalent):

import time

def measure(x, steps):
  # TensorFlow initializes a GPU the first time it's used, exclude from timing.
  tf.matmul(x, x)
  start = time.time()
  for i in range(steps):
    x = tf.matmul(x, x)
  # tf.matmul can return before completing the matrix multiplication
  # (e.g., can return after enqueing the operation on a CUDA stream).
  # The x.numpy() call below will ensure that all enqueued operations
  # have completed (and will also copy the result to host memory,
  # so we're including a little more than just the matmul operation
  # time).
  _ = x.numpy()
  end = time.time()
  return end - start

shape = (1000, 1000)
steps = 200
print("Time to multiply a {} matrix by itself {} times:".format(shape, steps))

# Run on CPU:
with tf.device("/cpu:0"):
  print("CPU: {} secs".format(measure(tf.random.normal(shape), steps)))

# Run on GPU, if available:
if tf.test.is_gpu_available():
  with tf.device("/gpu:0"):
    print("GPU: {} secs".format(measure(tf.random.normal(shape), steps)))
  print("GPU: not found")
Time to multiply a (1000, 1000) matrix by itself 200 times:
CPU: 0.8578016757965088 secs
GPU: 0.040564775466918945 secs

A tf.Tensor object can be copied to a different device to execute its operations:

if tf.test.is_gpu_available():
  x = tf.random.normal([10, 10])

  x_gpu0 = x.gpu()
  x_cpu = x.cpu()

  _ = tf.matmul(x_cpu, x_cpu)    # Runs on CPU
  _ = tf.matmul(x_gpu0, x_gpu0)  # Runs on GPU:0


For compute-heavy models, such as ResNet50 training on a GPU, eager execution performance is comparable to tf.function execution. But this gap grows larger for models with less computation and there is work to be done for optimizing hot code paths for models with lots of small operations.

Work with functions

While eager execution makes development and debugging more interactive, TensorFlow 1.x style graph execution has advantages for distributed training, performance optimizations, and production deployment. To bridge this gap, TensorFlow 2.0 introduces functions via the tf.function API. For more information, see the Autograph guide.