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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

```
import os
import tensorflow as tf
import cProfile
```

In Tensorflow 2.0, eager execution is enabled by default.

```
tf.executing_eagerly()
```

True

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. The TensorFlow
`tf.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]])
print(a)
```

tf.Tensor( [[1 2] [3 4]], shape=(2, 2), dtype=int32)

```
# Broadcasting support
b = tf.add(a, 1)
print(b)
```

tf.Tensor( [[2 3] [4 5]], shape=(2, 2), dtype=int32)

```
# Operator overloading is supported
print(a * b)
```

tf.Tensor( [[ 2 6] [12 20]], shape=(2, 2), dtype=int32)

```
# Use NumPy values
import numpy as np
c = np.multiply(a, b)
print(c)
```

[[ 2 6] [12 20]]

```
# Obtain numpy value from a tensor:
print(a.numpy())
# => [[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:
print('FizzBuzz')
elif int(num % 3) == 0:
print('Fizz')
elif int(num % 5) == 0:
print('Buzz')
else:
print(num.numpy())
counter += 1
```

```
fizzbuzz(15)
```

1 2 Fizz 4 Buzz Fizz 7 8 Fizz Buzz 11 Fizz 13 14 FizzBuzz

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.data.Dataset.from_tensor_slices(
(tf.cast(mnist_images[...,tf.newaxis]/255, tf.float32),
tf.cast(mnist_labels,tf.int64)))
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'),
tf.keras.layers.GlobalAveragePooling2D(),
tf.keras.layers.Dense(10)
])
```

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: [[-0.01775933 -0.01194787 -0.08372174 -0.06535977 0.00338565 -0.01974326 -0.04763228 0.00904049 -0.00144051 -0.01944664]]

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)
loss_history.append(loss_value.numpy().mean())
grads = tape.gradient(loss_value, mnist_model.trainable_variables)
optimizer.apply_gradients(zip(grads, mnist_model.trainable_variables))
```

```
def train(epochs):
for epoch in range(epochs):
for (batch, (images, labels)) in enumerate(dataset):
train_step(images, labels)
print ('Epoch {} finished'.format(epoch))
```

```
train(epochs = 3)
```

Epoch 0 finished Epoch 1 finished Epoch 2 finished

```
import matplotlib.pyplot as plt
plt.plot(loss_history)
plt.xlabel('Batch #')
plt.ylabel('Loss [entropy]')
```

Text(0, 0.5, 'Loss [entropy]')

### Variables and optimizers

`tf.Variable`

objects store mutable `tf.Tensor`

-like values accessed during
training to make automatic differentiation easier.

The collections of variables can be encapsulated into layers or models, along with methods that operate on them. See Custom Keras layers and models for details. The main difference between layers and models is that models add methods like `Model.fit`

, `Model.evaluate`

, and `Model.save`

.

For example, the automatic differentiation example above can be rewritten:

```
class Linear(tf.keras.Model):
def __init__(self):
super(Linear, 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
NUM_EXAMPLES = 2000
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])
```

Next:

- Create the model.
- The Derivatives of a loss function with respect to model parameters.
- A strategy for updating the variables based on the derivatives.

```
model = Linear()
optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)
print("Initial loss: {:.3f}".format(loss(model, training_inputs, training_outputs)))
steps = 300
for i in range(steps):
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)))
```

Initial loss: 69.909 Loss at step 000: 67.145 Loss at step 020: 30.170 Loss at step 040: 13.859 Loss at step 060: 6.659 Loss at step 080: 3.479 Loss at step 100: 2.074 Loss at step 120: 1.453 Loss at step 140: 1.178 Loss at step 160: 1.056 Loss at step 180: 1.003 Loss at step 200: 0.979 Loss at step 220: 0.968 Loss at step 240: 0.963 Loss at step 260: 0.961 Loss at step 280: 0.960

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

Final loss: 0.960

```
print("W = {}, B = {}".format(model.W.numpy(), model.B.numpy()))
```

W = 2.9515867233276367, B = 2.0210201740264893

### Object-based saving

A `tf.keras.Model`

includes a convenient `save_weights`

method allowing you to easily create a checkpoint:

```
model.save_weights('weights')
status = model.load_weights('weights')
```

Using `tf.train.Checkpoint`

you can take full control over this process.

This section is an abbreviated version of the guide to training 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/'
checkpoint.save(checkpoint_path)
```

'./ckpt/-1'

```
x.assign(11.) # Change the variable after saving.
# Restore values from the checkpoint
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_path))
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`

:

```
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(16,[3,3], activation='relu'),
tf.keras.layers.GlobalAveragePooling2D(),
tf.keras.layers.Dense(10)
])
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
checkpoint_dir = 'path/to/model_dir'
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
root = tf.train.Checkpoint(optimizer=optimizer,
model=model)
root.save(checkpoint_prefix)
root.restore(tf.train.latest_checkpoint(checkpoint_dir))
```

<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f4ba0648310>

### 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(0)
m(5)
m.result() # => 2.5
m([8, 9])
m.result() # => 5.5
```

<tf.Tensor: shape=(), dtype=float32, numpy=5.5>

### Summaries and TensorBoard

TensorBoard is a visualization tool for understanding, debugging and optimizing the model training process. It uses summary events that are written while executing the program.

You can use `tf.summary`

to record summaries of variable in eager execution.
For example, to record summaries of `loss`

once every 100 training steps:

```
logdir = "./tb/"
writer = tf.summary.create_file_writer(logdir)
steps = 1000
with writer.as_default(): # or call writer.set_as_default() before the loop.
for i in range(steps):
step = i + 1
# Calculate loss with your real train function.
loss = 1 - 0.001 * step
if step % 100 == 0:
tf.summary.scalar('loss', loss, step=step)
```

`ls tb/`

events.out.tfevents.1632342765.kokoro-gcp-ubuntu-prod-230753280.22287.0.v2

## 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 tracked.
# But to calculate a gradient from a tensor, you must `watch` it.
tape.watch(init_x)
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:

```
@tf.custom_gradient
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:
tape.watch(x)
value = log1pexp(x)
return tape.gradient(value, x)
```

```
# The gradient computation works fine at x = 0.
grad_log1pexp(tf.constant(0.)).numpy()
```

0.5

```
# However, x = 100 fails because of numerical instability.
grad_log1pexp(tf.constant(100.)).numpy()
```

nan

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:

```
@tf.custom_gradient
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:
tape.watch(x)
value = log1pexp(x)
return tape.gradient(value, x)
```

```
# As before, the gradient computation works fine at x = 0.
grad_log1pexp(tf.constant(0.)).numpy()
```

0.5

```
# And the gradient computation also works at x = 100.
grad_log1pexp(tf.constant(100.)).numpy()
```

1.0

## Performance

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.config.list_physical_devices("GPU"):
with tf.device("/gpu:0"):
print("GPU: {} secs".format(measure(tf.random.normal(shape), steps)))
else:
print("GPU: not found")
```

Time to multiply a (1000, 1000) matrix by itself 200 times: CPU: 1.007401466369629 secs GPU: 0.04124784469604492 secs

A `tf.Tensor`

object can be copied to a different device to execute its
operations:

```
if tf.config.list_physical_devices("GPU"):
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
```

WARNING:tensorflow:From /tmp/ipykernel_22287/406964202.py:4: _EagerTensorBase.gpu (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.identity instead. WARNING:tensorflow:From /tmp/ipykernel_22287/406964202.py:5: _EagerTensorBase.cpu (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.identity instead.

### Benchmarks

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 `function`

s via the `tf.function`

API. For more information, see the tf.function guide.