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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
!pip install -q tensorflow-gpu==2.0.0-beta1
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. 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]])
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)
```

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz 11493376/11490434 [==============================] - 0s 0us/step

```
# 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: [[ 0.0179956 0.00757212 0.00764954 0.02016721 -0.00743234 -0.01830849 0.0138132 -0.01235204 -0.01126359 0.01111226]]

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():
for epoch in range(3):
for (batch, (images, labels)) in enumerate(dataset):
train_step(images, labels)
print ('Epoch {} finished'.format(epoch))
```

```
train()
```

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`

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
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])
# 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: 70.202 Loss at step 000: 67.441 Loss at step 020: 30.431 Loss at step 040: 14.040 Loss at step 060: 6.780 Loss at step 080: 3.563 Loss at step 100: 2.137 Loss at step 120: 1.505 Loss at step 140: 1.225 Loss at step 160: 1.101 Loss at step 180: 1.045 Loss at step 200: 1.021 Loss at step 220: 1.010 Loss at step 240: 1.005 Loss at step 260: 1.003 Loss at step 280: 1.002 Final loss: 1.002 W = 2.9762353897094727, B = 1.9818612337112427

## 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.Variable`

s 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/'
checkpoint.save('./ckpt/')
```

'./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`

:

```
import os
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 0x7f1b6c28d550>

### 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: 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
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.test.is_gpu_available():
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: 0.7861723899841309 secs GPU: 0.03987884521484375 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
```

### 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 Autograph guide.