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Getting started /
Introduction to Keras for Researchers

**Author:** fchollet

**Date created:** 2020/04/01

**Last modified:** 2020/04/28

**Description:** Everything you need to know to use Keras & TF 2.0 for deep learning research.

```
import tensorflow as tf
from tensorflow import keras
```

Are you a machine learning researcher? Do you publish at NeurIPS and push the state-of-the-art in CV and NLP? This guide will serve as your first introduction to core Keras API concepts.

In this guide, you will learn about:

- Creating layers by subclassing the
`Layer`

class - Computing gradients with a
`GradientTape`

and writing low-level training loops - Tracking losses created by layers via the
`add_loss()`

method - Tracking metrics in a low-level training loop
- Speeding up execution with a compiled
`tf.function`

- Executing layers in training or inference mode
- The Keras Functional API

You will also see the Keras API in action in two end-to-end research examples: a Variational Autoencoder, and a Hypernetwork.

`Layer`

classThe `Layer`

is the fundamental abstraction in Keras.
A `Layer`

encapsulates a state (weights) and some computation
(defined in the call method).

A simple layer looks like this:

```
class Linear(keras.layers.Layer):
"""y = w.x + b"""
def __init__(self, units=32, input_dim=32):
super(Linear, self).__init__()
w_init = tf.random_normal_initializer()
self.w = tf.Variable(
initial_value=w_init(shape=(input_dim, units), dtype="float32"),
trainable=True,
)
b_init = tf.zeros_initializer()
self.b = tf.Variable(
initial_value=b_init(shape=(units,), dtype="float32"), trainable=True
)
def call(self, inputs):
return tf.matmul(inputs, self.w) + self.b
```

You would use a `Layer`

instance much like a Python function:

```
# Instantiate our layer.
linear_layer = Linear(units=4, input_dim=2)
# The layer can be treated as a function.
# Here we call it on some data.
y = linear_layer(tf.ones((2, 2)))
assert y.shape == (2, 4)
```

The weight variables (created in `__init__`

) are automatically
tracked under the `weights`

property:

```
assert linear_layer.weights == [linear_layer.w, linear_layer.b]
```

You have many built-in layers available, from `Dense`

to `Conv2D`

to `LSTM`

to
fancier ones like `Conv3DTranspose`

or `ConvLSTM2D`

. Be smart about reusing
built-in functionality.

The add_weight method gives you a shortcut for creating weights:

```
class Linear(keras.layers.Layer):
"""y = w.x + b"""
def __init__(self, units=32):
super(Linear, self).__init__()
self.units = units
def build(self, input_shape):
self.w = self.add_weight(
shape=(input_shape[-1], self.units),
initializer="random_normal",
trainable=True,
)
self.b = self.add_weight(
shape=(self.units,), initializer="random_normal", trainable=True
)
def call(self, inputs):
return tf.matmul(inputs, self.w) + self.b
# Instantiate our lazy layer.
linear_layer = Linear(4)
# This will also call `build(input_shape)` and create the weights.
y = linear_layer(tf.ones((2, 2)))
```

You can automatically retrieve the gradients of the weights of a layer by
calling it inside a `GradientTape`

. Using these gradients, you can update the
weights of the layer, either manually, or using an optimizer object. Of course,
you can modify the gradients before using them, if you need to.

```
# Prepare a dataset.
(x_train, y_train), _ = tf.keras.datasets.mnist.load_data()
dataset = tf.data.Dataset.from_tensor_slices(
(x_train.reshape(60000, 784).astype("float32") / 255, y_train)
)
dataset = dataset.shuffle(buffer_size=1024).batch(64)
# Instantiate our linear layer (defined above) with 10 units.
linear_layer = Linear(10)
# Instantiate a logistic loss function that expects integer targets.
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
# Instantiate an optimizer.
optimizer = tf.keras.optimizers.SGD(learning_rate=1e-3)
# Iterate over the batches of the dataset.
for step, (x, y) in enumerate(dataset):
# Open a GradientTape.
with tf.GradientTape() as tape:
# Forward pass.
logits = linear_layer(x)
# Loss value for this batch.
loss = loss_fn(y, logits)
# Get gradients of weights wrt the loss.
gradients = tape.gradient(loss, linear_layer.trainable_weights)
# Update the weights of our linear layer.
optimizer.apply_gradients(zip(gradients, linear_layer.trainable_weights))
# Logging.
if step % 100 == 0:
print("Step:", step, "Loss:", float(loss))
```

```
Step: 0 Loss: 2.456793785095215
Step: 100 Loss: 2.2678098678588867
Step: 200 Loss: 2.196652412414551
Step: 300 Loss: 2.132258892059326
Step: 400 Loss: 2.0797274112701416
Step: 500 Loss: 1.9761338233947754
Step: 600 Loss: 1.7839593887329102
Step: 700 Loss: 1.8158284425735474
Step: 800 Loss: 1.7084990739822388
Step: 900 Loss: 1.6562185287475586
```

Weights created by layers can be either trainable or non-trainable. They're
exposed in `trainable_weights`

and `non_trainable_weights`

respectively.
Here's a layer with a non-trainable weight:

```
class ComputeSum(keras.layers.Layer):
"""Returns the sum of the inputs."""
def __init__(self, input_dim):
super(ComputeSum, self).__init__()
# Create a non-trainable weight.
self.total = tf.Variable(initial_value=tf.zeros((input_dim,)), trainable=False)
def call(self, inputs):
self.total.assign_add(tf.reduce_sum(inputs, axis=0))
return self.total
my_sum = ComputeSum(2)
x = tf.ones((2, 2))
y = my_sum(x)
print(y.numpy()) # [2. 2.]
y = my_sum(x)
print(y.numpy()) # [4. 4.]
assert my_sum.weights == [my_sum.total]
assert my_sum.non_trainable_weights == [my_sum.total]
assert my_sum.trainable_weights == []
```

```
[2. 2.]
[4. 4.]
```

Layers can be recursively nested to create bigger computation blocks. Each layer will track the weights of its sublayers (both trainable and non-trainable).

```
# Let's reuse the Linear class
# with a `build` method that we defined above.
class MLP(keras.layers.Layer):
"""Simple stack of Linear layers."""
def __init__(self):
super(MLP, self).__init__()
self.linear_1 = Linear(32)
self.linear_2 = Linear(32)
self.linear_3 = Linear(10)
def call(self, inputs):
x = self.linear_1(inputs)
x = tf.nn.relu(x)
x = self.linear_2(x)
x = tf.nn.relu(x)
return self.linear_3(x)
mlp = MLP()
# The first call to the `mlp` object will create the weights.
y = mlp(tf.ones(shape=(3, 64)))
# Weights are recursively tracked.
assert len(mlp.weights) == 6
```

Note that our manually-created MLP above is equivalent to the following built-in option:

```
mlp = keras.Sequential(
[
keras.layers.Dense(32, activation=tf.nn.relu),
keras.layers.Dense(32, activation=tf.nn.relu),
keras.layers.Dense(10),
]
)
```

Layers can create losses during the forward pass via the `add_loss()`

method.
This is especially useful for regularization losses.
The losses created by sublayers are recursively tracked by the parent layers.

Here's a layer that creates an activity regularization loss:

```
class ActivityRegularization(keras.layers.Layer):
"""Layer that creates an activity sparsity regularization loss."""
def __init__(self, rate=1e-2):
super(ActivityRegularization, self).__init__()
self.rate = rate
def call(self, inputs):
# We use `add_loss` to create a regularization loss
# that depends on the inputs.
self.add_loss(self.rate * tf.reduce_sum(inputs))
return inputs
```

Any model incorporating this layer will track this regularization loss:

```
# Let's use the loss layer in a MLP block.
class SparseMLP(keras.layers.Layer):
"""Stack of Linear layers with a sparsity regularization loss."""
def __init__(self):
super(SparseMLP, self).__init__()
self.linear_1 = Linear(32)
self.regularization = ActivityRegularization(1e-2)
self.linear_3 = Linear(10)
def call(self, inputs):
x = self.linear_1(inputs)
x = tf.nn.relu(x)
x = self.regularization(x)
return self.linear_3(x)
mlp = SparseMLP()
y = mlp(tf.ones((10, 10)))
print(mlp.losses) # List containing one float32 scalar
```

```
[<tf.Tensor: shape=(), dtype=float32, numpy=0.23114467>]
```

These losses are cleared by the top-level layer at the start of each forward
pass -- they don't accumulate. `layer.losses`

always contains only the losses
created during the last forward pass. You would typically use these losses by
summing them before computing your gradients when writing a training loop.

```
# Losses correspond to the *last* forward pass.
mlp = SparseMLP()
mlp(tf.ones((10, 10)))
assert len(mlp.losses) == 1
mlp(tf.ones((10, 10)))
assert len(mlp.losses) == 1 # No accumulation.
# Let's demonstrate how to use these losses in a training loop.
# Prepare a dataset.
(x_train, y_train), _ = tf.keras.datasets.mnist.load_data()
dataset = tf.data.Dataset.from_tensor_slices(
(x_train.reshape(60000, 784).astype("float32") / 255, y_train)
)
dataset = dataset.shuffle(buffer_size=1024).batch(64)
# A new MLP.
mlp = SparseMLP()
# Loss and optimizer.
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.SGD(learning_rate=1e-3)
for step, (x, y) in enumerate(dataset):
with tf.GradientTape() as tape:
# Forward pass.
logits = mlp(x)
# External loss value for this batch.
loss = loss_fn(y, logits)
# Add the losses created during the forward pass.
loss += sum(mlp.losses)
# Get gradients of weights wrt the loss.
gradients = tape.gradient(loss, mlp.trainable_weights)
# Update the weights of our linear layer.
optimizer.apply_gradients(zip(gradients, mlp.trainable_weights))
# Logging.
if step % 100 == 0:
print("Step:", step, "Loss:", float(loss))
```

```
Step: 0 Loss: 5.991635799407959
Step: 100 Loss: 2.6379199028015137
Step: 200 Loss: 2.39302921295166
Step: 300 Loss: 2.3888492584228516
Step: 400 Loss: 2.356649160385132
Step: 500 Loss: 2.3454649448394775
Step: 600 Loss: 2.327338695526123
Step: 700 Loss: 2.3245863914489746
Step: 800 Loss: 2.3086981773376465
Step: 900 Loss: 2.3108632564544678
```

Keras offers a broad range of built-in metrics, like `tf.keras.metrics.AUC`

or `tf.keras.metrics.PrecisionAtRecall`

. It's also easy to create your
own metrics in a few lines of code.

To use a metric in a custom training loop, you would:

- Instantiate the metric object, e.g.
`metric = tf.keras.metrics.AUC()`

- Call its
`metric.udpate_state(targets, predictions)`

method for each batch of data - Query its result via
`metric.result()`

- Reset the metric's state at the end of an epoch or at the start of an evaluation via
`metric.reset_states()`

Here's a simple example:

```
# Instantiate a metric object
accuracy = tf.keras.metrics.SparseCategoricalAccuracy()
# Prepare our layer, loss, and optimizer.
model = keras.Sequential(
[
keras.layers.Dense(32, activation="relu"),
keras.layers.Dense(32, activation="relu"),
keras.layers.Dense(10),
]
)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)
for epoch in range(2):
# Iterate over the batches of a dataset.
for step, (x, y) in enumerate(dataset):
with tf.GradientTape() as tape:
logits = model(x)
# Compute the loss value for this batch.
loss_value = loss_fn(y, logits)
# Update the state of the `accuracy` metric.
accuracy.update_state(y, logits)
# Update the weights of the model to minimize the loss value.
gradients = tape.gradient(loss_value, model.trainable_weights)
optimizer.apply_gradients(zip(gradients, model.trainable_weights))
# Logging the current accuracy value so far.
if step % 200 == 0:
print("Epoch:", epoch, "Step:", step)
print("Total running accuracy so far: %.3f" % accuracy.result())
# Reset the metric's state at the end of an epoch
accuracy.reset_states()
```

```
Epoch: 0 Step: 0
Total running accuracy so far: 0.047
Epoch: 0 Step: 200
Total running accuracy so far: 0.760
Epoch: 0 Step: 400
Total running accuracy so far: 0.831
Epoch: 0 Step: 600
Total running accuracy so far: 0.859
Epoch: 0 Step: 800
Total running accuracy so far: 0.875
Epoch: 1 Step: 0
Total running accuracy so far: 0.938
Epoch: 1 Step: 200
Total running accuracy so far: 0.938
Epoch: 1 Step: 400
Total running accuracy so far: 0.939
Epoch: 1 Step: 600
Total running accuracy so far: 0.941
Epoch: 1 Step: 800
Total running accuracy so far: 0.941
```

In addition to this, similarly to the `self.add_loss()`

method, you have access
to an `self.add_metric()`

method on layers. It tracks the average of
whatever quantity you pass to it. You can reset the value of these metrics
by calling `layer.reset_metrics()`

on any layer or model.

Running eagerly is great for debugging, but you will get better performance by
compiling your computation into static graphs. Static graphs are a researcher's
best friends. You can compile any function by wrapping it in a `tf.function`

decorator.

```
# Prepare our layer, loss, and optimizer.
model = keras.Sequential(
[
keras.layers.Dense(32, activation="relu"),
keras.layers.Dense(32, activation="relu"),
keras.layers.Dense(10),
]
)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)
# Create a training step function.
@tf.function # Make it fast.
def train_on_batch(x, y):
with tf.GradientTape() as tape:
logits = model(x)
loss = loss_fn(y, logits)
gradients = tape.gradient(loss, model.trainable_weights)
optimizer.apply_gradients(zip(gradients, model.trainable_weights))
return loss
# Prepare a dataset.
(x_train, y_train), _ = tf.keras.datasets.mnist.load_data()
dataset = tf.data.Dataset.from_tensor_slices(
(x_train.reshape(60000, 784).astype("float32") / 255, y_train)
)
dataset = dataset.shuffle(buffer_size=1024).batch(64)
for step, (x, y) in enumerate(dataset):
loss = train_on_batch(x, y)
if step % 100 == 0:
print("Step:", step, "Loss:", float(loss))
```

```
Step: 0 Loss: 2.4244790077209473
Step: 100 Loss: 0.6268073320388794
Step: 200 Loss: 0.5352152585983276
Step: 300 Loss: 0.18634426593780518
Step: 400 Loss: 0.2614487111568451
Step: 500 Loss: 0.5878333449363708
Step: 600 Loss: 0.392818808555603
Step: 700 Loss: 0.24846115708351135
Step: 800 Loss: 0.1844426691532135
Step: 900 Loss: 0.1664549559354782
```

Some layers, in particular the `BatchNormalization`

layer and the `Dropout`

layer, have different behaviors during training and inference. For such layers,
it is standard practice to expose a `training`

(boolean) argument in the `call`

method.

By exposing this argument in `call`

, you enable the built-in training and
evaluation loops (e.g. fit) to correctly use the layer in training and
inference modes.

```
class Dropout(keras.layers.Layer):
def __init__(self, rate):
super(Dropout, self).__init__()
self.rate = rate
def call(self, inputs, training=None):
if training:
return tf.nn.dropout(inputs, rate=self.rate)
return inputs
class MLPWithDropout(keras.layers.Layer):
def __init__(self):
super(MLPWithDropout, self).__init__()
self.linear_1 = Linear(32)
self.dropout = Dropout(0.5)
self.linear_3 = Linear(10)
def call(self, inputs, training=None):
x = self.linear_1(inputs)
x = tf.nn.relu(x)
x = self.dropout(x, training=training)
return self.linear_3(x)
mlp = MLPWithDropout()
y_train = mlp(tf.ones((2, 2)), training=True)
y_test = mlp(tf.ones((2, 2)), training=False)
```

To build deep learning models, you don't have to use object-oriented programming all the time. All layers we've seen so far can also be composed functionally, like this (we call it the "Functional API"):

```
# We use an `Input` object to describe the shape and dtype of the inputs.
# This is the deep learning equivalent of *declaring a type*.
# The shape argument is per-sample; it does not include the batch size.
# The functional API focused on defining per-sample transformations.
# The model we create will automatically batch the per-sample transformations,
# so that it can be called on batches of data.
inputs = tf.keras.Input(shape=(16,), dtype="float32")
# We call layers on these "type" objects
# and they return updated types (new shapes/dtypes).
x = Linear(32)(inputs) # We are reusing the Linear layer we defined earlier.
x = Dropout(0.5)(x) # We are reusing the Dropout layer we defined earlier.
outputs = Linear(10)(x)
# A functional `Model` can be defined by specifying inputs and outputs.
# A model is itself a layer like any other.
model = tf.keras.Model(inputs, outputs)
# A functional model already has weights, before being called on any data.
# That's because we defined its input shape in advance (in `Input`).
assert len(model.weights) == 4
# Let's call our model on some data, for fun.
y = model(tf.ones((2, 16)))
assert y.shape == (2, 10)
# You can pass a `training` argument in `__call__`
# (it will get passed down to the Dropout layer).
y = model(tf.ones((2, 16)), training=True)
```

The Functional API tends to be more concise than subclassing, and provides a few other advantages (generally the same advantages that functional, typed languages provide over untyped OO development). However, it can only be used to define DAGs of layers -- recursive networks should be defined as Layer subclasses instead.

Learn more about the Functional API here.

In your research workflows, you may often find yourself mix-and-matching OO models and Functional models.

Note that the `Model`

class also features built-in training & evaluation loops
(`fit()`

and `evaluate()`

). You can always subclass the `Model`

class
(it works exactly like subclassing `Layer`

) if you want to leverage these loops
for your OO models.

Here are some of things you've learned so far:

- A
`Layer`

encapsulate a state (created in`__init__`

or`build`

) and some computation (defined in`call`

). - Layers can be recursively nested to create new, bigger computation blocks.
- You can easily write highly hackable training loops by opening a
`GradientTape`

, calling your model inside the tape's scope, then retrieving gradients and applying them via an optimizer. - You can speed up your training loops using the
`@tf.function`

decorator. - Layers can create and track losses (typically regularization losses) via
`self.add_loss()`

.

Let's put all of these things together into an end-to-end example: we're going to implement a Variational AutoEncoder (VAE). We'll train it on MNIST digits.

Our VAE will be a subclass of `Layer`

, built as a nested composition of layers that
subclass `Layer`

. It will feature a regularization loss (KL divergence).

Below is our model definition.

First, we have an `Encoder`

class, which uses a `Sampling`

layer to map a MNIST digit to
a latent-space triplet `(z_mean, z_log_var, z)`

.

```
from tensorflow.keras import layers
class Sampling(layers.Layer):
"""Uses (z_mean, z_log_var) to sample z, the vector encoding a digit."""
def call(self, inputs):
z_mean, z_log_var = inputs
batch = tf.shape(z_mean)[0]
dim = tf.shape(z_mean)[1]
epsilon = tf.keras.backend.random_normal(shape=(batch, dim))
return z_mean + tf.exp(0.5 * z_log_var) * epsilon
class Encoder(layers.Layer):
"""Maps MNIST digits to a triplet (z_mean, z_log_var, z)."""
def __init__(self, latent_dim=32, intermediate_dim=64, **kwargs):
super(Encoder, self).__init__(**kwargs)
self.dense_proj = layers.Dense(intermediate_dim, activation=tf.nn.relu)
self.dense_mean = layers.Dense(latent_dim)
self.dense_log_var = layers.Dense(latent_dim)
self.sampling = Sampling()
def call(self, inputs):
x = self.dense_proj(inputs)
z_mean = self.dense_mean(x)
z_log_var = self.dense_log_var(x)
z = self.sampling((z_mean, z_log_var))
return z_mean, z_log_var, z
```

Next, we have a `Decoder`

class, which maps the probabilistic latent space coordinates
back to a MNIST digit.

```
class Decoder(layers.Layer):
"""Converts z, the encoded digit vector, back into a readable digit."""
def __init__(self, original_dim, intermediate_dim=64, **kwargs):
super(Decoder, self).__init__(**kwargs)
self.dense_proj = layers.Dense(intermediate_dim, activation=tf.nn.relu)
self.dense_output = layers.Dense(original_dim, activation=tf.nn.sigmoid)
def call(self, inputs):
x = self.dense_proj(inputs)
return self.dense_output(x)
```

Finally, our `VariationalAutoEncoder`

composes together an encoder and a decoder, and
creates a KL divergence regularization loss via `add_loss()`

.

```
class VariationalAutoEncoder(layers.Layer):
"""Combines the encoder and decoder into an end-to-end model for training."""
def __init__(self, original_dim, intermediate_dim=64, latent_dim=32, **kwargs):
super(VariationalAutoEncoder, self).__init__(**kwargs)
self.original_dim = original_dim
self.encoder = Encoder(latent_dim=latent_dim, intermediate_dim=intermediate_dim)
self.decoder = Decoder(original_dim, intermediate_dim=intermediate_dim)
def call(self, inputs):
z_mean, z_log_var, z = self.encoder(inputs)
reconstructed = self.decoder(z)
# Add KL divergence regularization loss.
kl_loss = -0.5 * tf.reduce_mean(
z_log_var - tf.square(z_mean) - tf.exp(z_log_var) + 1
)
self.add_loss(kl_loss)
return reconstructed
```

Now, let's write a training loop. Our training step is decorated with a `@tf.function`

to
compile into a super fast graph function.

```
# Our model.
vae = VariationalAutoEncoder(original_dim=784, intermediate_dim=64, latent_dim=32)
# Loss and optimizer.
loss_fn = tf.keras.losses.MeanSquaredError()
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)
# Prepare a dataset.
(x_train, _), _ = tf.keras.datasets.mnist.load_data()
dataset = tf.data.Dataset.from_tensor_slices(
x_train.reshape(60000, 784).astype("float32") / 255
)
dataset = dataset.shuffle(buffer_size=1024).batch(32)
@tf.function
def training_step(x):
with tf.GradientTape() as tape:
reconstructed = vae(x) # Compute input reconstruction.
# Compute loss.
loss = loss_fn(x, reconstructed)
loss += sum(vae.losses) # Add KLD term.
# Update the weights of the VAE.
grads = tape.gradient(loss, vae.trainable_weights)
optimizer.apply_gradients(zip(grads, vae.trainable_weights))
return loss
losses = [] # Keep track of the losses over time.
for step, x in enumerate(dataset):
loss = training_step(x)
# Logging.
losses.append(float(loss))
if step % 100 == 0:
print("Step:", step, "Loss:", sum(losses) / len(losses))
# Stop after 1000 steps.
# Training the model to convergence is left
# as an exercise to the reader.
if step >= 1000:
break
```

```
Step: 0 Loss: 0.34360969066619873
Step: 100 Loss: 0.1268921259901311
Step: 200 Loss: 0.10019794225099668
Step: 300 Loss: 0.08994597142122909
Step: 400 Loss: 0.08490597068371618
Step: 500 Loss: 0.08168080423822897
Step: 600 Loss: 0.07927437018162994
Step: 700 Loss: 0.07791767667941803
Step: 800 Loss: 0.07669395401832019
Step: 900 Loss: 0.07571622215781969
Step: 1000 Loss: 0.07479669768121336
```

As you can see, building and training this type of model in Keras is quick and painless.

Now, you may find that the code above is somewhat verbose: we handle every little detail on our own, by hand. This gives the most flexibility, but it's also a bit of work.

Let's take a look at what the Functional API version of our VAE looks like:

```
original_dim = 784
intermediate_dim = 64
latent_dim = 32
# Define encoder model.
original_inputs = tf.keras.Input(shape=(original_dim,), name="encoder_input")
x = layers.Dense(intermediate_dim, activation="relu")(original_inputs)
z_mean = layers.Dense(latent_dim, name="z_mean")(x)
z_log_var = layers.Dense(latent_dim, name="z_log_var")(x)
z = Sampling()((z_mean, z_log_var))
encoder = tf.keras.Model(inputs=original_inputs, outputs=z, name="encoder")
# Define decoder model.
latent_inputs = tf.keras.Input(shape=(latent_dim,), name="z_sampling")
x = layers.Dense(intermediate_dim, activation="relu")(latent_inputs)
outputs = layers.Dense(original_dim, activation="sigmoid")(x)
decoder = tf.keras.Model(inputs=latent_inputs, outputs=outputs, name="decoder")
# Define VAE model.
outputs = decoder(z)
vae = tf.keras.Model(inputs=original_inputs, outputs=outputs, name="vae")
# Add KL divergence regularization loss.
kl_loss = -0.5 * tf.reduce_mean(z_log_var - tf.square(z_mean) - tf.exp(z_log_var) + 1)
vae.add_loss(kl_loss)
```

Much more concise, right?

By the way, Keras also features built-in training & evaluation loops on its `Model`

class
(`fit()`

and `evaluate()`

). Check it out:

```
# Loss and optimizer.
loss_fn = tf.keras.losses.MeanSquaredError()
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)
# Prepare a dataset.
(x_train, _), _ = tf.keras.datasets.mnist.load_data()
dataset = tf.data.Dataset.from_tensor_slices(
x_train.reshape(60000, 784).astype("float32") / 255
)
dataset = dataset.map(lambda x: (x, x)) # Use x_train as both inputs & targets
dataset = dataset.shuffle(buffer_size=1024).batch(32)
# Configure the model for training.
vae.compile(optimizer, loss=loss_fn)
# Actually training the model.
vae.fit(dataset, epochs=1)
```

```
1875/1875 [==============================] - 2s 980us/step - loss: 0.0713
<tensorflow.python.keras.callbacks.History at 0x167c2cd50>
```

The use of the Functional API and `fit`

reduces our example from 65 lines to 25 lines
(including model definition & training). The Keras philosophy is to offer you
productivity-boosting features like
these, while simultaneously empowering you to write everything yourself to gain absolute
control over every little detail. Like we did in the low-level training loop two
paragraphs earlier.

Let's take a look at another kind of research experiment: hypernetworks.

A hypernetwork is a deep neural network whose weights are generated by another network (usually smaller).

Let's implement a really trivial hypernetwork: we'll use a small 2-layer network to generate the weights of a larger 3-layer network.

```
import numpy as np
input_dim = 784
classes = 10
# This is the model we'll actually use to predict labels (the hypernetwork).
outer_model = keras.Sequential(
[keras.layers.Dense(64, activation=tf.nn.relu), keras.layers.Dense(classes),]
)
# It doesn't need to create its own weights, so let's mark its layers
# as already built. That way, calling `outer_model` won't create new variables.
for layer in outer_model.layers:
layer.built = True
# This is the number of weight coefficients to generate. Each layer in the
# hypernetwork requires output_dim * input_dim + output_dim coefficients.
num_weights_to_generate = (classes * 64 + classes) + (64 * input_dim + 64)
# This is the model that generates the weights of the `outer_model` above.
inner_model = keras.Sequential(
[
keras.layers.Dense(16, activation=tf.nn.relu),
keras.layers.Dense(num_weights_to_generate, activation=tf.nn.sigmoid),
]
)
```

This is our training loop. For each batch of data:

- We use
`inner_model`

to generate an array of weight coefficients,`weights_pred`

- We reshape these coefficients into kernel & bias tensors for the
`outer_model`

- We run the forward pass of the
`outer_model`

to compute the actual MNIST predictions - We run backprop through the weights of the
`inner_model`

to minimize the final classification loss

```
# Loss and optimizer.
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4)
# Prepare a dataset.
(x_train, y_train), _ = tf.keras.datasets.mnist.load_data()
dataset = tf.data.Dataset.from_tensor_slices(
(x_train.reshape(60000, 784).astype("float32") / 255, y_train)
)
# We'll use a batch size of 1 for this experiment.
dataset = dataset.shuffle(buffer_size=1024).batch(1)
@tf.function
def train_step(x, y):
with tf.GradientTape() as tape:
# Predict weights for the outer model.
weights_pred = inner_model(x)
# Reshape them to the expected shapes for w and b for the outer model.
# Layer 0 kernel.
start_index = 0
w0_shape = (input_dim, 64)
w0_coeffs = weights_pred[:, start_index : start_index + np.prod(w0_shape)]
w0 = tf.reshape(w0_coeffs, w0_shape)
start_index += np.prod(w0_shape)
# Layer 0 bias.
b0_shape = (64,)
b0_coeffs = weights_pred[:, start_index : start_index + np.prod(b0_shape)]
b0 = tf.reshape(b0_coeffs, b0_shape)
start_index += np.prod(b0_shape)
# Layer 1 kernel.
w1_shape = (64, classes)
w1_coeffs = weights_pred[:, start_index : start_index + np.prod(w1_shape)]
w1 = tf.reshape(w1_coeffs, w1_shape)
start_index += np.prod(w1_shape)
# Layer 1 bias.
b1_shape = (classes,)
b1_coeffs = weights_pred[:, start_index : start_index + np.prod(b1_shape)]
b1 = tf.reshape(b1_coeffs, b1_shape)
start_index += np.prod(b1_shape)
# Set the weight predictions as the weight variables on the outer model.
outer_model.layers[0].kernel = w0
outer_model.layers[0].bias = b0
outer_model.layers[1].kernel = w1
outer_model.layers[1].bias = b1
# Inference on the outer model.
preds = outer_model(x)
loss = loss_fn(y, preds)
# Train only inner model.
grads = tape.gradient(loss, inner_model.trainable_weights)
optimizer.apply_gradients(zip(grads, inner_model.trainable_weights))
return loss
losses = [] # Keep track of the losses over time.
for step, (x, y) in enumerate(dataset):
loss = train_step(x, y)
# Logging.
losses.append(float(loss))
if step % 100 == 0:
print("Step:", step, "Loss:", sum(losses) / len(losses))
# Stop after 1000 steps.
# Training the model to convergence is left
# as an exercise to the reader.
if step >= 1000:
break
```

```
Step: 0 Loss: 4.737176418304443
Step: 100 Loss: 2.6431594647393366
Step: 200 Loss: 2.3565089883495918
Step: 300 Loss: 2.1680791037268565
Step: 400 Loss: 2.044000819636662
Step: 500 Loss: 2.01625633314475
Step: 600 Loss: 1.9379815768527302
Step: 700 Loss: 1.855055329773368
Step: 800 Loss: 1.7896285848904616
Step: 900 Loss: 1.69601594678629
Step: 1000 Loss: 1.6704652742821007
```

Implementing arbitrary research ideas with Keras is straightforward and highly productive. Imagine trying out 25 ideas per day (20 minutes per experiment on average)!

Keras has been designed to go from idea to results as fast as possible, because we believe this is the key to doing great research.

We hope you enjoyed this quick introduction. Let us know what you build with Keras!

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