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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Introduction to Keras for Researchers
Author
Step1:  Introduction
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 & TensorFlow API concepts.
In this guide, you will learn about
Step2:  You can get its value as a NumPy array by calling .numpy()
Step3:  Much like a NumPy array, it features the attributes dtype and shape
Step4:  A common way to create constant tensors is via tf.ones and tf.zeros (just like np.ones and np.zeros)
Step5:  You can also create random constant tensors
Step6:  Variables
Variables are special tensors used to store mutable state (such as the weights of a neural network).
You create a Variable using some initial value
Step7:  You update the value of a Variable by using the methods .assign(value), .assign_add(increment), or .assign_sub(decrement)
Step8:  Doing math in TensorFlow
If you've used NumPy, doing math in TensorFlow will look very familiar.
The main difference is that your TensorFlow code can run on GPU and TPU.
Step9:  Gradients
Here's another big difference with NumPy
Step10:  By default, variables are watched automatically, so you don't need to manually watch them
Step11:  Note that you can compute higher-order derivatives by nesting tapes
Step13:  Keras layers
While TensorFlow is an infrastructure layer for differentiable programming,
dealing with tensors, variables, and gradients,
Keras is a user interface for deep learning, dealing with
layers, models, optimizers, loss functions, metrics, and more.
Keras serves as the high-level API for TensorFlow
Step14:  You would use a Layer instance much like a Python function
Step15:  The weight variables (created in __init__) are automatically
tracked under the weights property
Step17:  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.
Layer weight creation
The self.add_weight() method gives you a shortcut for creating weights
Step18:  Layer gradients
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.
Step20:  Trainable and non-trainable weights
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
Step22:  Layers that own layers
Layers can be recursively nested to create bigger computation blocks.
Each layer will track the weights of its sublayers
(both trainable and non-trainable).
Step23:  Note that our manually-created MLP above is equivalent to the following
built-in option
Step25:  Tracking losses created by layers
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
Step27:  Any model incorporating this layer will track this regularization loss
Step28:  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.
Step29:  Keeping track of training metrics
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
Step30:  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.
You can also define your own metrics by subclassing keras.metrics.Metric.
You need to override the three functions called above
Step31:  Let's test-drive it
Step32:  Compiled functions
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.
Step33:  Training mode & inference mode
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.
Step34:  The Functional API for model-building
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")
Step35:  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
Step36:  You can always subclass the Model class (it works exactly like subclassing
Layer) if you want to leverage built-in training loops for your OO models.
Just override the Model.train_step() to
customize what happens in fit() while retaining support
for the built-in infrastructure features outlined above -- callbacks,
zero-code distribution support, and step fusing support.
You may also override test_step() to customize what happens in evaluate(),
and override predict_step() to customize what happens in predict(). For more
information, please refer to
this guide.
Step39:  End-to-end experiment example 1
Step41:  Next, we have a Decoder class, which maps the probabilistic latent space coordinates
back to a MNIST digit.
Step43:  Finally, our VariationalAutoEncoder composes together an encoder and a decoder, and
creates a KL divergence regularization loss via add_loss().
Step44:  Now, let's write a training loop. Our training step is decorated with a @tf.function to
compile into a super fast graph function.
Step45:  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
Step46:  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
Step47:  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.
End-to-end experiment example 2
Step48:  This is our training loop. For each batch of data
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Python Code:
import tensorflow as tf
from tensorflow import keras
Explanation: Introduction to Keras for Researchers
Author: fchollet<br>
Date created: 2020/04/01<br>
Last modified: 2020/10/02<br>
Description: Everything you need to know to use Keras & TensorFlow for deep learning research.
Setup
End of explanation
x = tf.constant([[5, 2], [1, 3]])
print(x)
Explanation: Introduction
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 & TensorFlow API concepts.
In this guide, you will learn about:
Tensors, variables, and gradients in TensorFlow
Creating layers by subclassing the Layer class
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.
Tensors
TensorFlow is an infrastructure layer for differentiable programming.
At its heart, it's a framework for manipulating N-dimensional arrays (tensors),
much like NumPy.
However, there are three key differences between NumPy and TensorFlow:
TensorFlow can leverage hardware accelerators such as GPUs and TPUs.
TensorFlow can automatically compute the gradient of arbitrary differentiable tensor expressions.
TensorFlow computation can be distributed to large numbers of devices on a single machine, and large number of
machines (potentially with multiple devices each).
Let's take a look at the object that is at the core of TensorFlow: the Tensor.
Here's a constant tensor:
End of explanation
x.numpy()
Explanation: You can get its value as a NumPy array by calling .numpy():
End of explanation
print("dtype:", x.dtype)
print("shape:", x.shape)
Explanation: Much like a NumPy array, it features the attributes dtype and shape:
End of explanation
print(tf.ones(shape=(2, 1)))
print(tf.zeros(shape=(2, 1)))
Explanation: A common way to create constant tensors is via tf.ones and tf.zeros (just like np.ones and np.zeros):
End of explanation
x = tf.random.normal(shape=(2, 2), mean=0.0, stddev=1.0)
x = tf.random.uniform(shape=(2, 2), minval=0, maxval=10, dtype="int32")
Explanation: You can also create random constant tensors:
End of explanation
initial_value = tf.random.normal(shape=(2, 2))
a = tf.Variable(initial_value)
print(a)
Explanation: Variables
Variables are special tensors used to store mutable state (such as the weights of a neural network).
You create a Variable using some initial value:
End of explanation
new_value = tf.random.normal(shape=(2, 2))
a.assign(new_value)
for i in range(2):
    for j in range(2):
        assert a[i, j] == new_value[i, j]
added_value = tf.random.normal(shape=(2, 2))
a.assign_add(added_value)
for i in range(2):
    for j in range(2):
        assert a[i, j] == new_value[i, j] + added_value[i, j]
Explanation: You update the value of a Variable by using the methods .assign(value), .assign_add(increment), or .assign_sub(decrement):
End of explanation
a = tf.random.normal(shape=(2, 2))
b = tf.random.normal(shape=(2, 2))
c = a + b
d = tf.square(c)
e = tf.exp(d)
Explanation: Doing math in TensorFlow
If you've used NumPy, doing math in TensorFlow will look very familiar.
The main difference is that your TensorFlow code can run on GPU and TPU.
End of explanation
a = tf.random.normal(shape=(2, 2))
b = tf.random.normal(shape=(2, 2))
with tf.GradientTape() as tape:
    tape.watch(a)  # Start recording the history of operations applied to `a`
    c = tf.sqrt(tf.square(a) + tf.square(b))  # Do some math using `a`
    # What's the gradient of `c` with respect to `a`?
    dc_da = tape.gradient(c, a)
    print(dc_da)
Explanation: Gradients
Here's another big difference with NumPy: you can automatically retrieve the gradient of any differentiable expression.
Just open a GradientTape, start "watching" a tensor via tape.watch(),
and compose a differentiable expression using this tensor as input:
End of explanation
a = tf.Variable(a)
with tf.GradientTape() as tape:
    c = tf.sqrt(tf.square(a) + tf.square(b))
    dc_da = tape.gradient(c, a)
    print(dc_da)
Explanation: By default, variables are watched automatically, so you don't need to manually watch them:
End of explanation
with tf.GradientTape() as outer_tape:
    with tf.GradientTape() as tape:
        c = tf.sqrt(tf.square(a) + tf.square(b))
        dc_da = tape.gradient(c, a)
    d2c_da2 = outer_tape.gradient(dc_da, a)
    print(d2c_da2)
Explanation: Note that you can compute higher-order derivatives by nesting tapes:
End of explanation
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
Explanation: Keras layers
While TensorFlow is an infrastructure layer for differentiable programming,
dealing with tensors, variables, and gradients,
Keras is a user interface for deep learning, dealing with
layers, models, optimizers, loss functions, metrics, and more.
Keras serves as the high-level API for TensorFlow:
Keras is what makes TensorFlow simple and productive.
The Layer class 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:
End of explanation
# 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)
Explanation: You would use a Layer instance much like a Python function:
End of explanation
assert linear_layer.weights == [linear_layer.w, linear_layer.b]
Explanation: The weight variables (created in __init__) are automatically
tracked under the weights property:
End of explanation
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)))
Explanation: 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.
Layer weight creation
The self.add_weight() method gives you a shortcut for creating weights:
End of explanation
# 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 the loss wrt the weights.
    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))
Explanation: Layer gradients
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.
End of explanation
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 == []
Explanation: Trainable and non-trainable weights
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:
End of explanation
# 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
Explanation: Layers that own layers
Layers can be recursively nested to create bigger computation blocks.
Each layer will track the weights of its sublayers
(both trainable and non-trainable).
End of explanation
mlp = keras.Sequential(
    [
        keras.layers.Dense(32, activation=tf.nn.relu),
        keras.layers.Dense(32, activation=tf.nn.relu),
        keras.layers.Dense(10),
    ]
)
Explanation: Note that our manually-created MLP above is equivalent to the following
built-in option:
End of explanation
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
Explanation: Tracking losses created by layers
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:
End of explanation
# 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
Explanation: Any model incorporating this layer will track this regularization loss:
End of explanation
# 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 the loss wrt the weights.
        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))
Explanation: 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.
End of explanation
# 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_state()
Explanation: Keeping track of training metrics
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_state()
Here's a simple example:
End of explanation
class F1Score(keras.metrics.Metric):
    def __init__(self, name="f1_score", dtype="float32", threshold=0.5, **kwargs):
        super().__init__(name=name, dtype=dtype, **kwargs)
        self.threshold = 0.5
        self.true_positives = self.add_weight(
            name="tp", dtype=dtype, initializer="zeros"
        )
        self.false_positives = self.add_weight(
            name="fp", dtype=dtype, initializer="zeros"
        )
        self.false_negatives = self.add_weight(
            name="fn", dtype=dtype, initializer="zeros"
        )
    def update_state(self, y_true, y_pred, sample_weight=None):
        y_pred = tf.math.greater_equal(y_pred, self.threshold)
        y_true = tf.cast(y_true, tf.bool)
        y_pred = tf.cast(y_pred, tf.bool)
        true_positives = tf.cast(y_true & y_pred, self.dtype)
        false_positives = tf.cast(~y_true & y_pred, self.dtype)
        false_negatives = tf.cast(y_true & ~y_pred, self.dtype)
        if sample_weight is not None:
            sample_weight = tf.cast(sample_weight, self.dtype)
            true_positives *= sample_weight
            false_positives *= sample_weight
            false_negatives *= sample_weight
        self.true_positives.assign_add(tf.reduce_sum(true_positives))
        self.false_positives.assign_add(tf.reduce_sum(false_positives))
        self.false_negatives.assign_add(tf.reduce_sum(false_negatives))
    def result(self):
        precision = self.true_positives / (self.true_positives + self.false_positives)
        recall = self.true_positives / (self.true_positives + self.false_negatives)
        return precision * recall * 2.0 / (precision + recall)
    def reset_state(self):
        self.true_positives.assign(0)
        self.false_positives.assign(0)
        self.false_negatives.assign(0)
Explanation: 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.
You can also define your own metrics by subclassing keras.metrics.Metric.
You need to override the three functions called above:
Override update_state() to update the statistic values.
Override result() to return the metric value.
Override reset_state() to reset the metric to its initial state.
Here is an example where we implement the F1-score metric
(with support for sample weighting).
End of explanation
m = F1Score()
m.update_state([0, 1, 0, 0], [0.3, 0.5, 0.8, 0.9])
print("Intermediate result:", float(m.result()))
m.update_state([1, 1, 1, 1], [0.1, 0.7, 0.6, 0.0])
print("Final result:", float(m.result()))
Explanation: Let's test-drive it:
End of explanation
# 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))
Explanation: Compiled functions
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.
End of explanation
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)
Explanation: Training mode & inference mode
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.
End of explanation
# 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)
Explanation: The Functional API for model-building
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"):
End of explanation
inputs = tf.keras.Input(shape=(784,), dtype="float32")
x = keras.layers.Dense(32, activation="relu")(inputs)
x = keras.layers.Dense(32, activation="relu")(x)
outputs = keras.layers.Dense(10)(x)
model = tf.keras.Model(inputs, outputs)
# Specify the loss, optimizer, and metrics with `compile()`.
model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.Adam(learning_rate=1e-3),
    metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
# Train the model with the dataset for 2 epochs.
model.fit(dataset, epochs=2)
model.predict(dataset)
model.evaluate(dataset)
Explanation: 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(), predict() and evaluate() (configured via the compile() method).
These built-in functions give you access to the
following built-in training infrastructure features:
Callbacks. You can leverage built-in
callbacks for early-stopping, model checkpointing,
and monitoring training with TensorBoard. You can also
implement custom callbacks if needed.
Distributed training. You
can easily scale up your training to multiple GPUs, TPU, or even multiple machines
with the tf.distribute API -- with no changes to your code.
Step fusing.
With the steps_per_execution argument in Model.compile(), you can process
multiple batches in a single tf.function call, which greatly improves
device utilization on TPUs.
We won't go into the details, but we provide a simple code example
below. It leverages the built-in training infrastructure to implement the MNIST
example above.
End of explanation
class CustomModel(keras.Model):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.loss_tracker = keras.metrics.Mean(name="loss")
        self.accuracy = keras.metrics.SparseCategoricalAccuracy()
        self.loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
        self.optimizer = keras.optimizers.Adam(learning_rate=1e-3)
    def train_step(self, data):
        # Unpack the data. Its structure depends on your model and
        # on what you pass to `fit()`.
        x, y = data
        with tf.GradientTape() as tape:
            y_pred = self(x, training=True)  # Forward pass
            loss = self.loss_fn(y, y_pred)
        gradients = tape.gradient(loss, self.trainable_weights)
        self.optimizer.apply_gradients(zip(gradients, self.trainable_weights))
        # Update metrics (includes the metric that tracks the loss)
        self.loss_tracker.update_state(loss)
        self.accuracy.update_state(y, y_pred)
        # Return a dict mapping metric names to current value
        return {"loss": self.loss_tracker.result(), "accuracy": self.accuracy.result()}
    @property
    def metrics(self):
        # We list our `Metric` objects here so that `reset_states()` can be
        # called automatically at the start of each epoch.
        return [self.loss_tracker, self.accuracy]
inputs = tf.keras.Input(shape=(784,), dtype="float32")
x = keras.layers.Dense(32, activation="relu")(inputs)
x = keras.layers.Dense(32, activation="relu")(x)
outputs = keras.layers.Dense(10)(x)
model = CustomModel(inputs, outputs)
model.compile()
model.fit(dataset, epochs=2)
Explanation: You can always subclass the Model class (it works exactly like subclassing
Layer) if you want to leverage built-in training loops for your OO models.
Just override the Model.train_step() to
customize what happens in fit() while retaining support
for the built-in infrastructure features outlined above -- callbacks,
zero-code distribution support, and step fusing support.
You may also override test_step() to customize what happens in evaluate(),
and override predict_step() to customize what happens in predict(). For more
information, please refer to
this guide.
End of explanation
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
Explanation: End-to-end experiment example 1: variational autoencoders.
Here are some of the things you've learned so far:
A Layer encapsulates 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).
End of explanation
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)
Explanation: Next, we have a Decoder class, which maps the probabilistic latent space coordinates
back to a MNIST digit.
End of explanation
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
Explanation: Finally, our VariationalAutoEncoder composes together an encoder and a decoder, and
creates a KL divergence regularization loss via add_loss().
End of explanation
# 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
Explanation: Now, let's write a training loop. Our training step is decorated with a @tf.function to
compile into a super fast graph function.
End of explanation
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)
Explanation: 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:
End of explanation
# 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)
Explanation: 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:
End of explanation
import numpy as np
input_dim = 784
classes = 10
# This is the main network we'll actually use to predict labels.
main_network = 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 `main_network` won't create new variables.
for layer in main_network.layers:
    layer.built = True
# This is the number of weight coefficients to generate. Each layer in the
# main network requires output_dim * input_dim + output_dim coefficients.
num_weights_to_generate = (classes * 64 + classes) + (64 * input_dim + 64)
# This is the hypernetwork that generates the weights of the `main_network` above.
hypernetwork = keras.Sequential(
    [
        keras.layers.Dense(16, activation=tf.nn.relu),
        keras.layers.Dense(num_weights_to_generate, activation=tf.nn.sigmoid),
    ]
)
Explanation: 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.
End-to-end experiment example 2: hypernetworks.
Let's take a look at another kind of research experiment: hypernetworks.
The idea is to use a small deep neural network (the hypernetwork) to generate
the weights for a larger network (the main network).
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.
End of explanation
# 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 = hypernetwork(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.
        main_network.layers[0].kernel = w0
        main_network.layers[0].bias = b0
        main_network.layers[1].kernel = w1
        main_network.layers[1].bias = b1
        # Inference on the outer model.
        preds = main_network(x)
        loss = loss_fn(y, preds)
    # Train only inner model.
    grads = tape.gradient(loss, hypernetwork.trainable_weights)
    optimizer.apply_gradients(zip(grads, hypernetwork.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
Explanation: This is our training loop. For each batch of data:
We use hypernetwork to generate an array of weight coefficients, weights_pred
We reshape these coefficients into kernel & bias tensors for the main_network
We run the forward pass of the main_network to compute the actual MNIST predictions
We run backprop through the weights of the hypernetwork to minimize the
final classification loss
End of explanation
<END_TASK>