"""
Title: Making new layers and models via subclassing
Author: [fchollet](https://twitter.com/fchollet)
Date created: 2019/03/01
Last modified: 2023/03/16
Description: Complete guide to writing `Layer` and `Model` objects from scratch.
Accelerator: GPU
"""
"""
## Setup
"""

import tensorflow as tf
from tensorflow import keras

"""
## The `Layer` class: the combination of state (weights) and some computation

One of the central abstractions in Keras is the `Layer` class. A layer
encapsulates both a state (the layer's "weights") and a transformation from
inputs to outputs (a "call", the layer's forward pass).

Here's a densely-connected layer. It has a state: the variables `w` and `b`.
"""


class Linear(keras.layers.Layer):
    def __init__(self, units=32, input_dim=32):
        super().__init__()
        self.w = self.add_weight(
            shape=(input_dim, units), initializer="random_normal", trainable=True
        )
        self.b = self.add_weight(shape=(units,), initializer="zeros", trainable=True)

    def call(self, inputs):
        return tf.matmul(inputs, self.w) + self.b


"""
You would use a layer by calling it on some tensor input(s), much like a Python
function.
"""

x = tf.ones((2, 2))
linear_layer = Linear(4, 2)
y = linear_layer(x)
print(y)

"""
Note that the weights `w` and `b` are automatically tracked by the layer upon
being set as layer attributes:
"""

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

"""
## Layers can have non-trainable weights

Besides trainable weights, you can add non-trainable weights to a layer as
well. Such weights are meant not to be taken into account during
backpropagation, when you are training the layer.

Here's how to add and use a non-trainable weight:
"""


class ComputeSum(keras.layers.Layer):
    def __init__(self, input_dim):
        super().__init__()
        self.total = self.add_weight(
            initializer="zeros", shape=(input_dim,), trainable=False
        )

    def call(self, inputs):
        self.total.assign_add(tf.reduce_sum(inputs, axis=0))
        return self.total


x = tf.ones((2, 2))
my_sum = ComputeSum(2)
y = my_sum(x)
print(y.numpy())
y = my_sum(x)
print(y.numpy())

"""
It's part of `layer.weights`, but it gets categorized as a non-trainable weight:
"""

print("weights:", len(my_sum.weights))
print("non-trainable weights:", len(my_sum.non_trainable_weights))

# It's not included in the trainable weights:
print("trainable_weights:", my_sum.trainable_weights)

"""
## Best practice: deferring weight creation until the shape of the inputs is known

Our `Linear` layer above took an `input_dim` argument that was used to compute
the shape of the weights `w` and `b` in `__init__()`:
"""


class Linear(keras.layers.Layer):
    def __init__(self, units=32, input_dim=32):
        super().__init__()
        self.w = self.add_weight(
            shape=(input_dim, units), initializer="random_normal", trainable=True
        )
        self.b = self.add_weight(shape=(units,), initializer="zeros", trainable=True)

    def call(self, inputs):
        return tf.matmul(inputs, self.w) + self.b


"""
In many cases, you may not know in advance the size of your inputs, and you
would like to lazily create weights when that value becomes known, some time
after instantiating the layer.

In the Keras API, we recommend creating layer weights in the `build(self,
inputs_shape)` method of your layer. Like this:
"""


class Linear(keras.layers.Layer):
    def __init__(self, units=32):
        super().__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


"""
The `__call__()` method of your layer will automatically run build the first time
it is called. You now have a layer that's lazy and thus easier to use:
"""

# At instantiation, we don't know on what inputs this is going to get called
linear_layer = Linear(32)

# The layer's weights are created dynamically the first time the layer is called
y = linear_layer(x)


"""
Implementing `build()` separately as shown above nicely separates creating weights
only once from using weights in every call. However, for some advanced custom
layers, it can become impractical to separate the state creation and computation.
Layer implementers are allowed to defer weight creation to the first `__call__()`,
but need to take care that later calls use the same weights. In addition, since
`__call__()` is likely to be executed for the first time inside a `tf.function`,
any variable creation that takes place in `__call__()` should be wrapped in a `tf.init_scope`.
"""

"""
## Layers are recursively composable

If you assign a Layer instance as an attribute of another Layer, the outer layer
will start tracking the weights created by the inner layer.

We recommend creating such sublayers in the `__init__()` method and leave it to
the first `__call__()` to trigger building their weights.
"""


class MLPBlock(keras.layers.Layer):
    def __init__(self):
        super().__init__()
        self.linear_1 = Linear(32)
        self.linear_2 = Linear(32)
        self.linear_3 = Linear(1)

    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 = MLPBlock()
y = mlp(tf.ones(shape=(3, 64)))  # The first call to the `mlp` will create the weights
print("weights:", len(mlp.weights))
print("trainable weights:", len(mlp.trainable_weights))

"""
## The `add_loss()` method

When writing the `call()` method of a layer, you can create loss tensors that
you will want to use later, when writing your training loop. This is doable by
calling `self.add_loss(value)`:
"""


# A layer that creates an activity regularization loss
class ActivityRegularizationLayer(keras.layers.Layer):
    def __init__(self, rate=1e-2):
        super().__init__()
        self.rate = rate

    def call(self, inputs):
        self.add_loss(self.rate * tf.reduce_mean(inputs))
        return inputs


"""
Notice that `add_loss()` can take the result of plain TensorFlow operations.
There is no need to call a `Loss` object here.

These losses (including those created by any inner layer) can be retrieved via
`layer.losses`. This property is reset at the start of every `__call__()` to
the top-level layer, so that `layer.losses` always contains the loss values
created during the last forward pass.
"""


class OuterLayer(keras.layers.Layer):
    def __init__(self):
        super().__init__()
        self.activity_reg = ActivityRegularizationLayer(1e-2)

    def call(self, inputs):
        return self.activity_reg(inputs)


layer = OuterLayer()
assert len(layer.losses) == 0  # No losses yet since the layer has never been called

_ = layer(tf.zeros(1, 1))
assert len(layer.losses) == 1  # We created one loss value

# `layer.losses` gets reset at the start of each __call__
_ = layer(tf.zeros(1, 1))
assert len(layer.losses) == 1  # This is the loss created during the call above

"""
In addition, the `loss` property also contains regularization losses created
for the weights of any inner layer:
"""


class OuterLayerWithKernelRegularizer(keras.layers.Layer):
    def __init__(self):
        super().__init__()
        self.dense = keras.layers.Dense(
            32, kernel_regularizer=keras.regularizers.l2(1e-3)
        )

    def call(self, inputs):
        return self.dense(inputs)


layer = OuterLayerWithKernelRegularizer()
_ = layer(tf.zeros((1, 1)))

# This is `1e-3 * sum(layer.dense.kernel ** 2)`,
# created by the `kernel_regularizer` above.
print(layer.losses)

"""
These losses are meant to be taken into account when writing training loops,
like this:

```python
# Instantiate an optimizer.
optimizer = keras.optimizers.SGD(learning_rate=1e-3)
loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True)

# Iterate over the batches of a dataset.
for x_batch_train, y_batch_train in train_dataset:
    with tf.GradientTape() as tape:
        logits = layer(x_batch_train)  # Logits for this minibatch
        # Loss value for this minibatch
        loss_value = loss_fn(y_batch_train, logits)
        # Add extra losses created during this forward pass:
        loss_value += sum(model.losses)

    grads = tape.gradient(loss_value, model.trainable_weights)
    optimizer.apply_gradients(zip(grads, model.trainable_weights))
```
"""

"""
For a detailed guide about writing training loops, see the
[guide to writing a training loop from scratch](/guides/writing_a_training_loop_from_scratch/).

These losses also work seamlessly with `fit()` (they get automatically summed
and added to the main loss, if any):
"""

import numpy as np

inputs = keras.Input(shape=(3,))
outputs = ActivityRegularizationLayer()(inputs)
model = keras.Model(inputs, outputs)

# If there is a loss passed in `compile`, the regularization
# losses get added to it
model.compile(optimizer="adam", loss="mse")
model.fit(np.random.random((2, 3)), np.random.random((2, 3)))

# It's also possible not to pass any loss in `compile`,
# since the model already has a loss to minimize, via the `add_loss`
# call during the forward pass!
model.compile(optimizer="adam")
model.fit(np.random.random((2, 3)), np.random.random((2, 3)))

"""
## You can optionally enable serialization on your layers

If you need your custom layers to be serializable as part of a
[Functional model](/guides/functional_api/), you can optionally implement a `get_config()`
method:
"""


class Linear(keras.layers.Layer):
    def __init__(self, units=32):
        super().__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

    def get_config(self):
        return {"units": self.units}


# Now you can recreate the layer from its config:
layer = Linear(64)
config = layer.get_config()
print(config)
new_layer = Linear.from_config(config)

"""
Note that the `__init__()` method of the base `Layer` class takes some keyword
arguments, in particular a `name` and a `dtype`. It's good practice to pass
these arguments to the parent class in `__init__()` and to include them in the
layer config:
"""


class Linear(keras.layers.Layer):
    def __init__(self, units=32, **kwargs):
        super().__init__(**kwargs)
        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

    def get_config(self):
        config = super().get_config()
        config.update({"units": self.units})
        return config


layer = Linear(64)
config = layer.get_config()
print(config)
new_layer = Linear.from_config(config)

"""
If you need more flexibility when deserializing the layer from its config, you
can also override the `from_config()` class method. This is the base
implementation of `from_config()`:

```python
def from_config(cls, config):
  return cls(**config)
```

To learn more about serialization and saving, see the complete
[guide to saving and serializing models](/guides/serialization_and_saving/).
"""

"""
## Privileged `training` argument in the `call()` method

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.
"""


class CustomDropout(keras.layers.Layer):
    def __init__(self, rate, **kwargs):
        super().__init__(**kwargs)
        self.rate = rate

    def call(self, inputs, training=False):
        if training:
            return tf.nn.dropout(inputs, rate=self.rate)
        return inputs


"""
## Privileged `mask` argument in the `call()` method

The other privileged argument supported by `call()` is the `mask` argument.

You will find it in all Keras RNN layers. A mask is a boolean tensor (one
boolean value per timestep in the input) used to skip certain input timesteps
when processing timeseries data.

Keras will automatically pass the correct `mask` argument to `__call__()` for
layers that support it, when a mask is generated by a prior layer.
Mask-generating layers are the `Embedding`
layer configured with `mask_zero=True`, and the `Masking` layer.

To learn more about masking and how to write masking-enabled layers, please
check out the guide
["understanding padding and masking"](/guides/understanding_masking_and_padding/).
"""

"""
## The `Model` class

In general, you will use the `Layer` class to define inner computation blocks,
and will use the `Model` class to define the outer model -- the object you
will train.

For instance, in a ResNet50 model, you would have several ResNet blocks
subclassing `Layer`, and a single `Model` encompassing the entire ResNet50
network.

The `Model` class has the same API as `Layer`, with the following differences:

- It exposes built-in training, evaluation, and prediction loops
(`model.fit()`, `model.evaluate()`, `model.predict()`).
- It exposes the list of its inner layers, via the `model.layers` property.
- It exposes saving and serialization APIs (`save()`, `save_weights()`...)

Effectively, the `Layer` class corresponds to what we refer to in the
literature as a "layer" (as in "convolution layer" or "recurrent layer") or as
a "block" (as in "ResNet block" or "Inception block").

Meanwhile, the `Model` class corresponds to what is referred to in the
literature as a "model" (as in "deep learning model") or as a "network" (as in
"deep neural network").

So if you're wondering, "should I use the `Layer` class or the `Model` class?",
ask yourself: will I need to call `fit()` on it? Will I need to call `save()`
on it? If so, go with `Model`. If not (either because your class is just a block
in a bigger system, or because you are writing training & saving code yourself),
use `Layer`.

For instance, we could take our mini-resnet example above, and use it to build
a `Model` that we could train with `fit()`, and that we could save with
`save_weights()`:
"""

"""
```python
class ResNet(keras.Model):

    def __init__(self, num_classes=1000):
        super().__init__()
        self.block_1 = ResNetBlock()
        self.block_2 = ResNetBlock()
        self.global_pool = layers.GlobalAveragePooling2D()
        self.classifier = Dense(num_classes)

    def call(self, inputs):
        x = self.block_1(inputs)
        x = self.block_2(x)
        x = self.global_pool(x)
        return self.classifier(x)


resnet = ResNet()
dataset = ...
resnet.fit(dataset, epochs=10)
resnet.save(filepath.keras)
```
"""

"""
## Putting it all together: an end-to-end example

Here's what 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.
- Layers can create and track losses (typically regularization losses)
via `add_loss()`.
- The outer container, the thing you want to train, is a `Model`. A `Model` is
just like a `Layer`, but with added training and serialization utilities.

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 `Model`, built as a nested composition of layers
that subclass `Layer`. It will feature a regularization loss (KL divergence).
"""

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 = 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, name="encoder", **kwargs):
        super().__init__(name=name, **kwargs)
        self.dense_proj = layers.Dense(intermediate_dim, activation="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


class Decoder(layers.Layer):
    """Converts z, the encoded digit vector, back into a readable digit."""

    def __init__(self, original_dim, intermediate_dim=64, name="decoder", **kwargs):
        super().__init__(name=name, **kwargs)
        self.dense_proj = layers.Dense(intermediate_dim, activation="relu")
        self.dense_output = layers.Dense(original_dim, activation="sigmoid")

    def call(self, inputs):
        x = self.dense_proj(inputs)
        return self.dense_output(x)


class VariationalAutoEncoder(keras.Model):
    """Combines the encoder and decoder into an end-to-end model for training."""

    def __init__(
        self,
        original_dim,
        intermediate_dim=64,
        latent_dim=32,
        name="autoencoder",
        **kwargs
    ):
        super().__init__(name=name, **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


"""
Let's write a simple training loop on MNIST:
"""

original_dim = 784
vae = VariationalAutoEncoder(original_dim, 64, 32)

optimizer = keras.optimizers.Adam(learning_rate=1e-3)
mse_loss_fn = keras.losses.MeanSquaredError()

loss_metric = keras.metrics.Mean()

(x_train, _), _ = keras.datasets.mnist.load_data()
x_train = x_train.reshape(60000, 784).astype("float32") / 255

train_dataset = tf.data.Dataset.from_tensor_slices(x_train)
train_dataset = train_dataset.shuffle(buffer_size=1024).batch(64)

epochs = 2

# Iterate over epochs.
for epoch in range(epochs):
    print("Start of epoch %d" % (epoch,))

    # Iterate over the batches of the dataset.
    for step, x_batch_train in enumerate(train_dataset):
        with tf.GradientTape() as tape:
            reconstructed = vae(x_batch_train)
            # Compute reconstruction loss
            loss = mse_loss_fn(x_batch_train, reconstructed)
            loss += sum(vae.losses)  # Add KLD regularization loss

        grads = tape.gradient(loss, vae.trainable_weights)
        optimizer.apply_gradients(zip(grads, vae.trainable_weights))

        loss_metric(loss)

        if step % 100 == 0:
            print("step %d: mean loss = %.4f" % (step, loss_metric.result()))

"""
Note that since the VAE is subclassing `Model`, it features built-in training
loops. So you could also have trained it like this:
"""

vae = VariationalAutoEncoder(784, 64, 32)

optimizer = keras.optimizers.Adam(learning_rate=1e-3)

vae.compile(optimizer, loss=keras.losses.MeanSquaredError())
vae.fit(x_train, x_train, epochs=2, batch_size=64)
