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: 所有你需要知道的：使用Keras & TensorFlow来做深度学习研究 <br>
翻译： 叶正
设置
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 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))

"""
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 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))

"""
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_states()

"""
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_states()

Here's a simple example:
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: 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.
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
"""


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: 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.
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 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),
    ]
)

"""
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.
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.
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 = 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

"""
Explanation: 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
End of explanation
"""


