#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Explanation: Copyright 2020 The TensorFlow Authors.
End of explanation
"""


import tensorflow as tf
from tensorflow import keras

"""
Explanation: 通过子类化创建新的层和模型
<table class="tfo-notebook-buttons" align="left">
  <td data-segment-approved="false">     <a target="_blank" href="https://tensorflow.google.cn/guide/keras/custom_layers_and_models"><img src="https://tensorflow.google.cn/images/tf_logo_32px.png">在 TensorFlow.org 上查看</a>   </td>
  <td data-segment-approved="false"><a target="_blank" href="https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/guide/keras/custom_layers_and_models.ipynb"><img src="https://tensorflow.google.cn/images/colab_logo_32px.png">在 Google Colab 中运行 </a></td>
  <td data-segment-approved="false">     <a target="_blank" href="https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/guide/keras/custom_layers_and_models.ipynb"><img src="https://tensorflow.google.cn/images/GitHub-Mark-32px.png">在 GitHub 上查看源代码</a>   </td>
  <td data-segment-approved="false">     <a href="https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/guide/keras/custom_layers_and_models.ipynb"><img src="https://tensorflow.google.cn/images/download_logo_32px.png">下载笔记本</a>   </td>
</table>

设置
End of explanation
"""


class Linear(keras.layers.Layer):
    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: Layer 类：状态（权重）和部分计算的组合
Keras 的一个中心抽象是 Layer 类。层封装了状态（层的“权重”）和从输入到输出的转换（“调用”，即层的前向传递）。
下面是一个密集连接的层。它具有一个状态：变量 w 和 b。
End of explanation
"""


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

"""
Explanation: 您可以在某些张量输入上通过调用来使用层，这一点很像 Python 函数。
End of explanation
"""


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

"""
Explanation: 请注意，权重 w 和 b 在被设置为层特性后会由层自动跟踪：
End of explanation
"""


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


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

"""
Explanation: 请注意，您还可以使用一种更加快捷的方式为层添加权重：add_weight() 方法：
End of explanation
"""


class ComputeSum(keras.layers.Layer):
    def __init__(self, input_dim):
        super(ComputeSum, self).__init__()
        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


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

"""
Explanation: 层可以具有不可训练权重
除了可训练权重外，您还可以向层添加不可训练权重。训练层时，不必在反向传播期间考虑此类权重。
以下是添加和使用不可训练权重的方式：
End of explanation
"""


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)

"""
Explanation: 它是 layer.weights 的一部分，但被归类为不可训练权重：
End of explanation
"""


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


"""
Explanation: 最佳做法：将权重创建推迟到得知输入的形状之后
上面的 Linear 层接受了一个 input_dim 参数，用于计算 __init__() 中权重 w 和 b 的形状：
End of explanation
"""


class Linear(keras.layers.Layer):
    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


"""
Explanation: 在许多情况下，您可能事先不知道输入的大小，并希望在得知该值时（对层进行实例化后的某个时间）再延迟创建权重。
在 Keras API 中，我们建议您在层的 build(self, inputs_shape) 方法中创建层权重。如下所示：
End of explanation
"""


# 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)

"""
Explanation: 层的 __call__() 方法将在首次调用时自动运行构建。现在，您有了一个延迟并因此更易使用的层：
End of explanation
"""


# Let's assume we are reusing the Linear class
# with a `build` method that we defined above.


class MLPBlock(keras.layers.Layer):
    def __init__(self):
        super(MLPBlock, self).__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))

"""
Explanation: 层可递归组合
如果将层实例分配为另一个层的特性，则外部层将开始跟踪内部层的权重。
我们建议在 __init__() 方法中创建此类子层（由于子层通常具有构建方法，它们将与外部层同时构建）。
End of explanation
"""


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

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


"""
Explanation: add_loss() 方法
在编写层的 call() 方法时，您可以在编写训练循环时创建想要稍后使用的损失张量。这可以通过调用 self.add_loss(value) 来实现：
End of explanation
"""


class OuterLayer(keras.layers.Layer):
    def __init__(self):
        super(OuterLayer, self).__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

"""
Explanation: 这些损失（包括由任何内部层创建的损失）可通过 layer.losses 取回。此属性会在每个 __call__() 开始时重置到顶层，因此 layer.losses 始终包含在上一次前向传递过程中创建的损失值。
End of explanation
"""


class OuterLayerWithKernelRegularizer(keras.layers.Layer):
    def __init__(self):
        super(OuterLayerWithKernelRegularizer, self).__init__()
        self.dense = keras.layers.Dense(
            32, kernel_regularizer=tf.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)

"""
Explanation: 此外，loss 属性还包含为任何内部层的权重创建的正则化损失：
End of explanation
"""


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)))

"""
Explanation: 在编写训练循环时应考虑这些损失，如下所示：
```python
Instantiate an optimizer.
optimizer = tf.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))
```
有关编写训练循环的详细指南，请参阅从头开始编写训练循环指南。
这些损失还可以无缝使用 fit()（它们会自动求和并添加到主损失中，如果有）：
End of explanation
"""


class LogisticEndpoint(keras.layers.Layer):
    def __init__(self, name=None):
        super(LogisticEndpoint, self).__init__(name=name)
        self.loss_fn = keras.losses.BinaryCrossentropy(from_logits=True)
        self.accuracy_fn = keras.metrics.BinaryAccuracy()

    def call(self, targets, logits, sample_weights=None):
        # Compute the training-time loss value and add it
        # to the layer using `self.add_loss()`.
        loss = self.loss_fn(targets, logits, sample_weights)
        self.add_loss(loss)

        # Log accuracy as a metric and add it
        # to the layer using `self.add_metric()`.
        acc = self.accuracy_fn(targets, logits, sample_weights)
        self.add_metric(acc, name="accuracy")

        # Return the inference-time prediction tensor (for `.predict()`).
        return tf.nn.softmax(logits)


"""
Explanation: add_metric() 方法
与 add_loss() 类似，层还具有 add_metric() 方法，用于在训练过程中跟踪数量的移动平均值。
请思考下面的 "logistic endpoint" 层。它将预测和目标作为输入，计算通过 add_loss() 跟踪的损失，并计算通过 add_metric() 跟踪的准确率标量。
End of explanation
"""


layer = LogisticEndpoint()

targets = tf.ones((2, 2))
logits = tf.ones((2, 2))
y = layer(targets, logits)

print("layer.metrics:", layer.metrics)
print("current accuracy value:", float(layer.metrics[0].result()))

"""
Explanation: 可通过 layer.metrics 访问以这种方式跟踪的指标：
End of explanation
"""


inputs = keras.Input(shape=(3,), name="inputs")
targets = keras.Input(shape=(10,), name="targets")
logits = keras.layers.Dense(10)(inputs)
predictions = LogisticEndpoint(name="predictions")(logits, targets)

model = keras.Model(inputs=[inputs, targets], outputs=predictions)
model.compile(optimizer="adam")

data = {
    "inputs": np.random.random((3, 3)),
    "targets": np.random.random((3, 10)),
}
model.fit(data)

"""
Explanation: 和 add_loss() 一样，这些指标也是通过 fit() 跟踪的：
End of explanation
"""


class Linear(keras.layers.Layer):
    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

    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)

"""
Explanation: 可选择在层上启用序列化
如果需要将自定义层作为函数式模型的一部分进行序列化，您可以选择实现 get_config() 方法：
End of explanation
"""


class Linear(keras.layers.Layer):
    def __init__(self, units=32, **kwargs):
        super(Linear, self).__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(Linear, self).get_config()
        config.update({"units": self.units})
        return config


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

"""
Explanation: 请注意，基础 Layer 类的 __init__() 方法会接受一些关键字参数，尤其是 name 和 dtype。最好将这些参数传递给 __init__() 中的父类，并将其包含在层配置中：
End of explanation
"""


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

    def call(self, inputs, training=None):
        if training:
            return tf.nn.dropout(inputs, rate=self.rate)
        return inputs


"""
Explanation: 如果根据层的配置对层进行反序列化时需要更大的灵活性，还可以重写 from_config() 类方法。下面是 from_config() 的基础实现：
python
def from_config(cls, config):
  return cls(**config)
要详细了解序列化和保存，请参阅完整的保存和序列化模型指南。
call() 方法中的特权 training 参数
某些层，尤其是 BatchNormalization 层和 Dropout 层，在训练和推断期间具有不同的行为。对于此类层，标准做法是在 call() 方法中公开 training（布尔）参数。
通过在 call() 中公开此参数，可以启用内置的训练和评估循环（例如 fit()）以在训练和推断中正确使用层。
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, name="encoder", **kwargs):
        super(Encoder, self).__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(Decoder, self).__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(VariationalAutoEncoder, self).__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


"""
Explanation: call() 方法中的特权 mask 参数
call() 支持的另一个特权参数是 mask 参数。
它会出现在所有 Keras RNN 层中。掩码是布尔张量（在输入中每个时间步骤对应一个布尔值），用于在处理时间序列数据时跳过某些输入时间步骤。
当先前的层生成掩码时，Keras 会自动将正确的 mask 参数传递给 __call__()（针对支持它的层）。掩码生成层是配置了 mask_zero=True 的 Embedding 层和 Masking 层。
要详细了解遮盖以及如何编写启用遮盖的层，请查看了解填充和遮盖指南。
Model 类
通常，您会使用 Layer 类来定义内部计算块，并使用 Model 类来定义外部模型，即您将训练的对象。
例如，在 ResNet50 模型中，您会有几个子类化 Layer 的 ResNet 块，以及一个包含整个 ResNet50 网络的 Model。
Model 类具有与 Layer 相同的 API，但有如下区别：

它会公开内置训练、评估和预测循环（model.fit()、model.evaluate()、model.predict()）。
它会通过 model.layers 属性公开其内部层的列表。
它会公开保存和序列化 API（save()、save_weights()…）

实际上，Layer 类对应于我们在文献中所称的“层”（如“卷积层”或“循环层”）或“块”（如“ResNet 块”或“Inception 块”）。
同时，Model 类对应于文献中所称的“模型”（如“深度学习模型”）或“网络”（如“深度神经网络”）。
因此，如果您想知道“我应该用 Layer 类还是 Model 类？”，请问自己：我是否需要在它上面调用 fit()？我是否需要在它上面调用 save()？如果是，则使用 Model。如果不是（要么因为您的类只是更大系统中的一个块，要么因为您正在自己编写训练和保存代码），则使用 Layer。
例如，我们可以使用上面的 mini-resnet 示例，用它来构建一个 Model，该模型可以通过 fit() 进行训练，并通过 save_weights() 进行保存：
```python
class ResNet(tf.keras.Model):
def __init__(self, num_classes=1000):
    super(ResNet, self).__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)
```
汇总：端到端示例
到目前为止，您已学习以下内容：

Layer 封装了状态（在 __init__() 或 build() 中创建）和一些计算（在 call() 中定义）。
层可以递归嵌套以创建新的更大的计算块。
层可以通过 add_loss() 和 add_metric() 创建并跟踪损失（通常是正则化损失）以及指标。
您要训练的外部容器是 Model。Model 就像 Layer，但是添加了训练和序列化实用工具。

让我们将这些内容全部汇总到一个端到端示例：我们将实现一个变分自动编码器 (VAE)，并用 MNIST 数字对其进行训练。
我们的 VAE 将是 Model 的一个子类，它是作为子类化 Layer 的嵌套组合层进行构建的。它将具有正则化损失（KL 散度）。
End of explanation
"""


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

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

loss_metric = tf.keras.metrics.Mean()

(x_train, _), _ = tf.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()))

"""
Explanation: 让我们在 MNIST 上编写一个简单的训练循环：
End of explanation
"""


vae = VariationalAutoEncoder(784, 64, 32)

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

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

"""
Explanation: 请注意，由于 VAE 是 Model 的子类，它具有内置的训练循环。因此，您也可以用以下方式训练它：
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)

# Train.
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)
vae.compile(optimizer, loss=tf.keras.losses.MeanSquaredError())
vae.fit(x_train, x_train, epochs=3, batch_size=64)

"""
Explanation: 超越面向对象的开发：函数式 API
这个示例对您来说是否包含了太多面向对象的开发？您也可以使用函数式 API 来构建模型。重要的是，选择其中一种样式并不妨碍您利用以另一种样式编写的组件：您随时可以搭配使用。
例如，下面的函数式 API 示例重用了我们在上面的示例中定义的同一个 Sampling 层：
End of explanation
"""


