通过子类化创建新的层和模型

设置

import tensorflow as tffrom tensorflow import keras

`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。

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

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。

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

2021-08-13 19:58:32.393148: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero2021-08-13 19:58:32.401011: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero2021-08-13 19:58:32.401904: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero2021-08-13 19:58:32.403509: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMATo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.2021-08-13 19:58:32.404028: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero2021-08-13 19:58:32.404900: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero2021-08-13 19:58:32.405739: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero2021-08-13 19:58:32.975828: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero2021-08-13 19:58:32.976731: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero2021-08-13 19:58:32.977545: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero2021-08-13 19:58:32.978384: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14648 MB memory:  -> device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0tf.Tensor([[ 0.04646244  0.18147472 -0.03977904 -0.01213008] [ 0.04646244  0.18147472 -0.03977904 -0.01213008]], shape=(2, 4), dtype=float32)

请注意，权重 w 和 b 在被设置为层特性后会由层自动跟踪：

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

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：

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)

tf.Tensor([[-0.03581849  0.09276912  0.03415143  0.02351041] [-0.03581849  0.09276912  0.03415143  0.02351041]], shape=(2, 4), dtype=float32)

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：

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())

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

它是 layer.weights 的一部分，但被归类为不可训练权重：

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)

weights: 1non-trainable weights: 1trainable_weights: []

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：

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

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：

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

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：

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

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

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)。

# 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 weightsprint("weights:", len(mlp.weights))print("trainable weights:", len(mlp.trainable_weights))

weights: 6trainable weights: 6

`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：

# A layer that creates an activity regularization lossclass 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

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。

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

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：

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)

[<tf.Tensor: shape=(), dtype=float32, numpy=0.0013762739>]

在编写训练循环时应考虑这些损失，如下所示：

# 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)：

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 itmodel.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)))

1/1 [==============================] - 0s 99ms/step - loss: 0.09911/1 [==============================] - 0s 42ms/step - loss: 0.02282021-08-13 19:58:33.977054: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)<keras.callbacks.History at 0x7fcc643809d0>

`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。

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)

可通过 layer.metrics 访问以这种方式跟踪的指标：

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()))

layer.metrics: [<keras.metrics.BinaryAccuracy object at 0x7fcd1c589690>]current accuracy value: 1.0

和 add_loss() 一样，这些指标也是通过 fit() 跟踪的：

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)

1/1 [==============================] - 0s 230ms/step - loss: 1.0306 - binary_accuracy: 0.0000e+00<keras.callbacks.History at 0x7fcc6437cd10>

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：

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)

{'units': 64}

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：

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)

{'name': 'linear_8', 'trainable': True, 'dtype': 'float32', 'units': 64}

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：

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。

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

`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：

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)。

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

让我们在 MNIST 上编写一个简单的训练循环：

original_dim = 784vae = 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()))

Start of epoch 0step 0: mean loss = 0.3553step 100: mean loss = 0.1263step 200: mean loss = 0.0996step 300: mean loss = 0.0894step 400: mean loss = 0.0844step 500: mean loss = 0.0810step 600: mean loss = 0.0789step 700: mean loss = 0.0772step 800: mean loss = 0.0761step 900: mean loss = 0.0750Start of epoch 1step 0: mean loss = 0.0747step 100: mean loss = 0.0741step 200: mean loss = 0.0736step 300: mean loss = 0.0731step 400: mean loss = 0.0728step 500: mean loss = 0.0723step 600: mean loss = 0.0720step 700: mean loss = 0.0718step 800: mean loss = 0.0715step 900: mean loss = 0.0712

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：

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)

Epoch 1/2938/938 [==============================] - 3s 2ms/step - loss: 0.0749Epoch 2/2938/938 [==============================] - 2s 2ms/step - loss: 0.0676<keras.callbacks.History at 0x7fcc641e6dd0>

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：

original_dim = 784intermediate_dim = 64latent_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)

Epoch 1/3938/938 [==============================] - 3s 2ms/step - loss: 0.0745Epoch 2/3938/938 [==============================] - 2s 2ms/step - loss: 0.0677Epoch 3/3938/938 [==============================] - 2s 2ms/step - loss: 0.0675<keras.callbacks.History at 0x7fc8935f78d0>

有关详情，请务必阅读函数式 API 指南。