► 开发者指南 / 使用 TensorFlow 自定义 `fit()` 中的行为
使用 fit() 自定义 TensorFlow 中的行为
作者： fchollet
创建时间 2020/04/15
上次修改时间 2023/06/27
描述：使用 TensorFlow 覆盖 Model 类的训练步骤。
引言
在进行监督学习时，您可以使用 `fit()`，一切都会顺利进行。
当您需要控制每一个细节时，您可以从头开始编写自己的训练循环。
但是，如果您需要一个自定义的训练算法，但仍然希望受益于 `fit()` 的便利功能，例如回调函数、内置分布式支持或步骤融合，该怎么办？
Keras 的核心原则之一是**逐步公开复杂性**。您应该始终能够以渐进的方式进入更低级别的流程。如果高级功能不能完全满足您的用例，您不应该掉入陷阱。您应该能够更好地控制细微的细节，同时保留相应数量的高级便利性。
当您需要自定义 `fit()` 的行为时，您应该**覆盖 `Model` 类的训练步骤函数**。这是 `fit()` 对每个数据批次调用的函数。然后，您可以像往常一样调用 `fit()`——它将运行您自己的学习算法。
请注意，此模式不会阻止您使用函数式 API 构建模型。无论您是构建 `Sequential` 模型、函数式 API 模型还是子类化模型，都可以这样做。
让我们看看它是如何工作的。
设置
import os
# This guide can only be run with the TF backend.
os.environ["KERAS_BACKEND"] = "tensorflow"
import tensorflow as tf
import keras
from keras import layers
import numpy as np
第一个简单的示例
让我们从一个简单的示例开始
- 我们创建一个新的类，它继承自
keras.Model。 - 我们只需覆盖方法
train_step(self, data)。 - 我们返回一个字典，将指标名称（包括损失）映射到它们当前的值。
输入参数 `data` 是作为训练数据传递给 fit 的内容
- 如果您通过调用 `fit(x, y, ...)` 传递 NumPy 数组，则 `data` 将是元组 `(x, y)`
- 如果您通过调用 `fit(dataset, ...)` 传递
tf.data.Dataset，则 `data` 将是在每个批次中由 `dataset` 生成的内容。
在 `train_step()` 方法的主体中，我们实现了一个常规的训练更新，类似于您已经熟悉的。重要的是，**我们通过 `self.compute_loss()` 计算损失**，它包装了传递给 `compile()` 的损失函数。
类似地，我们对来自 `self.metrics` 的指标调用 `metric.update_state(y, y_pred)`，以更新传递到 `compile()` 中的指标的状态，并在最后查询 `self.metrics` 的结果以检索其当前值。
class CustomModel(keras.Model):
def train_step(self, data):
# Unpack the data. Its structure depends on your model and
# on what you pass to `fit()`.
x, y = data
with tf.GradientTape() as tape:
y_pred = self(x, training=True) # Forward pass
# Compute the loss value
# (the loss function is configured in `compile()`)
loss = self.compute_loss(y=y, y_pred=y_pred)
# Compute gradients
trainable_vars = self.trainable_variables
gradients = tape.gradient(loss, trainable_vars)
# Update weights
self.optimizer.apply(gradients, trainable_vars)
# Update metrics (includes the metric that tracks the loss)
for metric in self.metrics:
if metric.name == "loss":
metric.update_state(loss)
else:
metric.update_state(y, y_pred)
# Return a dict mapping metric names to current value
return {m.name: m.result() for m in self.metrics}
让我们试试这个
# Construct and compile an instance of CustomModel
inputs = keras.Input(shape=(32,))
outputs = keras.layers.Dense(1)(inputs)
model = CustomModel(inputs, outputs)
model.compile(optimizer="adam", loss="mse", metrics=["mae"])
# Just use `fit` as usual
x = np.random.random((1000, 32))
y = np.random.random((1000, 1))
model.fit(x, y, epochs=3)
Epoch 1/3
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - mae: 0.5089 - loss: 0.3778
Epoch 2/3
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 318us/step - mae: 0.3986 - loss: 0.2466
Epoch 3/3
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 372us/step - mae: 0.3848 - loss: 0.2319
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1699222602.443035 1 device_compiler.h:187] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
<keras.src.callbacks.history.History at 0x2a5599f00>
进入更低级别
当然，您可以跳过在 `compile()` 中传递损失函数，而是在 `train_step` 中手动完成所有操作。指标也是如此。
这是一个更低级别的示例，它仅使用 `compile()` 来配置优化器
- 我们首先创建 `Metric` 实例来跟踪我们的损失和 MAE 分数（在 `__init__()` 中）。
- 我们实现了一个自定义的 `train_step()`，它更新这些指标的状态（通过调用 `update_state()`），然后查询它们（通过 `result()`）以返回它们的当前平均值，以便进度条显示并传递给任何回调函数。
- 请注意，我们需要在每个 epoch 之间对我们的指标调用 `reset_states()`！否则，调用 `result()` 将返回从训练开始以来的平均值，而我们通常使用每个 epoch 的平均值。幸运的是，框架可以为我们做到这一点：只需在模型的 `metrics` 属性中列出您想要重置的任何指标。模型将在每个 `fit()` epoch 的开头或在调用 `evaluate()` 的开头对此处列出的任何对象调用 `reset_states()`。
class CustomModel(keras.Model):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.loss_tracker = keras.metrics.Mean(name="loss")
self.mae_metric = keras.metrics.MeanAbsoluteError(name="mae")
self.loss_fn = keras.losses.MeanSquaredError()
def train_step(self, data):
x, y = data
with tf.GradientTape() as tape:
y_pred = self(x, training=True) # Forward pass
# Compute our own loss
loss = self.loss_fn(y, y_pred)
# Compute gradients
trainable_vars = self.trainable_variables
gradients = tape.gradient(loss, trainable_vars)
# Update weights
self.optimizer.apply(gradients, trainable_vars)
# Compute our own metrics
self.loss_tracker.update_state(loss)
self.mae_metric.update_state(y, y_pred)
return {
"loss": self.loss_tracker.result(),
"mae": self.mae_metric.result(),
}
@property
def metrics(self):
# We list our `Metric` objects here so that `reset_states()` can be
# called automatically at the start of each epoch
# or at the start of `evaluate()`.
return [self.loss_tracker, self.mae_metric]
# Construct an instance of CustomModel
inputs = keras.Input(shape=(32,))
outputs = keras.layers.Dense(1)(inputs)
model = CustomModel(inputs, outputs)
# We don't pass a loss or metrics here.
model.compile(optimizer="adam")
# Just use `fit` as usual -- you can use callbacks, etc.
x = np.random.random((1000, 32))
y = np.random.random((1000, 1))
model.fit(x, y, epochs=5)
Epoch 1/5
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 4.0292 - mae: 1.9270
Epoch 2/5
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 385us/step - loss: 2.2155 - mae: 1.3920
Epoch 3/5
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 336us/step - loss: 1.1863 - mae: 0.9700
Epoch 4/5
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 373us/step - loss: 0.6510 - mae: 0.6811
Epoch 5/5
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 330us/step - loss: 0.4059 - mae: 0.5094
<keras.src.callbacks.history.History at 0x2a7a02860>
支持 `sample_weight` 和 `class_weight`
您可能已经注意到，我们第一个基本示例没有提及样本加权。如果您想支持 `fit()` 参数 `sample_weight` 和 `class_weight`，只需执行以下操作
- 从 `data` 参数中解包 `sample_weight`
- 将其传递给 `compute_loss` 和 `update_state`（当然，如果您不依赖 `compile()` 来处理损失和指标，也可以手动应用它）
- 就是这样。
class CustomModel(keras.Model):
def train_step(self, data):
# Unpack the data. Its structure depends on your model and
# on what you pass to `fit()`.
if len(data) == 3:
x, y, sample_weight = data
else:
sample_weight = None
x, y = data
with tf.GradientTape() as tape:
y_pred = self(x, training=True) # Forward pass
# Compute the loss value.
# The loss function is configured in `compile()`.
loss = self.compute_loss(
y=y,
y_pred=y_pred,
sample_weight=sample_weight,
)
# Compute gradients
trainable_vars = self.trainable_variables
gradients = tape.gradient(loss, trainable_vars)
# Update weights
self.optimizer.apply(gradients, trainable_vars)
# Update the metrics.
# Metrics are configured in `compile()`.
for metric in self.metrics:
if metric.name == "loss":
metric.update_state(loss)
else:
metric.update_state(y, y_pred, sample_weight=sample_weight)
# Return a dict mapping metric names to current value.
# Note that it will include the loss (tracked in self.metrics).
return {m.name: m.result() for m in self.metrics}
# Construct and compile an instance of CustomModel
inputs = keras.Input(shape=(32,))
outputs = keras.layers.Dense(1)(inputs)
model = CustomModel(inputs, outputs)
model.compile(optimizer="adam", loss="mse", metrics=["mae"])
# You can now use sample_weight argument
x = np.random.random((1000, 32))
y = np.random.random((1000, 1))
sw = np.random.random((1000, 1))
model.fit(x, y, sample_weight=sw, epochs=3)
Epoch 1/3
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - mae: 0.4228 - loss: 0.1420
Epoch 2/3
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 449us/step - mae: 0.3751 - loss: 0.1058
Epoch 3/3
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 337us/step - mae: 0.3478 - loss: 0.0951
<keras.src.callbacks.history.History at 0x2a7491780>
提供您自己的评估步骤
如果您想对调用 `model.evaluate()` 执行相同的操作怎么办？然后，您将以完全相同的方式覆盖 `test_step`。以下是它的样子
class CustomModel(keras.Model):
def test_step(self, data):
# Unpack the data
x, y = data
# Compute predictions
y_pred = self(x, training=False)
# Updates the metrics tracking the loss
loss = self.compute_loss(y=y, y_pred=y_pred)
# Update the metrics.
for metric in self.metrics:
if metric.name == "loss":
metric.update_state(loss)
else:
metric.update_state(y, y_pred)
# Return a dict mapping metric names to current value.
# Note that it will include the loss (tracked in self.metrics).
return {m.name: m.result() for m in self.metrics}
# Construct an instance of CustomModel
inputs = keras.Input(shape=(32,))
outputs = keras.layers.Dense(1)(inputs)
model = CustomModel(inputs, outputs)
model.compile(loss="mse", metrics=["mae"])
# Evaluate with our custom test_step
x = np.random.random((1000, 32))
y = np.random.random((1000, 1))
model.evaluate(x, y)
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 927us/step - mae: 0.8518 - loss: 0.9166
[0.912325382232666, 0.8567370176315308]
总结：一个端到端的 GAN 示例
让我们逐步完成一个利用您刚刚学到的所有内容的端到端示例。
让我们考虑
- 一个旨在生成 28x28x1 图像的生成器网络。
- 一个旨在将 28x28x1 图像分类为两个类别（“假”和“真”）的判别器网络。
- 每个网络一个优化器。
- 一个用于训练判别器的损失函数。
# Create the discriminator
discriminator = keras.Sequential(
[
keras.Input(shape=(28, 28, 1)),
layers.Conv2D(64, (3, 3), strides=(2, 2), padding="same"),
layers.LeakyReLU(negative_slope=0.2),
layers.Conv2D(128, (3, 3), strides=(2, 2), padding="same"),
layers.LeakyReLU(negative_slope=0.2),
layers.GlobalMaxPooling2D(),
layers.Dense(1),
],
name="discriminator",
)
# Create the generator
latent_dim = 128
generator = keras.Sequential(
[
keras.Input(shape=(latent_dim,)),
# We want to generate 128 coefficients to reshape into a 7x7x128 map
layers.Dense(7 * 7 * 128),
layers.LeakyReLU(negative_slope=0.2),
layers.Reshape((7, 7, 128)),
layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding="same"),
layers.LeakyReLU(negative_slope=0.2),
layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding="same"),
layers.LeakyReLU(negative_slope=0.2),
layers.Conv2D(1, (7, 7), padding="same", activation="sigmoid"),
],
name="generator",
)
这是一个功能齐全的 GAN 类，覆盖 `compile()` 以使用其自己的签名，并在 `train_step` 中用 17 行代码实现了整个 GAN 算法
class GAN(keras.Model):
def __init__(self, discriminator, generator, latent_dim):
super().__init__()
self.discriminator = discriminator
self.generator = generator
self.latent_dim = latent_dim
self.d_loss_tracker = keras.metrics.Mean(name="d_loss")
self.g_loss_tracker = keras.metrics.Mean(name="g_loss")
self.seed_generator = keras.random.SeedGenerator(1337)
@property
def metrics(self):
return [self.d_loss_tracker, self.g_loss_tracker]
def compile(self, d_optimizer, g_optimizer, loss_fn):
super().compile()
self.d_optimizer = d_optimizer
self.g_optimizer = g_optimizer
self.loss_fn = loss_fn
def train_step(self, real_images):
if isinstance(real_images, tuple):
real_images = real_images[0]
# Sample random points in the latent space
batch_size = tf.shape(real_images)[0]
random_latent_vectors = keras.random.normal(
shape=(batch_size, self.latent_dim), seed=self.seed_generator
)
# Decode them to fake images
generated_images = self.generator(random_latent_vectors)
# Combine them with real images
combined_images = tf.concat([generated_images, real_images], axis=0)
# Assemble labels discriminating real from fake images
labels = tf.concat(
[tf.ones((batch_size, 1)), tf.zeros((batch_size, 1))], axis=0
)
# Add random noise to the labels - important trick!
labels += 0.05 * keras.random.uniform(
tf.shape(labels), seed=self.seed_generator
)
# Train the discriminator
with tf.GradientTape() as tape:
predictions = self.discriminator(combined_images)
d_loss = self.loss_fn(labels, predictions)
grads = tape.gradient(d_loss, self.discriminator.trainable_weights)
self.d_optimizer.apply(grads, self.discriminator.trainable_weights)
# Sample random points in the latent space
random_latent_vectors = keras.random.normal(
shape=(batch_size, self.latent_dim), seed=self.seed_generator
)
# Assemble labels that say "all real images"
misleading_labels = tf.zeros((batch_size, 1))
# Train the generator (note that we should *not* update the weights
# of the discriminator)!
with tf.GradientTape() as tape:
predictions = self.discriminator(self.generator(random_latent_vectors))
g_loss = self.loss_fn(misleading_labels, predictions)
grads = tape.gradient(g_loss, self.generator.trainable_weights)
self.g_optimizer.apply(grads, self.generator.trainable_weights)
# Update metrics and return their value.
self.d_loss_tracker.update_state(d_loss)
self.g_loss_tracker.update_state(g_loss)
return {
"d_loss": self.d_loss_tracker.result(),
"g_loss": self.g_loss_tracker.result(),
}
让我们试驾一下
# Prepare the dataset. We use both the training & test MNIST digits.
batch_size = 64
(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
all_digits = np.concatenate([x_train, x_test])
all_digits = all_digits.astype("float32") / 255.0
all_digits = np.reshape(all_digits, (-1, 28, 28, 1))
dataset = tf.data.Dataset.from_tensor_slices(all_digits)
dataset = dataset.shuffle(buffer_size=1024).batch(batch_size)
gan = GAN(discriminator=discriminator, generator=generator, latent_dim=latent_dim)
gan.compile(
d_optimizer=keras.optimizers.Adam(learning_rate=0.0003),
g_optimizer=keras.optimizers.Adam(learning_rate=0.0003),
loss_fn=keras.losses.BinaryCrossentropy(from_logits=True),
)
# To limit the execution time, we only train on 100 batches. You can train on
# the entire dataset. You will need about 20 epochs to get nice results.
gan.fit(dataset.take(100), epochs=1)
100/100 ━━━━━━━━━━━━━━━━━━━━ 51s 500ms/step - d_loss: 0.5645 - g_loss: 0.7434
<keras.src.callbacks.history.History at 0x14a4f1b10>
深度学习背后的思想很简单，那么为什么它们的实现需要如此痛苦呢？