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设置
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
2022-12-14 21:36:00.307025: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory 2022-12-14 21:36:00.307122: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory 2022-12-14 21:36:00.307132: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
简介
Keras 提供了默认的训练与评估循环 fit() 和 evaluate()。使用内置方法进行训练和评估指南中介绍了它们的用法。
如果想要自定义模型的学习算法，同时又能利用 fit() 的便利性（例如，使用 fit() 训练 GAN），则可以将 Model 类子类化并实现自己的 train_step() 方法，此方法可在 fit() 中重复调用。自定义 fit() 的功能指南对此进行了介绍。
现在，如果您想对训练和评估进行低级别控制，则应当从头开始编写自己的训练和评估循环。这正是本指南要介绍的内容。
使用 GradientTape：第一个端到端示例
在 GradientTape 作用域内调用模型使您可以检索层的可训练权重相对于损失值的梯度。利用优化器实例，您可以使用上述梯度来更新这些变量（可以使用 model.trainable_weights 检索这些变量）。
我们考虑一个简单的 MNIST 模型：
inputs = keras.Input(shape=(784,), name="digits")
x1 = layers.Dense(64, activation="relu")(inputs)
x2 = layers.Dense(64, activation="relu")(x1)
outputs = layers.Dense(10, name="predictions")(x2)
model = keras.Model(inputs=inputs, outputs=outputs)
我们使用带自定义训练循环的 mini-batch 梯度对其进行训练。
首先，我们需要优化器、损失函数和数据集：
# Instantiate an optimizer.
optimizer = keras.optimizers.SGD(learning_rate=1e-3)
# Instantiate a loss function.
loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
# Prepare the training dataset.
batch_size = 64
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = np.reshape(x_train, (-1, 784))
x_test = np.reshape(x_test, (-1, 784))
# Reserve 10,000 samples for validation.
x_val = x_train[-10000:]
y_val = y_train[-10000:]
x_train = x_train[:-10000]
y_train = y_train[:-10000]
# Prepare the training dataset.
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)
# Prepare the validation dataset.
val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val))
val_dataset = val_dataset.batch(batch_size)
下面是我们的训练循环：
- 我们打开一个遍历各周期的
for循环 - 对于每个周期，我们打开一个分批遍历数据集的
for循环 - 对于每个批次，我们打开一个
GradientTape()作用域 - 在此作用域内，我们调用模型（前向传递）并计算损失
- 在作用域之外，我们检索模型权重相对于损失的梯度
- 最后，我们根据梯度使用优化器来更新模型的权重
epochs = 2
for epoch in range(epochs):
print("\nStart of epoch %d" % (epoch,))
# Iterate over the batches of the dataset.
for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
# Open a GradientTape to record the operations run
# during the forward pass, which enables auto-differentiation.
with tf.GradientTape() as tape:
# Run the forward pass of the layer.
# The operations that the layer applies
# to its inputs are going to be recorded
# on the GradientTape.
logits = model(x_batch_train, training=True) # Logits for this minibatch
# Compute the loss value for this minibatch.
loss_value = loss_fn(y_batch_train, logits)
# Use the gradient tape to automatically retrieve
# the gradients of the trainable variables with respect to the loss.
grads = tape.gradient(loss_value, model.trainable_weights)
# Run one step of gradient descent by updating
# the value of the variables to minimize the loss.
optimizer.apply_gradients(zip(grads, model.trainable_weights))
# Log every 200 batches.
if step % 200 == 0:
print(
"Training loss (for one batch) at step %d: %.4f"
% (step, float(loss_value))
)
print("Seen so far: %s samples" % ((step + 1) * batch_size))
Start of epoch 0 WARNING:tensorflow:5 out of the last 5 calls to <function _BaseOptimizer._update_step_xla at 0x7f98a0094700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. WARNING:tensorflow:6 out of the last 6 calls to <function _BaseOptimizer._update_step_xla at 0x7f98a0094700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. Training loss (for one batch) at step 0: 86.9427 Seen so far: 64 samples Training loss (for one batch) at step 200: 1.6592 Seen so far: 12864 samples Training loss (for one batch) at step 400: 0.9552 Seen so far: 25664 samples Training loss (for one batch) at step 600: 1.1426 Seen so far: 38464 samples Start of epoch 1 Training loss (for one batch) at step 0: 0.6918 Seen so far: 64 samples Training loss (for one batch) at step 200: 0.8808 Seen so far: 12864 samples Training loss (for one batch) at step 400: 0.5513 Seen so far: 25664 samples Training loss (for one batch) at step 600: 0.6500 Seen so far: 38464 samples
指标的低级处理
我们在此基本循环中添加指标监视。
在这种从头开始编写的训练循环中，您可以轻松重用内置指标（或编写的自定义指标）。下面列出了具体流程：
- 在循环开始时实例化指标
- 在每个批次后调用
metric.update_state() - 当您需要显示指标的当前值时，调用
metric.result() - 当您需要清除指标的状态（通常在周期结束）时，调用
metric.reset_states()
我们利用这些知识在每个周期结束时基于验证数据计算 SparseCategoricalAccuracy
# Get model
inputs = keras.Input(shape=(784,), name="digits")
x = layers.Dense(64, activation="relu", name="dense_1")(inputs)
x = layers.Dense(64, activation="relu", name="dense_2")(x)
outputs = layers.Dense(10, name="predictions")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
# Instantiate an optimizer to train the model.
optimizer = keras.optimizers.SGD(learning_rate=1e-3)
# Instantiate a loss function.
loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
# Prepare the metrics.
train_acc_metric = keras.metrics.SparseCategoricalAccuracy()
val_acc_metric = keras.metrics.SparseCategoricalAccuracy()
下面是我们的训练和评估循环：
import time
epochs = 2
for epoch in range(epochs):
print("\nStart of epoch %d" % (epoch,))
start_time = time.time()
# Iterate over the batches of the dataset.
for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
with tf.GradientTape() as tape:
logits = model(x_batch_train, training=True)
loss_value = loss_fn(y_batch_train, logits)
grads = tape.gradient(loss_value, model.trainable_weights)
optimizer.apply_gradients(zip(grads, model.trainable_weights))
# Update training metric.
train_acc_metric.update_state(y_batch_train, logits)
# Log every 200 batches.
if step % 200 == 0:
print(
"Training loss (for one batch) at step %d: %.4f"
% (step, float(loss_value))
)
print("Seen so far: %d samples" % ((step + 1) * batch_size))
# Display metrics at the end of each epoch.
train_acc = train_acc_metric.result()
print("Training acc over epoch: %.4f" % (float(train_acc),))
# Reset training metrics at the end of each epoch
train_acc_metric.reset_states()
# Run a validation loop at the end of each epoch.
for x_batch_val, y_batch_val in val_dataset:
val_logits = model(x_batch_val, training=False)
# Update val metrics
val_acc_metric.update_state(y_batch_val, val_logits)
val_acc = val_acc_metric.result()
val_acc_metric.reset_states()
print("Validation acc: %.4f" % (float(val_acc),))
print("Time taken: %.2fs" % (time.time() - start_time))
Start of epoch 0 Training loss (for one batch) at step 0: 130.6638 Seen so far: 64 samples Training loss (for one batch) at step 200: 1.2743 Seen so far: 12864 samples Training loss (for one batch) at step 400: 0.5572 Seen so far: 25664 samples Training loss (for one batch) at step 600: 1.2868 Seen so far: 38464 samples Training acc over epoch: 0.7351 Validation acc: 0.8345 Time taken: 10.28s Start of epoch 1 Training loss (for one batch) at step 0: 0.7856 Seen so far: 64 samples Training loss (for one batch) at step 200: 0.3686 Seen so far: 12864 samples Training loss (for one batch) at step 400: 0.3548 Seen so far: 25664 samples Training loss (for one batch) at step 600: 0.4795 Seen so far: 38464 samples Training acc over epoch: 0.8586 Validation acc: 0.8801 Time taken: 10.14s
使用 tf.function 加快训练步骤速度
TensorFlow 2 中的默认运行时为 Eager Execution。因此，上述训练循环以 Eager 模式执行。
这非常适合调试，但计算图编译具有无限性能优势。将您的计算描述为静态计算图可以让框架应用全局性能优化。当框架在不了解后续运算的情况下被限制为以贪婪方式依次执行运算时，全局性能优化无法实现。
您可以编译成任何函数均将张量作为输入的静态计算图。只需在上面添加一个 @tf.function 装饰器，如下所示：
@tf.function
def train_step(x, y):
with tf.GradientTape() as tape:
logits = model(x, training=True)
loss_value = loss_fn(y, logits)
grads = tape.gradient(loss_value, model.trainable_weights)
optimizer.apply_gradients(zip(grads, model.trainable_weights))
train_acc_metric.update_state(y, logits)
return loss_value
我们对评估步骤执行相同的操作：
@tf.function
def test_step(x, y):
val_logits = model(x, training=False)
val_acc_metric.update_state(y, val_logits)
现在，我们使用编译后的训练步骤重新运行训练循环：
import time
epochs = 2
for epoch in range(epochs):
print("\nStart of epoch %d" % (epoch,))
start_time = time.time()
# Iterate over the batches of the dataset.
for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
loss_value = train_step(x_batch_train, y_batch_train)
# Log every 200 batches.
if step % 200 == 0:
print(
"Training loss (for one batch) at step %d: %.4f"
% (step, float(loss_value))
)
print("Seen so far: %d samples" % ((step + 1) * batch_size))
# Display metrics at the end of each epoch.
train_acc = train_acc_metric.result()
print("Training acc over epoch: %.4f" % (float(train_acc),))
# Reset training metrics at the end of each epoch
train_acc_metric.reset_states()
# Run a validation loop at the end of each epoch.
for x_batch_val, y_batch_val in val_dataset:
test_step(x_batch_val, y_batch_val)
val_acc = val_acc_metric.result()
val_acc_metric.reset_states()
print("Validation acc: %.4f" % (float(val_acc),))
print("Time taken: %.2fs" % (time.time() - start_time))
Start of epoch 0 Training loss (for one batch) at step 0: 0.3613 Seen so far: 64 samples Training loss (for one batch) at step 200: 0.4453 Seen so far: 12864 samples Training loss (for one batch) at step 400: 0.6252 Seen so far: 25664 samples Training loss (for one batch) at step 600: 0.4868 Seen so far: 38464 samples Training acc over epoch: 0.8827 Validation acc: 0.8897 Time taken: 1.70s Start of epoch 1 Training loss (for one batch) at step 0: 0.5291 Seen so far: 64 samples Training loss (for one batch) at step 200: 0.2467 Seen so far: 12864 samples Training loss (for one batch) at step 400: 0.5289 Seen so far: 25664 samples Training loss (for one batch) at step 600: 0.5384 Seen so far: 38464 samples Training acc over epoch: 0.8960 Validation acc: 0.8981 Time taken: 1.28s
速度快了很多，对吗？
对模型跟踪的损失进行低级处理
层和模型以递归方式跟踪调用 self.add_loss(value) 的层在前向传递过程中创建的任何损失。可在前向传递结束时通过属性 model.losses 获得标量损失值的结果列表。
如果要使用这些损失分量，应将它们求和并添加到训练步骤的主要损失中。
考虑下面这个层，它会产生活动正则化损失：
class ActivityRegularizationLayer(layers.Layer):
def call(self, inputs):
self.add_loss(1e-2 * tf.reduce_sum(inputs))
return inputs
我们构建一个使用它的超简单模型：
inputs = keras.Input(shape=(784,), name="digits")
x = layers.Dense(64, activation="relu")(inputs)
# Insert activity regularization as a layer
x = ActivityRegularizationLayer()(x)
x = layers.Dense(64, activation="relu")(x)
outputs = layers.Dense(10, name="predictions")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
我们的训练步骤现在应当如下所示：
@tf.function
def train_step(x, y):
with tf.GradientTape() as tape:
logits = model(x, training=True)
loss_value = loss_fn(y, logits)
# Add any extra losses created during the forward pass.
loss_value += sum(model.losses)
grads = tape.gradient(loss_value, model.trainable_weights)
optimizer.apply_gradients(zip(grads, model.trainable_weights))
train_acc_metric.update_state(y, logits)
return loss_value
总结
现在，您已了解如何使用内置训练循环以及从头开始编写自己的训练循环。
总之，下面是一个简单的端到端示例，它将您在本指南中学到的所有知识串联起来：一个在 MNIST 数字上训练的 DCGAN。
端到端示例：从头开始的 GAN 训练循环
您可能熟悉生成对抗网络 (GAN)。通过学习图像训练数据集的隐分布（图像的“隐空间”），GAN 可以生成看起来极为真实的新图像。
一个 GAN 由两部分组成：一个“生成器”模型（可将隐空间中的点映射到图像空间中的点）和一个“判别器”模型，后者是一个可以区分真实图像（来自训练数据集）与虚假图像（生成器网络的输出）之间差异的分类器。
GAN 训练循环如下所示：
- 训练判别器。
- 在隐空间中对一批随机点采样。
- 通过“生成器”模型将这些点转换为虚假图像。
- 获取一批真实图像，并将它们与生成的图像组合。
- 训练“判别器”模型以对生成的图像与真实图像进行分类。
- 训练生成器。
- 在隐空间中对随机点采样。
- 通过“生成器”网络将这些点转换为虚假图像。
- 获取一批真实图像，并将它们与生成的图像组合。
- 训练“生成器”模型以“欺骗”判别器，并将虚假图像分类为真实图像。
有关 GAN 工作原理的详细介绍，请参阅 Deep Learning with Python。
我们来实现这个训练循环。首先，创建用于区分虚假数字和真实数字的判别器：
discriminator = keras.Sequential(
[
keras.Input(shape=(28, 28, 1)),
layers.Conv2D(64, (3, 3), strides=(2, 2), padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2D(128, (3, 3), strides=(2, 2), padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.GlobalMaxPooling2D(),
layers.Dense(1),
],
name="discriminator",
)
discriminator.summary()
Model: "discriminator"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 14, 14, 64) 640
leaky_re_lu (LeakyReLU) (None, 14, 14, 64) 0
conv2d_1 (Conv2D) (None, 7, 7, 128) 73856
leaky_re_lu_1 (LeakyReLU) (None, 7, 7, 128) 0
global_max_pooling2d (Globa (None, 128) 0
lMaxPooling2D)
dense_4 (Dense) (None, 1) 129
=================================================================
Total params: 74,625
Trainable params: 74,625
Non-trainable params: 0
_________________________________________________________________
接着，我们创建一个生成器网络，它可以将隐向量转换成形状为 (28, 28, 1)（表示 MNIST 数字）的输出：
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(alpha=0.2),
layers.Reshape((7, 7, 128)),
layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2D(1, (7, 7), padding="same", activation="sigmoid"),
],
name="generator",
)
这是关键部分：训练循环。如您所见，训练非常简单。训练步骤函数仅有 17 行代码。
# Instantiate one optimizer for the discriminator and another for the generator.
d_optimizer = keras.optimizers.Adam(learning_rate=0.0003)
g_optimizer = keras.optimizers.Adam(learning_rate=0.0004)
# Instantiate a loss function.
loss_fn = keras.losses.BinaryCrossentropy(from_logits=True)
@tf.function
def train_step(real_images):
# Sample random points in the latent space
random_latent_vectors = tf.random.normal(shape=(batch_size, latent_dim))
# Decode them to fake images
generated_images = 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((real_images.shape[0], 1))], axis=0
)
# Add random noise to the labels - important trick!
labels += 0.05 * tf.random.uniform(labels.shape)
# Train the discriminator
with tf.GradientTape() as tape:
predictions = discriminator(combined_images)
d_loss = loss_fn(labels, predictions)
grads = tape.gradient(d_loss, discriminator.trainable_weights)
d_optimizer.apply_gradients(zip(grads, discriminator.trainable_weights))
# Sample random points in the latent space
random_latent_vectors = tf.random.normal(shape=(batch_size, latent_dim))
# 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 = discriminator(generator(random_latent_vectors))
g_loss = loss_fn(misleading_labels, predictions)
grads = tape.gradient(g_loss, generator.trainable_weights)
g_optimizer.apply_gradients(zip(grads, generator.trainable_weights))
return d_loss, g_loss, generated_images
我们通过在各个图像批次上重复调用 train_step 来训练 GAN。
由于我们的判别器和生成器是卷积神经网络，因此您将在 GPU 上运行此代码。
import os
# 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)
epochs = 1 # In practice you need at least 20 epochs to generate nice digits.
save_dir = "./"
for epoch in range(epochs):
print("\nStart epoch", epoch)
for step, real_images in enumerate(dataset):
# Train the discriminator & generator on one batch of real images.
d_loss, g_loss, generated_images = train_step(real_images)
# Logging.
if step % 200 == 0:
# Print metrics
print("discriminator loss at step %d: %.2f" % (step, d_loss))
print("adversarial loss at step %d: %.2f" % (step, g_loss))
# Save one generated image
img = tf.keras.preprocessing.image.array_to_img(
generated_images[0] * 255.0, scale=False
)
img.save(os.path.join(save_dir, "generated_img" + str(step) + ".png"))
# To limit execution time we stop after 10 steps.
# Remove the lines below to actually train the model!
if step > 10:
break
Start epoch 0 discriminator loss at step 0: 0.70 adversarial loss at step 0: 0.73
就是这样！在 Colab GPU 上进行约 30 秒钟的训练后，您将获得漂亮的虚假 MNIST 数字。