在PyTorch中自定义fit()函数中的操作代码

import os

# This guide can only be run with the torch backend.
os.environ["KERAS_BACKEND"] = "torch"

import torch
import keras
from keras import layers
import numpy as np

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

        # Call torch.nn.Module.zero_grad() to clear the leftover gradients
        # for the weights from the previous train step.
        self.zero_grad()

        # Compute loss
        y_pred = self(x, training=True)  # Forward pass
        loss = self.compute_loss(y=y, y_pred=y_pred)

        # Call torch.Tensor.backward() on the loss to compute gradients
        # for the weights.
        loss.backward()

        trainable_weights = [v for v in self.trainable_weights]
        gradients = [v.value.grad for v in trainable_weights]

        # Update weights
        with torch.no_grad():
            self.optimizer.apply(gradients, trainable_weights)

        # 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
        # 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"])

# 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 551us/step - mae: 0.6533 - loss: 0.6036
Epoch 2/3
 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 522us/step - mae: 0.4013 - loss: 0.2522
Epoch 3/3
 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 516us/step - mae: 0.3813 - loss: 0.2256

<keras.src.callbacks.history.History at 0x299b7baf0>

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

        # Call torch.nn.Module.zero_grad() to clear the leftover gradients
        # for the weights from the previous train step.
        self.zero_grad()

        # Compute loss
        y_pred = self(x, training=True)  # Forward pass
        loss = self.loss_fn(y, y_pred)

        # Call torch.Tensor.backward() on the loss to compute gradients
        # for the weights.
        loss.backward()

        trainable_weights = [v for v in self.trainable_weights]
        gradients = [v.value.grad for v in trainable_weights]

        # Update weights
        with torch.no_grad():
            self.optimizer.apply(gradients, trainable_weights)

        # 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 461us/step - loss: 0.2470 - mae: 0.3953
Epoch 2/5
 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 456us/step - loss: 0.2386 - mae: 0.3910
Epoch 3/5
 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 456us/step - loss: 0.2359 - mae: 0.3901
Epoch 4/5
 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 480us/step - loss: 0.2013 - mae: 0.3572
Epoch 5/5
 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 463us/step - loss: 0.1903 - mae: 0.3480

<keras.src.callbacks.history.History at 0x299c5eec0>

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

        # Call torch.nn.Module.zero_grad() to clear the leftover gradients
        # for the weights from the previous train step.
        self.zero_grad()

        # Compute loss
        y_pred = self(x, training=True)  # Forward pass
        loss = self.compute_loss(
            y=y,
            y_pred=y_pred,
            sample_weight=sample_weight,
        )

        # Call torch.Tensor.backward() on the loss to compute gradients
        # for the weights.
        loss.backward()

        trainable_weights = [v for v in self.trainable_weights]
        gradients = [v.value.grad for v in trainable_weights]

        # Update weights
        with torch.no_grad():
            self.optimizer.apply(gradients, trainable_weights)

        # 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, 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 499us/step - mae: 1.4332 - loss: 1.0769
Epoch 2/3
 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 520us/step - mae: 0.9250 - loss: 0.5614
Epoch 3/3
 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 502us/step - mae: 0.6069 - loss: 0.2653

<keras.src.callbacks.history.History at 0x299c82bf0>

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 325us/step - mae: 0.4427 - loss: 0.2993

[0.2726495862007141, 0.42286917567253113]

# 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",
)

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)
        self.built = True

    @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):
        device = "cuda" if torch.cuda.is_available() else "cpu"
        if isinstance(real_images, tuple):
            real_images = real_images[0]
        # Sample random points in the latent space
        batch_size = real_images.shape[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
        real_images = torch.tensor(real_images, device=device)
        combined_images = torch.concat([generated_images, real_images], axis=0)

        # Assemble labels discriminating real from fake images
        labels = torch.concat(
            [
                torch.ones((batch_size, 1), device=device),
                torch.zeros((batch_size, 1), device=device),
            ],
            axis=0,
        )
        # Add random noise to the labels - important trick!
        labels += 0.05 * keras.random.uniform(labels.shape, seed=self.seed_generator)

        # Train the discriminator
        self.zero_grad()
        predictions = self.discriminator(combined_images)
        d_loss = self.loss_fn(labels, predictions)
        d_loss.backward()
        grads = [v.value.grad for v in self.discriminator.trainable_weights]
        with torch.no_grad():
            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 = torch.zeros((batch_size, 1), device=device)

        # Train the generator (note that we should *not* update the weights
        # of the discriminator)!
        self.zero_grad()
        predictions = self.discriminator(self.generator(random_latent_vectors))
        g_loss = self.loss_fn(misleading_labels, predictions)
        grads = g_loss.backward()
        grads = [v.value.grad for v in self.generator.trainable_weights]
        with torch.no_grad():
            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(),
        }

# 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))

# Create a TensorDataset
dataset = torch.utils.data.TensorDataset(
    torch.from_numpy(all_digits), torch.from_numpy(all_digits)
)
# Create a DataLoader
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)

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),
)

gan.fit(dataloader, epochs=1)

1094/1094 ━━━━━━━━━━━━━━━━━━━━ 1582s 1s/step - d_loss: 0.3581 - g_loss: 2.0571

<keras.src.callbacks.history.History at 0x299ce1840>