fit()
with PyTorchAuthor: fchollet
Date created: 2023/06/27
Last modified: 2024/08/01
Description: Overriding the training step of the Model class with PyTorch.
When you're doing supervised learning, you can use fit()
and everything works
smoothly.
When you need to take control of every little detail, you can write your own training loop entirely from scratch.
But what if you need a custom training algorithm, but you still want to benefit from
the convenient features of fit()
, such as callbacks, built-in distribution support,
or step fusing?
A core principle of Keras is progressive disclosure of complexity. You should always be able to get into lower-level workflows in a gradual way. You shouldn't fall off a cliff if the high-level functionality doesn't exactly match your use case. You should be able to gain more control over the small details while retaining a commensurate amount of high-level convenience.
When you need to customize what fit()
does, you should override the training step
function of the Model
class. This is the function that is called by fit()
for
every batch of data. You will then be able to call fit()
as usual – and it will be
running your own learning algorithm.
Note that this pattern does not prevent you from building models with the Functional
API. You can do this whether you're building Sequential
models, Functional API
models, or subclassed models.
Let's see how that works.
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
Let's start from a simple example:
keras.Model
.train_step(self, data)
.The input argument data
is what gets passed to fit as training data:
fit(x, y, ...)
, then data
will be the tuple
(x, y)
torch.utils.data.DataLoader
or a tf.data.Dataset
,
by calling fit(dataset, ...)
, then data
will be what gets yielded
by dataset
at each batch.In the body of the train_step()
method, we implement a regular training update,
similar to what you are already familiar with. Importantly, we compute the loss via
self.compute_loss()
, which wraps the loss(es) function(s) that were passed to
compile()
.
Similarly, we call metric.update_state(y, y_pred)
on metrics from self.metrics
,
to update the state of the metrics that were passed in compile()
,
and we query results from self.metrics
at the end to retrieve their current value.
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}
Let's try this out:
# 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: 0.3410 - loss: 0.1772
Epoch 2/3
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - mae: 0.3336 - loss: 0.1695
Epoch 3/3
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - mae: 0.3170 - loss: 0.1511
<keras.src.callbacks.history.History at 0x7f48a3255710>
Naturally, you could just skip passing a loss function in compile()
, and instead do
everything manually in train_step
. Likewise for metrics.
Here's a lower-level example, that only uses compile()
to configure the optimizer:
Metric
instances to track our loss and a MAE score (in __init__()
).train_step()
that updates the state of these metrics
(by calling update_state()
on them), then query them (via result()
) to return their current average value,
to be displayed by the progress bar and to be pass to any callback.reset_states()
on our metrics between each epoch! Otherwise
calling result()
would return an average since the start of training, whereas we usually work
with per-epoch averages. Thankfully, the framework can do that for us: just list any metric
you want to reset in the metrics
property of the model. The model will call reset_states()
on any object listed here at the beginning of each fit()
epoch or at the beginning of a call to
evaluate()
.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 2ms/step - loss: 0.6173 - mae: 0.6607
Epoch 2/5
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2340 - mae: 0.3883
Epoch 3/5
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1922 - mae: 0.3517
Epoch 4/5
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1802 - mae: 0.3411
Epoch 5/5
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1862 - mae: 0.3505
<keras.src.callbacks.history.History at 0x7f48975ccbd0>
sample_weight
& class_weight
You may have noticed that our first basic example didn't make any mention of sample
weighting. If you want to support the fit()
arguments sample_weight
and
class_weight
, you'd simply do the following:
sample_weight
from the data
argumentcompute_loss
& update_state
(of course, you could also just apply
it manually if you don't rely on compile()
for losses & metrics)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 2ms/step - mae: 0.3216 - loss: 0.0827
Epoch 2/3
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - mae: 0.3156 - loss: 0.0803
Epoch 3/3
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - mae: 0.3085 - loss: 0.0760
<keras.src.callbacks.history.History at 0x7f48975d7bd0>
What if you want to do the same for calls to model.evaluate()
? Then you would
override test_step
in exactly the same way. Here's what it looks like:
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)
1/32 [37m━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - mae: 0.8706 - loss: 0.9344
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - mae: 0.8959 - loss: 0.9952
[1.0077838897705078, 0.8984771370887756]
Let's walk through an end-to-end example that leverages everything you just learned.
Let's consider:
# 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",
)
Here's a feature-complete GAN class, overriding compile()
to use its own signature,
and implementing the entire GAN algorithm in 17 lines in train_step
:
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) or isinstance(real_images, list):
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(),
}
Let's test-drive it:
# 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 ━━━━━━━━━━━━━━━━━━━━ 394s 360ms/step - d_loss: 0.2436 - g_loss: 4.7259
<keras.src.callbacks.history.History at 0x7f489760a490>
The ideas behind deep learning are simple, so why should their implementation be painful?