Author: fchollet
Date created: 2020/04/01
Last modified: 2020/04/28
Description: Everything you need to know to use Keras to build real-world machine learning solutions.
import numpy as np
import tensorflow as tf
import keras
Are you a machine learning engineer looking to use Keras to ship deep-learning powered features in real products? This guide will serve as your first introduction to core Keras API concepts.
In this guide, you will learn how to:
tf.data.Dataset
objects).fit()
method, while being
mindful of checkpointing, metrics monitoring, and fault tolerance.fit()
does, for instance to build a GAN.At the end of this guide, you will get pointers to end-to-end examples to solidify these concepts:
Neural networks don't process raw data, like text files, encoded JPEG image files, or CSV files. They process vectorized & standardized representations.
Let's start with data loading.
Keras models accept three types of inputs:
Dataset
objects. This is a
high-performance option that is more suitable for datasets that do not fit in memory
and that are streamed from disk or from a distributed filesystem.keras.utils.Sequence
class).Before you start training a model, you will need to make your data available as one of
these formats. If you have a large dataset and you are training on GPU(s), consider
using Dataset
objects, since they will take care of performance-critical details,
such as:
Keras features a range of utilities to help you turn raw data on disk into a Dataset
:
keras.utils.image_dataset_from_directory
turns image files sorted into
class-specific folders into a labeled dataset of image tensors.keras.utils.text_dataset_from_directory
does the same for text files.In addition, the TensorFlow tf.data
includes other similar utilities, such as
tf.data.experimental.make_csv_dataset
to load structured data from CSV files.
Example: obtaining a labeled dataset from image files on disk
Supposed you have image files sorted by class in different folders, like this:
main_directory/
...class_a/
......a_image_1.jpg
......a_image_2.jpg
...class_b/
......b_image_1.jpg
......b_image_2.jpg
Then you can do:
# Create a dataset.
dataset = keras.utils.image_dataset_from_directory(
'path/to/main_directory', batch_size=64, image_size=(200, 200))
# For demonstration, iterate over the batches yielded by the dataset.
for data, labels in dataset:
print(data.shape) # (64, 200, 200, 3)
print(data.dtype) # float32
print(labels.shape) # (64,)
print(labels.dtype) # int32
The label of a sample is the rank of its folder in alphanumeric order. Naturally, this
can also be configured explicitly by passing, e.g.
class_names=['class_a', 'class_b']
, in which cases label 0
will be class_a
and
1
will be class_b
.
Example: obtaining a labeled dataset from text files on disk
Likewise for text: if you have .txt
documents sorted by class in different folders,
you can do:
dataset = keras.utils.text_dataset_from_directory(
'path/to/main_directory', batch_size=64)
# For demonstration, iterate over the batches yielded by the dataset.
for data, labels in dataset:
print(data.shape) # (64,)
print(data.dtype) # string
print(labels.shape) # (64,)
print(labels.dtype) # int32
Once your data is in the form of string/int/float NumPy arrays, or a Dataset
object
(or Python generator) that yields batches of string/int/float tensors,
it is time to preprocess the data. This can mean:
[0, 1]
range.In general, you should seek to do data preprocessing as part of your model as much as possible, not via an external data preprocessing pipeline. That's because external data preprocessing makes your models less portable when it's time to use them in production. Consider a model that processes text: it uses a specific tokenization algorithm and a specific vocabulary index. When you want to ship your model to a mobile app or a JavaScript app, you will need to recreate the exact same preprocessing setup in the target language. This can get very tricky: any small discrepancy between the original pipeline and the one you recreate has the potential to completely invalidate your model, or at least severely degrade its performance.
It would be much easier to be able to simply export an end-to-end model that already
includes preprocessing. The ideal model should expect as input something as close as
possible to raw data: an image model should expect RGB pixel values in the [0, 255]
range, and a text model should accept strings of utf-8
characters. That way, the
consumer of the exported model doesn't have
to know about the preprocessing pipeline.
In Keras, you do in-model data preprocessing via preprocessing layers. This includes:
TextVectorization
layerNormalization
layerThe key advantage of using Keras preprocessing layers is that they can be included directly into your model, either during training or after training, which makes your models portable.
Some preprocessing layers have a state:
TextVectorization
holds an index mapping words or tokens to integer indicesNormalization
holds the mean and variance of your featuresThe state of a preprocessing layer is obtained by calling layer.adapt(data)
on a
sample of the training data (or all of it).
Example: turning strings into sequences of integer word indices
from tensorflow.keras.layers import TextVectorization
# Example training data, of dtype `string`.
training_data = np.array([["This is the 1st sample."], ["And here's the 2nd sample."]])
# Create a TextVectorization layer instance. It can be configured to either
# return integer token indices, or a dense token representation (e.g. multi-hot
# or TF-IDF). The text standardization and text splitting algorithms are fully
# configurable.
vectorizer = TextVectorization(output_mode="int")
# Calling `adapt` on an array or dataset makes the layer generate a vocabulary
# index for the data, which can then be reused when seeing new data.
vectorizer.adapt(training_data)
# After calling adapt, the layer is able to encode any n-gram it has seen before
# in the `adapt()` data. Unknown n-grams are encoded via an "out-of-vocabulary"
# token.
integer_data = vectorizer(training_data)
print(integer_data)
tf.Tensor(
[[4 5 2 9 3]
[7 6 2 8 3]], shape=(2, 5), dtype=int64)
Example: turning strings into sequences of one-hot encoded bigrams
from tensorflow.keras.layers import TextVectorization
# Example training data, of dtype `string`.
training_data = np.array([["This is the 1st sample."], ["And here's the 2nd sample."]])
# Create a TextVectorization layer instance. It can be configured to either
# return integer token indices, or a dense token representation (e.g. multi-hot
# or TF-IDF). The text standardization and text splitting algorithms are fully
# configurable.
vectorizer = TextVectorization(output_mode="binary", ngrams=2)
# Calling `adapt` on an array or dataset makes the layer generate a vocabulary
# index for the data, which can then be reused when seeing new data.
vectorizer.adapt(training_data)
# After calling adapt, the layer is able to encode any n-gram it has seen before
# in the `adapt()` data. Unknown n-grams are encoded via an "out-of-vocabulary"
# token.
integer_data = vectorizer(training_data)
print(integer_data)
tf.Tensor(
[[0. 1. 1. 1. 1. 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1.]
[0. 1. 1. 0. 0. 1. 0. 0. 0. 1. 1. 1. 1. 1. 1. 0. 0.]], shape=(2, 17), dtype=float32)
Example: normalizing features
from tensorflow.keras.layers import Normalization
# Example image data, with values in the [0, 255] range
training_data = np.random.randint(0, 256, size=(64, 200, 200, 3)).astype("float32")
normalizer = Normalization(axis=-1)
normalizer.adapt(training_data)
normalized_data = normalizer(training_data)
print("var: %.4f" % np.var(normalized_data))
print("mean: %.4f" % np.mean(normalized_data))
var: 1.0005
mean: 0.0000
Example: rescaling & center-cropping images
Both the Rescaling
layer and the CenterCrop
layer are stateless, so it isn't
necessary to call adapt()
in this case.
from tensorflow.keras.layers import CenterCrop
from tensorflow.keras.layers import Rescaling
# Example image data, with values in the [0, 255] range
training_data = np.random.randint(0, 256, size=(64, 200, 200, 3)).astype("float32")
cropper = CenterCrop(height=150, width=150)
scaler = Rescaling(scale=1.0 / 255)
output_data = scaler(cropper(training_data))
print("shape:", output_data.shape)
print("min:", np.min(output_data))
print("max:", np.max(output_data))
shape: (64, 150, 150, 3)
min: 0.0
max: 1.0
A "layer" is a simple input-output transformation (such as the scaling & center-cropping transformations above). For instance, here's a linear projection layer that maps its inputs to a 16-dimensional feature space:
dense = keras.layers.Dense(units=16)
A "model" is a directed acyclic graph of layers. You can think of a model as a "bigger layer" that encompasses multiple sublayers and that can be trained via exposure to data.
The most common and most powerful way to build Keras models is the Functional API. To
build models with the Functional API, you start by specifying the shape (and
optionally the dtype) of your inputs. If any dimension of your input can vary, you can
specify it as None
. For instance, an input for 200x200 RGB image would have shape
(200, 200, 3)
, but an input for RGB images of any size would have shape (None,
None, 3)
.
# Let's say we expect our inputs to be RGB images of arbitrary size
inputs = keras.Input(shape=(None, None, 3))
After defining your input(s), you can chain layer transformations on top of your inputs, until your final output:
from tensorflow.keras import layers
# Center-crop images to 150x150
x = CenterCrop(height=150, width=150)(inputs)
# Rescale images to [0, 1]
x = Rescaling(scale=1.0 / 255)(x)
# Apply some convolution and pooling layers
x = layers.Conv2D(filters=32, kernel_size=(3, 3), activation="relu")(x)
x = layers.MaxPooling2D(pool_size=(3, 3))(x)
x = layers.Conv2D(filters=32, kernel_size=(3, 3), activation="relu")(x)
x = layers.MaxPooling2D(pool_size=(3, 3))(x)
x = layers.Conv2D(filters=32, kernel_size=(3, 3), activation="relu")(x)
# Apply global average pooling to get flat feature vectors
x = layers.GlobalAveragePooling2D()(x)
# Add a dense classifier on top
num_classes = 10
outputs = layers.Dense(num_classes, activation="softmax")(x)
Once you have defined the directed acyclic graph of layers that turns your input(s) into
your outputs, instantiate a Model
object:
model = keras.Model(inputs=inputs, outputs=outputs)
This model behaves basically like a bigger layer. You can call it on batches of data, like this:
data = np.random.randint(0, 256, size=(64, 200, 200, 3)).astype("float32")
processed_data = model(data)
print(processed_data.shape)
(64, 10)
You can print a summary of how your data gets transformed at each stage of the model. This is useful for debugging.
Note that the output shape displayed for each layer includes the batch size. Here the batch size is None, which indicates our model can process batches of any size.
model.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, None, None, 3)] 0
center_crop_1 (CenterCrop) (None, 150, 150, 3) 0
rescaling_1 (Rescaling) (None, 150, 150, 3) 0
conv2d (Conv2D) (None, 148, 148, 32) 896
max_pooling2d (MaxPooling2 (None, 49, 49, 32) 0
D)
conv2d_1 (Conv2D) (None, 47, 47, 32) 9248
max_pooling2d_1 (MaxPoolin (None, 15, 15, 32) 0
g2D)
conv2d_2 (Conv2D) (None, 13, 13, 32) 9248
global_average_pooling2d ( (None, 32) 0
GlobalAveragePooling2D)
dense (Dense) (None, 10) 330
=================================================================
Total params: 19722 (77.04 KB)
Trainable params: 19722 (77.04 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
The Functional API also makes it easy to build models that have multiple inputs (for instance, an image and its metadata) or multiple outputs (for instance, predicting the class of the image and the likelihood that a user will click on it). For a deeper dive into what you can do, see our guide to the Functional API.
fit()
At this point, you know:
tf.data.Dataset
object)The next step is to train your model on your data. The Model
class features a
built-in training loop, the fit()
method. It accepts Dataset
objects, Python
generators that yield batches of data, or NumPy arrays.
Before you can call fit()
, you need to specify an optimizer and a loss function (we
assume you are already familiar with these concepts). This is the compile()
step:
model.compile(optimizer=keras.optimizers.RMSprop(learning_rate=1e-3),
loss=keras.losses.CategoricalCrossentropy())
Loss and optimizer can be specified via their string identifiers (in this case their default constructor argument values are used):
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
Once your model is compiled, you can start "fitting" the model to the data. Here's what fitting a model looks like with NumPy data:
model.fit(numpy_array_of_samples, numpy_array_of_labels,
batch_size=32, epochs=10)
Besides the data, you have to specify two key parameters: the batch_size
and
the number of epochs (iterations on the data). Here our data will get sliced on batches
of 32 samples, and the model will iterate 10 times over the data during training.
Here's what fitting a model looks like with a dataset:
model.fit(dataset_of_samples_and_labels, epochs=10)
Since the data yielded by a dataset is expected to be already batched, you don't need to specify the batch size here.
Let's look at it in practice with a toy example model that learns to classify MNIST digits:
# Get the data as Numpy arrays
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
# Build a simple model
inputs = keras.Input(shape=(28, 28))
x = layers.Rescaling(1.0 / 255)(inputs)
x = layers.Flatten()(x)
x = layers.Dense(128, activation="relu")(x)
x = layers.Dense(128, activation="relu")(x)
outputs = layers.Dense(10, activation="softmax")(x)
model = keras.Model(inputs, outputs)
model.summary()
# Compile the model
model.compile(optimizer="adam", loss="sparse_categorical_crossentropy")
# Train the model for 1 epoch from Numpy data
batch_size = 64
print("Fit on NumPy data")
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=1)
# Train the model for 1 epoch using a dataset
dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size)
print("Fit on Dataset")
history = model.fit(dataset, epochs=1)
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 28, 28)] 0
rescaling_2 (Rescaling) (None, 28, 28) 0
flatten (Flatten) (None, 784) 0
dense_1 (Dense) (None, 128) 100480
dense_2 (Dense) (None, 128) 16512
dense_3 (Dense) (None, 10) 1290
=================================================================
Total params: 118282 (462.04 KB)
Trainable params: 118282 (462.04 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
Fit on NumPy data
938/938 [==============================] - 2s 2ms/step - loss: 0.2642
Fit on Dataset
938/938 [==============================] - 2s 2ms/step - loss: 0.1157
The fit()
call returns a "history" object which records what happened over the course
of training. The history.history
dict contains per-epoch timeseries of metrics
values (here we have only one metric, the loss, and one epoch, so we only get a single
scalar):
print(history.history)
{'loss': [0.11566586047410965]}
For a detailed overview of how to use fit()
, see the
guide to training & evaluation with the built-in Keras methods.
As you're training a model, you want to keep track of metrics such as classification accuracy, precision, recall, AUC, etc. Besides, you want to monitor these metrics not only on the training data, but also on a validation set.
Monitoring metrics
You can pass a list of metric objects to compile()
, like this:
model.compile(
optimizer="adam",
loss="sparse_categorical_crossentropy",
metrics=[keras.metrics.SparseCategoricalAccuracy(name="acc")],
)
history = model.fit(dataset, epochs=1)
938/938 [==============================] - 2s 2ms/step - loss: 0.0809 - acc: 0.9756
Passing validation data to fit()
You can pass validation data to fit()
to monitor your validation loss & validation
metrics. Validation metrics get reported at the end of each epoch.
val_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch_size)
history = model.fit(dataset, epochs=1, validation_data=val_dataset)
938/938 [==============================] - 2s 2ms/step - loss: 0.0571 - acc: 0.9830 - val_loss: 0.0963 - val_acc: 0.9704
If training goes on for more than a few minutes, it's important to save your model at regular intervals during training. You can then use your saved models to restart training in case your training process crashes (this is important for multi-worker distributed training, since with many workers at least one of them is bound to fail at some point).
An important feature of Keras is callbacks, configured in fit()
. Callbacks are
objects that get called by the model at different point during training, in particular:
Callbacks are a way to make model trainable entirely scriptable.
You can use callbacks to periodically save your model. Here's a simple example: a
ModelCheckpoint
callback
configured to save the model at the end of every epoch. The filename will include the
current epoch.
callbacks = [
keras.callbacks.ModelCheckpoint(
filepath='path/to/my/model_{epoch}',
save_freq='epoch')
]
model.fit(dataset, epochs=2, callbacks=callbacks)
You can also use callbacks to do things like periodically changing the learning of your optimizer, streaming metrics to a Slack bot, sending yourself an email notification when training is complete, etc.
For detailed overview of what callbacks are available and how to write your own, see the callbacks API documentation and the guide to writing custom callbacks.
Staring at the Keras progress bar isn't the most ergonomic way to monitor how your loss and metrics are evolving over time. There's a better solution: TensorBoard, a web application that can display real-time graphs of your metrics (and more).
To use TensorBoard with fit()
, simply pass a keras.callbacks.TensorBoard
callback
specifying the directory where to store TensorBoard logs:
callbacks = [
keras.callbacks.TensorBoard(log_dir='./logs')
]
model.fit(dataset, epochs=2, callbacks=callbacks)
You can then launch a TensorBoard instance that you can open in your browser to monitor the logs getting written to this location:
tensorboard --logdir=./logs
What's more, you can launch an in-line TensorBoard tab when training models in Jupyter / Colab notebooks. Here's more information.
fit()
: evaluating test performance & generating predictions on new dataOnce you have a trained model, you can evaluate its loss and metrics on new data via
evaluate()
:
loss, acc = model.evaluate(val_dataset) # returns loss and metrics
print("loss: %.2f" % loss)
print("acc: %.2f" % acc)
157/157 [==============================] - 0s 1ms/step - loss: 0.0963 - acc: 0.9704
loss: 0.10
acc: 0.97
You can also generate NumPy arrays of predictions (the activations of the output
layer(s) in the model) via predict()
:
predictions = model.predict(val_dataset)
print(predictions.shape)
157/157 [==============================] - 0s 1ms/step
(10000, 10)
fit()
with a custom training stepBy default, fit()
is configured for supervised learning. If you need a different
kind of training loop (for instance, a GAN training loop), you
can provide your own implementation of the Model.train_step()
method. This is the
method that is repeatedly called during fit()
.
Metrics, callbacks, etc. will work as usual.
Here's a simple example that reimplements what fit()
normally does:
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(zip(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}
# 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=[...])
# Just use `fit` as usual
model.fit(dataset, epochs=3, callbacks=...)
For a detailed overview of how you customize the built-in training & evaluation loops,
see the guide:
"Customizing what happens in fit()
".
If you write custom training steps or custom layers, you will need to debug them. The debugging experience is an integral part of a framework: with Keras, the debugging workflow is designed with the user in mind.
By default, your Keras models are compiled to highly-optimized computation graphs that
deliver fast execution times. That means that the Python code you write (e.g. in a
custom train_step
) is not the code you are actually executing. This introduces a
layer of indirection that can make debugging hard.
Debugging is best done step by step. You want to be able to sprinkle your code with
print()
statement to see what your data looks like after every operation, you want
to be able to use pdb
. You can achieve this by running your model eagerly. With
eager execution, the Python code you write is the code that gets executed.
Simply pass run_eagerly=True
to compile()
:
model.compile(optimizer='adam', loss='mse', run_eagerly=True)
Of course, the downside is that it makes your model significantly slower. Make sure to switch it back off to get the benefits of compiled computation graphs once you are done debugging!
In general, you will use run_eagerly=True
every time you need to debug what's
happening inside your fit()
call.
Keras has built-in industry-strength support for multi-GPU training and distributed
multi-worker training, via the tf.distribute
API.
If you have multiple GPUs on your machine, you can train your model on all of them by:
tf.distribute.MirroredStrategy
objectfit()
and evaluate()
on a dataset as usual# Create a MirroredStrategy.
strategy = tf.distribute.MirroredStrategy()
# Open a strategy scope.
with strategy.scope():
# Everything that creates variables should be under the strategy scope.
# In general this is only model construction & `compile()`.
model = Model(...)
model.compile(...)
# Train the model on all available devices.
train_dataset, val_dataset, test_dataset = get_dataset()
model.fit(train_dataset, epochs=2, validation_data=val_dataset)
# Test the model on all available devices.
model.evaluate(test_dataset)
For a detailed introduction to multi-GPU & distributed training, see this guide.
You've learned about preprocessing, and you've seen example where we put image
preprocessing layers (CenterCrop
and Rescaling
) directly inside our model.
Having preprocessing happen as part of the model during training
is great if you want to do on-device preprocessing, for instance, GPU-accelerated
feature normalization or image augmentation. But there are kinds of preprocessing that
are not suited to this setup: in particular, text preprocessing with the
TextVectorization
layer. Due to its sequential nature and due to the fact that it
can only run on CPU, it's often a good idea to do asynchronous preprocessing.
With asynchronous preprocessing, your preprocessing operations will run on CPU, and the preprocessed samples will be buffered into a queue while your GPU is busy with previous batch of data. The next batch of preprocessed samples will then be fetched from the queue to the GPU memory right before the GPU becomes available again (prefetching). This ensures that preprocessing will not be blocking and that your GPU can run at full utilization.
To do asynchronous preprocessing, simply use dataset.map
to inject a preprocessing
operation into your data pipeline:
# Example training data, of dtype `string`.
samples = np.array([["This is the 1st sample."], ["And here's the 2nd sample."]])
labels = [[0], [1]]
# Prepare a TextVectorization layer.
vectorizer = TextVectorization(output_mode="int")
vectorizer.adapt(samples)
# Asynchronous preprocessing: the text vectorization is part of the tf.data pipeline.
# First, create a dataset
dataset = tf.data.Dataset.from_tensor_slices((samples, labels)).batch(2)
# Apply text vectorization to the samples
dataset = dataset.map(lambda x, y: (vectorizer(x), y))
# Prefetch with a buffer size of 2 batches
dataset = dataset.prefetch(2)
# Our model should expect sequences of integers as inputs
inputs = keras.Input(shape=(None,), dtype="int64")
x = layers.Embedding(input_dim=10, output_dim=32)(inputs)
outputs = layers.Dense(1)(x)
model = keras.Model(inputs, outputs)
model.compile(optimizer="adam", loss="mse", run_eagerly=True)
model.fit(dataset)
1/1 [==============================] - 0s 41ms/step - loss: 0.5348
<keras.src.callbacks.History at 0x7f6c104565d0>
Compare this to doing text vectorization as part of the model:
# Our dataset will yield samples that are strings
dataset = tf.data.Dataset.from_tensor_slices((samples, labels)).batch(2)
# Our model should expect strings as inputs
inputs = keras.Input(shape=(1,), dtype="string")
x = vectorizer(inputs)
x = layers.Embedding(input_dim=10, output_dim=32)(x)
outputs = layers.Dense(1)(x)
model = keras.Model(inputs, outputs)
model.compile(optimizer="adam", loss="mse", run_eagerly=True)
model.fit(dataset)
1/1 [==============================] - 0s 24ms/step - loss: 0.5121
<keras.src.callbacks.History at 0x7f6c104bfd10>
When training text models on CPU, you will generally not see any performance difference between the two setups. When training on GPU, however, doing asynchronous buffered preprocessing on the host CPU while the GPU is running the model itself can result in a significant speedup.
After training, if you want to export an end-to-end model that includes the preprocessing
layer(s), this is easy to do, since TextVectorization
is a layer:
inputs = keras.Input(shape=(1,), dtype='string')
x = vectorizer(inputs)
outputs = trained_model(x)
end_to_end_model = keras.Model(inputs, outputs)
Once you have a working model, you're going to want to optimize its configuration -- architecture choices, layer sizes, etc. Human intuition can only go so far, so you'll want to leverage a systematic approach: hyperparameter search.
You can use
KerasTuner to find
the best hyperparameter for your Keras models. It's as easy as calling fit()
.
Here how it works.
First, place your model definition in a function, that takes a single hp
argument.
Inside this function, replace any value you want to tune with a call to hyperparameter
sampling methods, e.g. hp.Int()
or hp.Choice()
:
def build_model(hp):
inputs = keras.Input(shape=(784,))
x = layers.Dense(
units=hp.Int('units', min_value=32, max_value=512, step=32),
activation='relu')(inputs)
outputs = layers.Dense(10, activation='softmax')(x)
model = keras.Model(inputs, outputs)
model.compile(
optimizer=keras.optimizers.Adam(
hp.Choice('learning_rate',
values=[1e-2, 1e-3, 1e-4])),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
The function should return a compiled model.
Next, instantiate a tuner object specifying your optimization objective and other search parameters:
import keras_tuner
tuner = keras_tuner.tuners.Hyperband(
build_model,
objective='val_loss',
max_epochs=100,
max_trials=200,
executions_per_trial=2,
directory='my_dir')
Finally, start the search with the search()
method, which takes the same arguments as
Model.fit()
:
tuner.search(dataset, validation_data=val_dataset)
When search is over, you can retrieve the best model(s):
models = tuner.get_best_models(num_models=2)
Or print a summary of the results:
tuner.results_summary()
To familiarize yourself with the concepts in this introduction, see the following end-to-end examples:
fit()
and evaluate()
.