Guide to Keras Basics

Keras is a high-level API to build and train deep learning models. It’s used for fast prototyping, advanced research, and production, with three key advantages:

• User friendly – Keras has a simple, consistent interface optimized for common use cases. It provides clear and actionable feedback for user errors.
• Modular and composable – Keras models are made by connecting configurable building blocks together, with few restrictions.
• Easy to extend – Write custom building blocks to express new ideas for research. Create new layers, loss functions, and develop state-of-the-art models.

Import keras

To get started, load the keras library:

library(keras)

Build a simple model

Sequential model

In Keras, you assemble layers to build models. A model is (usually) a graph of layers. The most common type of model is a stack of layers: the sequential model.

To build a simple, fully-connected network (i.e., a multi-layer perceptron):

model <- keras_model_sequential()

model %>%

  # Adds a densely-connected layer with 64 units to the model:
  layer_dense(units = 64, activation = 'relu') %>%

  # Add another:
  layer_dense(units = 64, activation = 'relu') %>%

  # Add a softmax layer with 10 output units:
  layer_dense(units = 10, activation = 'softmax')

Configure the layers

There are many layers available with some common constructor parameters:

• activation: Set the activation function for the layer. By default, no activation is applied.
• kernel_initializer and bias_initializer: The initialization schemes that create the layer’s weights (kernel and bias). This defaults to the Glorot uniform initializer.
• kernel_regularizer and bias_regularizer: The regularization schemes that apply to the layer’s weights (kernel and bias), such as L1 or L2 regularization. By default, no regularization is applied.

The following instantiates dense layers using constructor arguments:

# Create a sigmoid layer:
layer_dense(units = 64, activation ='sigmoid')

<keras.src.layers.core.dense.Dense object at 0x7f954cab7be0>

# A linear layer with L1 regularization of factor 0.01 applied to the kernel matrix:
layer_dense(units = 64, kernel_regularizer = regularizer_l1(0.01))

Warning in keras$regularizers$l1(l = l): partial argument match of 'l' to
'l1'

<keras.src.layers.core.dense.Dense object at 0x7f954cb74100>

# A linear layer with L2 regularization of factor 0.01 applied to the bias vector:
layer_dense(units = 64, bias_regularizer = regularizer_l2(0.01))

Warning in keras$regularizers$l2(l = l): partial argument match of 'l' to
'l2'

<keras.src.layers.core.dense.Dense object at 0x7f954cb744f0>

# A linear layer with a kernel initialized to a random orthogonal matrix:
layer_dense(units = 64, kernel_initializer = 'orthogonal')

<keras.src.layers.core.dense.Dense object at 0x7f954cb74070>

# A linear layer with a bias vector initialized to 2.0:
layer_dense(units = 64, bias_initializer = initializer_constant(2.0))

<keras.src.layers.core.dense.Dense object at 0x7f954cb74c40>

Train and evaluate

Set up training

After the model is constructed, configure its learning process by calling the compile method:

model %>% compile(
  optimizer = 'adam',
  loss = 'categorical_crossentropy',
  metrics = list('accuracy')
)

compile takes three important arguments:

• optimizer: This object specifies the training procedure. Commonly used optimizers are e.g.
  adam, rmsprop, or sgd.
• loss: The function to minimize during optimization. Common choices include mean square error (mse), categorical_crossentropy, and binary_crossentropy.
• metrics: Used to monitor training. In classification, this usually is accuracy.

The following shows a few examples of configuring a model for training:

# Configure a model for mean-squared error regression.
model %>% compile(
  optimizer = 'adam',
  loss = 'mse',           # mean squared error
  metrics = list('mae')   # mean absolute error
)

# Configure a model for categorical classification.
model %>% compile(
  optimizer = optimizer_rmsprop(learning_rate = 0.01),
  loss = "categorical_crossentropy",
  metrics = list("categorical_accuracy")
)

Input data

You can train keras models directly on R matrices and arrays (possibly created from R data.frames). A model is fit to the training data using the fit method:

data <- matrix(rnorm(1000 * 32), nrow = 1000, ncol = 32)
labels <- matrix(rnorm(1000 * 10), nrow = 1000, ncol = 10)

model %>% fit(
  data,
  labels,
  epochs = 10,
  batch_size = 32
)

Epoch 1/10
32/32 - 1s - loss: -2.6513e-01 - categorical_accuracy: 0.0970 - 802ms/epoch - 25ms/step
Epoch 2/10
32/32 - 0s - loss: -3.8759e+00 - categorical_accuracy: 0.1110 - 79ms/epoch - 2ms/step
Epoch 3/10
32/32 - 0s - loss: -1.8322e+01 - categorical_accuracy: 0.1020 - 55ms/epoch - 2ms/step
Epoch 4/10
32/32 - 0s - loss: -5.0000e+01 - categorical_accuracy: 0.1070 - 59ms/epoch - 2ms/step
Epoch 5/10
32/32 - 0s - loss: -1.1058e+02 - categorical_accuracy: 0.0810 - 59ms/epoch - 2ms/step
Epoch 6/10
32/32 - 0s - loss: -1.6624e+02 - categorical_accuracy: 0.1150 - 59ms/epoch - 2ms/step
Epoch 7/10
32/32 - 0s - loss: -2.8676e+02 - categorical_accuracy: 0.1070 - 55ms/epoch - 2ms/step
Epoch 8/10
32/32 - 0s - loss: -4.4149e+02 - categorical_accuracy: 0.1140 - 57ms/epoch - 2ms/step
Epoch 9/10
32/32 - 0s - loss: -6.6879e+02 - categorical_accuracy: 0.1170 - 64ms/epoch - 2ms/step
Epoch 10/10
32/32 - 0s - loss: -8.8606e+02 - categorical_accuracy: 0.1050 - 61ms/epoch - 2ms/step

fit takes three important arguments:

• epochs: Training is structured into epochs. An epoch is one iteration over the entire input data (this is done in smaller batches).
• batch_size: When passed matrix or array data, the model slices the data into smaller batches and iterates over these batches during training. This integer specifies the size of each batch. Be aware that the last batch may be smaller if the total number of samples is not divisible by the batch size.
• validation_data: When prototyping a model, you want to easily monitor its performance on some validation data. Passing this argument — a list of inputs and labels — allows the model to display the loss and metrics in inference mode for the passed data, at the end of each epoch.

Here’s an example using validation_data:

data <- matrix(rnorm(1000 * 32), nrow = 1000, ncol = 32)
labels <- matrix(rnorm(1000 * 10), nrow = 1000, ncol = 10)

val_data <- matrix(rnorm(1000 * 32), nrow = 100, ncol = 32)

Warning in matrix(rnorm(1000 * 32), nrow = 100, ncol = 32): data length
differs from size of matrix: [32000 != 100 x 32]

val_labels <- matrix(rnorm(100 * 10), nrow = 100, ncol = 10)

model %>% fit(
  data,
  labels,
  epochs = 10,
  batch_size = 32,
  validation_data = list(val_data, val_labels)
)

Epoch 1/10
32/32 - 0s - loss: 377.5257 - categorical_accuracy: 0.1010 - val_loss: -9.1629e+02 - val_categorical_accuracy: 0.1500 - 184ms/epoch - 6ms/step
Epoch 2/10
32/32 - 0s - loss: 268.6779 - categorical_accuracy: 0.1070 - val_loss: -9.0091e+02 - val_categorical_accuracy: 0.1400 - 80ms/epoch - 2ms/step
Epoch 3/10
32/32 - 0s - loss: 17.4030 - categorical_accuracy: 0.1140 - val_loss: -1.0150e+03 - val_categorical_accuracy: 0.1400 - 76ms/epoch - 2ms/step
Epoch 4/10
32/32 - 0s - loss: -5.5530e+01 - categorical_accuracy: 0.1070 - val_loss: -1.1427e+03 - val_categorical_accuracy: 0.1100 - 74ms/epoch - 2ms/step
Epoch 5/10
32/32 - 0s - loss: 49.4249 - categorical_accuracy: 0.0810 - val_loss: -1.1145e+03 - val_categorical_accuracy: 0.1500 - 74ms/epoch - 2ms/step
Epoch 6/10
32/32 - 0s - loss: -8.4104e+01 - categorical_accuracy: 0.1090 - val_loss: -1.6088e+03 - val_categorical_accuracy: 0.0800 - 75ms/epoch - 2ms/step
Epoch 7/10
32/32 - 0s - loss: -2.5985e+02 - categorical_accuracy: 0.0960 - val_loss: -1.9842e+03 - val_categorical_accuracy: 0.1100 - 82ms/epoch - 3ms/step
Epoch 8/10
32/32 - 0s - loss: -6.4850e+02 - categorical_accuracy: 0.0980 - val_loss: -1.8305e+03 - val_categorical_accuracy: 0.0900 - 86ms/epoch - 3ms/step
Epoch 9/10
32/32 - 0s - loss: -6.2961e+02 - categorical_accuracy: 0.1050 - val_loss: -3.0540e+03 - val_categorical_accuracy: 0.1200 - 75ms/epoch - 2ms/step
Epoch 10/10
32/32 - 0s - loss: -1.4109e+03 - categorical_accuracy: 0.0980 - val_loss: -4.7981e+03 - val_categorical_accuracy: 0.0800 - 78ms/epoch - 2ms/step

Evaluate and predict

Same as fit, the evaluate and predict methods can use raw R data as well as a dataset.

To evaluate the inference-mode loss and metrics for the data provided:

model %>% evaluate(test_data, test_labels, batch_size = 32)

model %>% evaluate(test_dataset, steps = 30)

And to predict the output of the last layer in inference for the data provided, again as R data as well as a dataset:

model %>% predict(test_data, batch_size = 32)

model %>% predict(test_dataset, steps = 30)

Build advanced models

Functional API

The sequential model is a simple stack of layers that cannot represent arbitrary models. Use the Keras functional API to build complex model topologies such as:

• multi-input models,
• multi-output models,
• models with shared layers (the same layer called several times),
• models with non-sequential data flows (e.g., residual connections).

Building a model with the functional API works like this:

1. A layer instance is callable and returns a tensor.
2. Input tensors and output tensors are used to define a keras_model instance.
3. This model is trained just like the sequential model.

The following example uses the functional API to build a simple, fully-connected network:

inputs <- layer_input(shape = (32))  # Returns a placeholder tensor

predictions <- inputs %>%
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dense(units = 10, activation = 'softmax')

# Instantiate the model given inputs and outputs.
model <- keras_model(inputs = inputs, outputs = predictions)

# The compile step specifies the training configuration.
model %>% compile(
  optimizer = optimizer_rmsprop(lr = 0.001),
  loss = 'categorical_crossentropy',
  metrics = list('accuracy')
)

# Trains for 5 epochs
model %>% fit(
  data,
  labels,
  batch_size = 32,
  epochs = 5
)

Epoch 1/5
32/32 - 1s - loss: 0.4507 - accuracy: 0.0960 - 586ms/epoch - 18ms/step
Epoch 2/5
32/32 - 0s - loss: 0.1948 - accuracy: 0.1240 - 54ms/epoch - 2ms/step
Epoch 3/5
32/32 - 0s - loss: -3.9052e-03 - accuracy: 0.1500 - 55ms/epoch - 2ms/step
Epoch 4/5
32/32 - 0s - loss: -2.1561e-01 - accuracy: 0.1430 - 126ms/epoch - 4ms/step
Epoch 5/5
32/32 - 0s - loss: -4.4740e-01 - accuracy: 0.1530 - 61ms/epoch - 2ms/step

Custom layers

To create a custom Keras layer, you create an R6 class derived from KerasLayer. There are three methods to implement (only one of which, call(), is required for all types of layer):

• build(input_shape): This is where you will define your weights. Note that if your layer doesn’t define trainable weights then you need not implement this method.
• call(x): This is where the layer’s logic lives. Unless you want your layer to support masking, you only have to care about the first argument passed to call: the input tensor.
• compute_output_shape(input_shape): In case your layer modifies the shape of its input, you should specify here the shape transformation logic. This allows Keras to do automatic shape inference. If you don’t modify the shape of the input then you need not implement this method.

Here is an example custom layer that performs a matrix multiplication:

library(keras)

CustomLayer <- R6::R6Class("CustomLayer",

  inherit = KerasLayer,

  public = list(

    output_dim = NULL,

    kernel = NULL,

    initialize = function(output_dim) {
      self$output_dim <- output_dim
    },

    build = function(input_shape) {
      self$kernel <- self$add_weight(
        name = 'kernel',
        shape = list(input_shape[[2]], self$output_dim),
        initializer = initializer_random_normal(),
        trainable = TRUE
      )
    },

    call = function(x, mask = NULL) {
      k_dot(x, self$kernel)
    },

    compute_output_shape = function(input_shape) {
      list(input_shape[[1]], self$output_dim)
    }
  )
)

In order to use the custom layer within a Keras model you also need to create a wrapper function which instantiates the layer using the create_layer() function. For example:

# define layer wrapper function
layer_custom <- function(object, output_dim, name = NULL, trainable = TRUE) {
  create_layer(CustomLayer, object, list(
    output_dim = as.integer(output_dim),
    name = name,
    trainable = trainable
  ))
}

You can now use the layer in a model as usual:

model <- keras_model_sequential()
model %>%
  layer_dense(units = 32, input_shape = c(32,32)) %>%
  layer_custom(output_dim = 32)

Custom models

In addition to creating custom layers, you can also create a custom model. This might be necessary if you wanted to use TensorFlow eager execution in combination with an imperatively written forward pass.

In cases where this is not needed, but flexibility in building the architecture is required, it is recommended to just stick with the functional API.

A custom model is defined by calling keras_model_custom() passing a function that specifies the layers to be created and the operations to be executed on forward pass.

# define a custom model type
my_model_constructor <- new_model_class(
  "MyModel",

  initialize = function(output_dim, ...) {
    super$initialize(...)
    # store our output dim in self until build() is called
    self$output_dim <- output_dim
  },

  build = function(input_shape) {
    # create layers we'll need for the call (this code executes once)
    # note: the layers have to be created on the self object!
    self$dense1 <- layer_dense(units = 64,
                               activation = 'relu',
                               input_shape = input_shape)
    self$dense2 <- layer_dense(units = 64, activation = 'relu')
    self$dense3 <- layer_dense(units = self$output_dim, activation = 'softmax')
  },

  # implement call (this code executes during training & inference)
  call = function(inputs) {
    x <- inputs %>%
      self$dense1() %>%
      self$dense2() %>%
      self$dense3()
    x
  },

  # define a `get_config()` method in custom objects
  # to enable model saving and restoring
  get_config = function() {
    list(output_dim = self$output_dim)
  }
)



model <- my_model_constructor(output_dim = 10)

model %>% compile(
  optimizer = optimizer_rmsprop(learning_rate = 0.001),
  loss = 'categorical_crossentropy',
  metrics = list('accuracy')
)

# Trains for 5 epochs
model %>% fit(
  data,
  labels,
  batch_size = 32,
  epochs = 5
)

Epoch 1/5
32/32 - 1s - loss: 0.4321 - accuracy: 0.0980 - 653ms/epoch - 20ms/step
Epoch 2/5
32/32 - 0s - loss: 0.1700 - accuracy: 0.1190 - 61ms/epoch - 2ms/step
Epoch 3/5
32/32 - 0s - loss: -3.2296e-02 - accuracy: 0.1220 - 58ms/epoch - 2ms/step
Epoch 4/5
32/32 - 0s - loss: -2.6427e-01 - accuracy: 0.1290 - 56ms/epoch - 2ms/step
Epoch 5/5
32/32 - 0s - loss: -5.0211e-01 - accuracy: 0.1330 - 53ms/epoch - 2ms/step

Callbacks

A callback is an object passed to a model to customize and extend its behavior during training. You can write your own custom callback, or use the built-in callbacks that include:

• callback_model_checkpoint: Save checkpoints of your model at regular intervals.
• callback_learning_rate_scheduler: Dynamically change the learning rate.
• callback_early_stopping: Interrupt training when validation performance has stopped improving.
• callbacks_tensorboard: Monitor the model’s behavior using TensorBoard.

To use a callback, pass it to the model’s fit method:

callbacks <- list(
  callback_early_stopping(patience = 2, monitor = 'val_loss'),
  callback_tensorboard(log_dir = './logs')
)

model %>% fit(
  data,
  labels,
  batch_size = 32,
  epochs = 5,
  callbacks = callbacks,
  validation_data = list(val_data, val_labels)
)

Epoch 1/5
32/32 - 0s - loss: -7.4056e-01 - accuracy: 0.1470 - val_loss: -1.5806e+00 - val_accuracy: 0.0800 - 199ms/epoch - 6ms/step
Epoch 2/5
32/32 - 0s - loss: -1.0141e+00 - accuracy: 0.1400 - val_loss: -1.6356e+00 - val_accuracy: 0.1300 - 96ms/epoch - 3ms/step
Epoch 3/5
32/32 - 0s - loss: -1.2859e+00 - accuracy: 0.1340 - val_loss: -1.7233e+00 - val_accuracy: 0.1100 - 94ms/epoch - 3ms/step
Epoch 4/5
32/32 - 0s - loss: -1.5790e+00 - accuracy: 0.1350 - val_loss: -1.6630e+00 - val_accuracy: 0.0800 - 88ms/epoch - 3ms/step
Epoch 5/5
32/32 - 0s - loss: -1.8551e+00 - accuracy: 0.1450 - val_loss: -1.8464e+00 - val_accuracy: 0.0500 - 89ms/epoch - 3ms/step

Save and restore

Weights only

Save and load the weights of a model using save_model_weights_hdf5 and load_model_weights_hdf5, respectively:

# save in SavedModel format
model %>% save_model_weights_tf('my_model/')

# Restore the model's state,
# this requires a model with the same architecture.
model %>% load_model_weights_tf('my_model/')

Configuration only

A model’s configuration can be saved - this serializes the model architecture without any weights. A saved configuration can recreate and initialize the same model, even without the code that defined the original model. Keras supports JSON and YAML serialization formats:

# Serialize a model to JSON format
json_string <- model %>% model_to_json()

# Recreate the model (freshly initialized)
fresh_model <- model_from_json(json_string,
                               custom_objects = list('MyModel' = my_model_constructor))

Entire model

The entire model can be saved to a file that contains the weight values, the model’s configuration, and even the optimizer’s configuration. This allows you to checkpoint a model and resume training later —from the exact same state —without access to the original code.

# Save entire model to the SavedModel format
model %>% save_model_tf('my_model/')

# Recreate the exact same model, including weights and optimizer.
model <- load_model_tf('my_model/')