keras (version 2.13.0)

keras_model_sequential: Keras Model composed of a linear stack of layers

Description

Keras Model composed of a linear stack of layers

Usage

keras_model_sequential(layers = NULL, name = NULL, ...)

Arguments

layers

List of layers to add to the model

name

Name of model

...

Arguments passed on to sequential_model_input_layer

input_shape

an integer vector of dimensions (not including the batch axis), or a tf$TensorShape instance (also not including the batch axis).

batch_size

Optional input batch size (integer or NULL).

dtype

Optional datatype of the input. When not provided, the Keras default float type will be used.

input_tensor

Optional tensor to use as layer input. If set, the layer will use the tf$TypeSpec of this tensor rather than creating a new placeholder tensor.

sparse

Boolean, whether the placeholder created is meant to be sparse. Default to FALSE.

ragged

Boolean, whether the placeholder created is meant to be ragged. In this case, values of 'NULL' in the 'shape' argument represent ragged dimensions. For more information about RaggedTensors, see this guide. Default to FALSE.

type_spec

A tf$TypeSpec object to create Input from. This tf$TypeSpec represents the entire batch. When provided, all other args except name must be NULL.

input_layer_name,name

Optional name of the input layer (string).

See Also

Other model functions: compile.keras.engine.training.Model(), evaluate.keras.engine.training.Model(), evaluate_generator(), fit.keras.engine.training.Model(), fit_generator(), get_config(), get_layer(), keras_model(), multi_gpu_model(), pop_layer(), predict.keras.engine.training.Model(), predict_generator(), predict_on_batch(), predict_proba(), summary.keras.engine.training.Model(), train_on_batch()

Examples

if (FALSE) {

library(keras)

model <- keras_model_sequential()
model %>%
  layer_dense(units = 32, input_shape = c(784)) %>%
  layer_activation('relu') %>%
  layer_dense(units = 10) %>%
  layer_activation('softmax')

model %>% compile(
  optimizer = 'rmsprop',
  loss = 'categorical_crossentropy',
  metrics = c('accuracy')
)

# alternative way to provide input shape
model <- keras_model_sequential(input_shape = c(784)) %>%
  layer_dense(units = 32) %>%
  layer_activation('relu') %>%
  layer_dense(units = 10) %>%
  layer_activation('softmax')

}