keras_model_sequential: Keras Model composed of a linear stack of layers

In dfalbel/keras: R Interface to 'Keras'

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

Note

The first layer passed to a Sequential model should have a defined input shape. What that means is that it should have received an input_shape or batch_input_shape argument, or for some type of layers (recurrent, Dense...) an input_dim argument.

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

## Not run: 
 
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 
  optimizer = 'rmsprop',
  loss = 'categorical_crossentropy',
  metrics = c('accuracy')
)

## End(Not run)

dfalbel/keras documentation built on Nov. 27, 2019, 8:16 p.m.