keras_model_sequential

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Keras Model composed of a linear stack of layers

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, evaluate.keras.engine.training.Model, evaluate_generator, fit_generator, fit, 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

Aliases
  • keras_model_sequential
Examples
library(keras) # 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 %>% compile( optimizer = 'rmsprop', loss = 'categorical_crossentropy', metrics = c('accuracy') ) # }
Documentation reproduced from package keras, version 2.1.6, License: MIT + file LICENSE

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