library(keras)
# input layer
inputs <- layer_input(shape = c(784))
# outputs compose input + dense layers
predictions <- inputs %>%
layer_dense(units = 64, activation = 'relu') %>%
layer_dense(units = 64, activation = 'relu') %>%
layer_dense(units = 10, activation = 'softmax')
# create and compile model
model <- keras_model(inputs = inputs, outputs = predictions)
model %>% compile(
optimizer = 'rmsprop',
loss = 'categorical_crossentropy',
metrics = c('accuracy')
) keras_model
Keras Model
Description
A model is a directed acyclic graph of layers.
Usage
keras_model(inputs, outputs = NULL, ...) Arguments
| Arguments | Description |
|---|---|
| inputs | Input layer |
| outputs | Output layer |
| … | Any additional arguments |
Examples
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_sequential(), 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()