Keras Model
A model is a directed acyclic graph of layers.
keras_model(inputs, outputs = NULL, ...)
inputs |
Input layer |
outputs |
Output layer |
... |
Any additional arguments |
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()
## Not run:
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')
)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.