## ----setup, include = FALSE--------------------------------------------------- library(keras) knitr::opts_chunk$set(comment = NA, eval = FALSE) ## ----------------------------------------------------------------------------- # model %>% compile( # loss = 'categorical_crossentropy', # optimizer = optimizer_rmsprop(), # metrics = c('accuracy') # ) # # history <- model %>% fit( # x_train, y_train, # epochs = 30, batch_size = 128, # validation_split = 0.2 # ) ## ----------------------------------------------------------------------------- # plot(history) ## ----------------------------------------------------------------------------- # history_df <- as.data.frame(history) # str(history_df) ## ----------------------------------------------------------------------------- # # don't show metrics during this run # history <- model %>% fit( # x_train, y_train, # epochs = 30, batch_size = 128, # view_metrics = FALSE, # validation_split = 0.2 # ) # # # set global default to never show metrics # options(keras.view_metrics = FALSE) ## ----------------------------------------------------------------------------- # history <- model %>% fit( # x_train, y_train, # batch_size = batch_size, # epochs = epochs, # verbose = 1, # callbacks = callback_tensorboard("logs/run_a"), # validation_split = 0.2 # ) ## ----------------------------------------------------------------------------- # tensorboard("logs/run_a") ## ----------------------------------------------------------------------------- # # launch TensorBoard (data won't show up until after the first epoch) # tensorboard("logs/run_a") # # # fit the model with the TensorBoard callback # history <- model %>% fit( # x_train, y_train, # batch_size = batch_size, # epochs = epochs, # verbose = 1, # callbacks = callback_tensorboard("logs/run_a"), # validation_split = 0.2 # ) ## ----------------------------------------------------------------------------- # callback_tensorboard(log_dir = "logs/run_b") ## ----------------------------------------------------------------------------- # tensorboard("logs") ## ----------------------------------------------------------------------------- # tensorboard(c("logs/run_a", "logs/run_b"))