mnist_cnn_embeddings

    This example shows how to visualize embeddings in TensorBoard.

    Embeddings in the sense used here don’t necessarily refer to embedding layers. In fact, features (= activations) from other hidden layers can be visualized, as shown in this example for a dense layer.

    library(keras)
    
    # Data Preparation -----------------------------------------------------
    
    batch_size <- 128
    num_classes <- 10
    epochs <- 12
    
    # Input image dimensions
    img_rows <- 28
    img_cols <- 28
    
    # The data, shuffled and split between train and test sets
    mnist <- dataset_mnist()
    x_train <- mnist$train$x
    y_train <- mnist$train$y
    x_test <- mnist$test$x
    y_test <- mnist$test$y
    
    # Redefine  dimension of train/test inputs
    x_train <-
      array_reshape(x_train, c(nrow(x_train), img_rows, img_cols, 1))
    x_test <-
      array_reshape(x_test, c(nrow(x_test), img_rows, img_cols, 1))
    input_shape <- c(img_rows, img_cols, 1)
    
    # Transform RGB values into [0,1] range
    x_train <- x_train / 255
    x_test <- x_test / 255
    
    cat('x_train_shape:', dim(x_train), '\n')
    cat(nrow(x_train), 'train samples\n')
    cat(nrow(x_test), 'test samples\n')
    
    
    # Prepare for logging embeddings --------------------------------------------------
    
    embeddings_dir <- file.path(tempdir(), 'embeddings')
    if (!file.exists(embeddings_dir))
      dir.create(embeddings_dir)
    embeddings_metadata <- file.path(embeddings_dir, 'metadata.tsv')
    
    # we use the class names from the test set as embeddings_metadata
    readr::write_tsv(data.frame(y_test), path = embeddings_metadata, col_names = FALSE)
    
    tensorboard_callback <- callback_tensorboard(
      log_dir = embeddings_dir,
      batch_size = batch_size,
      embeddings_freq = 1,
      # if missing or NULL all embedding layers will be monitored
      embeddings_layer_names = list('features'),
      # single file for all embedding layers, could also be a named list mapping
      # layer names to file names
      embeddings_metadata = embeddings_metadata,
      # data to be embedded
      embeddings_data = x_test
    )
    
    
    # Define Model -----------------------------------------------------------
    
    # Convert class vectors to binary class matrices
    y_train <- to_categorical(y_train, num_classes)
    y_test <- to_categorical(y_test, num_classes)
    
    # Define model
    model <- keras_model_sequential() %>%
      layer_conv_2d(
        filters = 32,
        kernel_size = c(3, 3),
        activation = 'relu',
        input_shape = input_shape
      ) %>%
      layer_conv_2d(filters = 64,
                    kernel_size = c(3, 3),
                    activation = 'relu') %>%
      layer_max_pooling_2d(pool_size = c(2, 2)) %>%
      layer_dropout(rate = 0.25) %>%
      layer_flatten() %>%
      # these are the embeddings (activations) we are going to visualize
      layer_dense(units = 128, activation = 'relu', name = 'features') %>%
      layer_dropout(rate = 0.5) %>%
      layer_dense(units = num_classes, activation = 'softmax')
    
    # Compile model
    model %>% compile(
      loss = loss_categorical_crossentropy,
      optimizer = optimizer_adadelta(),
      metrics = c('accuracy')
    )
    
    # Launch TensorBoard
    #
    # As the model is being fit you will be able to view the embedings in the 
    # Projector tab. On the left, use "color by label" to see the digits displayed
    # in 10 different colors. Hover over a point to see its label.
    tensorboard(embeddings_dir)
    
    # Train model
    model %>% fit(
      x_train,
      y_train,
      batch_size = batch_size,
      epochs = epochs,
      validation_data = list(x_test, y_test),
      callbacks = list(tensorboard_callback)
    )
    
    scores <- model %>% evaluate(x_test, y_test, verbose = 0)
    
    # Output metrics
    cat('Test loss:', scores[[1]], '\n')
    cat('Test accuracy:', scores[[2]], '\n')