An Abalone Age Predictor

It’s possible to estimate the age of an abalone (sea snail) by the number of rings on its shell. However, because this task requires cutting, staining, and viewing the shell under a microscope, it’s desirable to find other measurements that can predict age.

The Abalone Data Set contains the following feature data for abalone:

Setup

This tutorial uses three data sets. abalone_train.csv contains labeled training data comprising 3,320 examples. abalone_test.csv contains labeled test data for 850 examples. abalone_predict contains 7 examples on which to make predictions.

The following sections walk through writing the Estimator code step by step; the full, final code is available here.

Downloading and Loading Abalone CSV Data

We first write a function that downloads the training, testing, and evaluation data from TensorFlow website if we haven’t downloaded them before.

library(tfestimators)

maybe_download_abalone <- function(train_data_path, test_data_path, predict_data_path, column_names_to_assign) {
  if (!file.exists(train_data_path) || !file.exists(test_data_path) || !file.exists(predict_data_path)) {
    cat("Downloading abalone data ...")
    train_data <- read.csv("http://download.tensorflow.org/data/abalone_train.csv", header = FALSE)
    test_data <- read.csv("http://download.tensorflow.org/data/abalone_test.csv", header = FALSE)
    predict_data <- read.csv("http://download.tensorflow.org/data/abalone_predict.csv", header = FALSE)
    colnames(train_data) <- column_names_to_assign
    colnames(test_data) <- column_names_to_assign
    colnames(predict_data) <- column_names_to_assign
    write.csv(train_data, train_data_path, row.names = FALSE)
    write.csv(test_data, test_data_path, row.names = FALSE)
    write.csv(predict_data, predict_data_path, row.names = FALSE)
  } else {
    train_data <- read.csv(train_data_path, header = TRUE)
    test_data <- read.csv(test_data_path, header = TRUE)
    predict_data <- read.csv(predict_data_path, header = TRUE)
  }
  return(list(train_data = train_data, test_data = test_data, predict_data = predict_data))
}

COLNAMES <- c("length", "diameter", "height", "whole_weight", "shucked_weight", "viscera_weight", "shell_weight", "num_rings")

downloaded_data <- maybe_download_abalone(
  file.path(getwd(), "train_abalone.csv"),
  file.path(getwd(), "test_abalone.csv"),
  file.path(getwd(), "predict_abalone.csv"),
  COLNAMES
)
train_data <- downloaded_data$train_data
test_data <- downloaded_data$test_data
predict_data <- downloaded_data$predict_data

Next, we construct the input function as follows:

constructed_input_fn <- function(dataset) {
  input_fn(dataset, features = -num_rings, response = num_rings, num_epochs = NULL)
}
train_input_fn <- constructed_input_fn(train_data)
test_input_fn <- constructed_input_fn(test_data)
predict_input_fn <- constructed_input_fn(predict_data)

Instantiating an Estimator

When defining a model using one of tf.estimator’s provided classes, such as linear_dnn_combined_classifier, you supply all the configuration parameters right in the constructor, e.g.:

diameter <- column_numeric("diameter")
height <- column_numeric("height")

model <- dnn_linear_combined_classifier(
  linear_feature_columns = feature_columns(diameter),
  dnn_feature_columns = feature_columns(height),
  dnn_hidden_units = c(100L, 50L)
)

You don’t need to write any further code to instruct TensorFlow how to train the model, calculate loss, or return predictions; that logic is already baked into the linear_dnn_combined_classifier.

By contrast, when you’re creating your own estimator from scratch, the constructor accepts just two high-level parameters for model configuration, model_fn and params:

model <- estimator(model_fn, params = model_params)

• model_fn: A function object that contains all the aforementioned logic to support training, evaluation, and prediction. You are responsible for implementing that functionality. The next section, Constructing the model_fn covers creating a model function in detail.

• params: An optional dict of hyperparameters (e.g., learning rate, dropout) that will be passed into the model_fn.

Note: Just like tfestimators’ predefined regressors and classifiers, the estimator initializer also accepts the general configuration arguments model_dir and config.

For the abalone age predictor, the model will accept one hyperparameter: learning rate. Here, learning_rate is set to 0.001, but you can tune this value as needed to achieve the best results during model training.

The following code creates the list model_params containing the learning rate and instantiates the Estimator:

# Set model params
model_params <- list(learning_rate = 0.001)

# Instantiate Estimator
model <- estimator(model_fn, params = model_params)

Constructing the model_fn

The basic skeleton for an Estimator API model function looks like this:

model_fn <- function(features, labels, mode, params, config) {
  # Logic to do the following:
  # 1. Configure the model via TensorFlow operations
  # 2. Define the loss function for training/evaluation
  # 3. Define the training operation/optimizer
  # 4. Generate predictions
  # 5. Return predictions/loss/train_op/eval_metric_ops in estimator_spec object
}

The model_fn must accept three arguments:

• features: A dict containing the features passed to the model via input_fn.
• labels: A Tensor containing the labels passed to the model via input_fn. Will be empty for predict() calls, as these are the values the model will infer.
• mode: One of the following mode_keys() string values indicating the context in which the model_fn was invoked:
  • "train" The model_fn was invoked in training mode, namely via a train() call.
  • "eval". The model_fn was invoked in evaluation mode, namely via an evaluate() call.
  • "infer". The model_fn was invoked in predict mode, namely via a predict() call.

model_fn may also accept a params argument containing a dict of hyperparameters used for training (as shown in the skeleton above) and a config that represents the configurations used in a model, including GPU percentage, cluster information, etc.

The body of the function performs the following tasks (described in detail in the sections that follow):

• Configuring the model—here, for the abalone predictor, this will be a neural network.
• Defining the loss function used to calculate how closely the model’s predictions match the target values.
• Defining the training operation that specifies the optimizer algorithm to minimize the loss values calculated by the loss function.

The model_fn must return an estimator_spec object, which contains the following values:

• mode (required). The mode in which the model was run. Typically, you will return the mode argument of the model_fn here.

• predictions (required in infer mode). A dict that maps key names of your choice to Tensors containing the predictions from the model, e.g.:

predictions <- list(results = tensor_of_predictions)

In infer mode, the dict that you return in estimator_spec will then be returned by predict(), so you can construct it in the format in which you’d like to consume it.

• loss (required in eval and train modes). A Tensor containing a scalar loss value: the output of the model’s loss function (discussed in more depth later in Defining loss for the model) calculated over all the input examples. This is used in train mode for error handling and logging, and is automatically included as a metric in eval mode.

• train_op (required only in train mode). An Op that runs one step of training.

• eval_metric_ops (optional). A dict of name/value pairs specifying the metrics that will be calculated when the model runs in eval mode. The name is a label of your choice for the metric, and the value is the result of your metric calculation. The tf$metrics module provides predefined functions for a variety of common metrics. The following eval_metric_ops contains an "accuracy" metric calculated using tf$metrics$accuracy:

eval_metric_ops <- list(accuracy = tf$metrics$accuracy(labels, predictions))

If you do not specify eval_metric_ops, only loss will be calculated during evaluation.

input_layer <- input_layer(
    features = features, feature_columns = c(age, height, weight))

hidden_layer <- tf$layers$dense(inputs = input_layer, units = 10L, activation = tf$nn$relu)

second_hidden_layer <- tf$layers$dense(
inputs = hidden_layer, units = 20L, activation = tf$nn$relu)

output_layer <- tf$layers$dense(inputs = second_hidden_layer,
units = 3L, activation = NULL)

output_layer <- tf$layers$dense(inputs = second_hidden_layer,
units = 10L, activation_fn = tf$sigmoid)

loss <- tf$losses$mean_squared_error(labels, predictions)

eval_metric_ops <- list(rmse = tf$metrics$root_mean_squared_error(
tf$cast(labels, tf$float64), predictions
))

optimizer <- tf$train$GradientDescentOptimizer(learning_rate = params$learning_rate)
train_op <- optimizer$minimize(loss = loss, global_step = tf$train$get_global_step())

model_fn <- function(features, labels, mode, params, config) {
  # Connect the first hidden layer to input layer
  first_hidden_layer <- tf$layers$dense(features, 10L, activation = tf$nn$relu)
  
  # Connect the second hidden layer to first hidden layer with relu
  second_hidden_layer <- tf$layers$dense(first_hidden_layer, 10L, activation = tf$nn$relu)
  
  # Connect the output layer to second hidden layer (no activation fn)
  output_layer <- tf$layers$dense(second_hidden_layer, 1L)
  
  # Reshape output layer to 1-dim Tensor to return predictions
  predictions <- tf$reshape(output_layer, list(-1L))
  predictions_list <- list(ages = predictions)
  
  # Calculate loss using mean squared error
  loss <- tf$losses$mean_squared_error(labels, predictions)
  
  eval_metric_ops <- list(rmse = tf$metrics$root_mean_squared_error(
    tf$cast(labels, tf$float64), predictions
  ))
  
  optimizer <- tf$train$GradientDescentOptimizer(learning_rate = params$learning_rate)
  train_op <- optimizer$minimize(loss = loss, global_step = tf$train$get_global_step())
  
  return(estimator_spec(
    mode = mode,
    predictions = predictions_list,
    loss = loss,
    train_op = train_op,
    eval_metric_ops = eval_metric_ops
  ))
}

model_params <- list(learning_rate = 0.001)
model <- estimator(model_fn, params = model_params)

Running the Abalone Model

You’ve instantiated an Estimator for the abalone predictor and defined its behavior in model_fn; all that’s left to do is train, evaluate, and make predictions.

The following code fits the neural network to the training data and evaluates the model performance based on the eval_metric_ops that we have defined:

train(model, input_fn = train_input_fn, steps = 2)

evaluate(model, input_fn = test_input_fn, steps = 2)