The tf.contrib.learn framework makes it easy to construct and train machine learning models via its high-level Estimator API. Estimator
offers classes you can instantiate to quickly configure common model types such as regressors and classifiers:
tf.contrib.learn.LinearClassifier
: Constructs a linear classification model.tf.contrib.learn.LinearRegressor
: Constructs a linear regression model.tf.contrib.learn.DNNClassifier
: Construct a neural network classification model.tf.contrib.learn.DNNRegressor
: Construct a neural network regressions model.But what if none of tf.contrib.learn
's predefined model types meets your needs? Perhaps you need more granular control over model configuration, such as the ability to customize the loss function used for optimization, or specify different activation functions for each neural network layer. Or maybe you're implementing a ranking or recommendation system, and neither a classifier nor a regressor is appropriate for generating predictions.
This tutorial covers how to create your own Estimator
using the building blocks provided in tf.contrib.learn
, which will predict the ages of abalones based on their physical measurements. You'll learn how to do the following:
Estimator
tf.contrib.layers
tf.losses
This tutorial assumes you already know tf.contrib.learn API basics, such as feature columns, input functions, and fit()
/evaluate()
/predict()
operations. If you've never used tf.contrib.learn before, or need a refresher, you should first review the following tutorials:
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:
Feature | Description |
---|---|
Length | Length of abalone (in longest direction; in mm) |
Diameter | Diameter of abalone (measurement perpendicular to length; in mm) |
Height | Height of abalone (with its meat inside shell; in mm) |
Whole Weight | Weight of entire abalone (in grams) |
Shucked Weight | Weight of abalone meat only (in grams) |
Viscera Weight | Gut weight of abalone (in grams), after bleeding |
Shell Weight | Weight of dried abalone shell (in grams) |
The label to predict is number of rings, as a proxy for abalone age.
“Abalone shell” (by Nicki Dugan Pogue, CC BY-SA 2.0)
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.
To feed the abalone dataset into the model, you'll need to download and load the CSVs into TensorFlow Dataset
s. First, add some standard Python and TensorFlow imports, and set up FLAGS:
from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import sys import tempfile # Import urllib from six.moves import urllib import numpy as np import tensorflow as tf from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib FLAGS = None
Enable logging:
tf.logging.set_verbosity(tf.logging.INFO)
Then define a function to load the CSVs (either from files specified in command-line options, or downloaded from tensorflow.org):
def maybe_download(train_data, test_data, predict_data): """Maybe downloads training data and returns train and test file names.""" if train_data: train_file_name = train_data else: train_file = tempfile.NamedTemporaryFile(delete=False) urllib.request.urlretrieve( "http://download.tensorflow.org/data/abalone_train.csv", train_file.name) train_file_name = train_file.name train_file.close() print("Training data is downloaded to %s" % train_file_name) if test_data: test_file_name = test_data else: test_file = tempfile.NamedTemporaryFile(delete=False) urllib.request.urlretrieve( "http://download.tensorflow.org/data/abalone_test.csv", test_file.name) test_file_name = test_file.name test_file.close() print("Test data is downloaded to %s" % test_file_name) if predict_data: predict_file_name = predict_data else: predict_file = tempfile.NamedTemporaryFile(delete=False) urllib.request.urlretrieve( "http://download.tensorflow.org/data/abalone_predict.csv", predict_file.name) predict_file_name = predict_file.name predict_file.close() print("Prediction data is downloaded to %s" % predict_file_name) return train_file_name, test_file_name, predict_file_name
Finally, create main()
and load the abalone CSVs into Datasets
, defining flags to allow users to optionally specify CSV files for training, test, and prediction datasets via the command line (by default, files will be downloaded from tensorflow.org):
def main(unused_argv): # Load datasets abalone_train, abalone_test, abalone_predict = maybe_download( FLAGS.train_data, FLAGS.test_data, FLAGS.predict_data) # Training examples training_set = tf.contrib.learn.datasets.base.load_csv_without_header( filename=abalone_train, target_dtype=np.int, features_dtype=np.float64) # Test examples test_set = tf.contrib.learn.datasets.base.load_csv_without_header( filename=abalone_test, target_dtype=np.int, features_dtype=np.float64) # Set of 7 examples for which to predict abalone ages prediction_set = tf.contrib.learn.datasets.base.load_csv_without_header( filename=abalone_predict, target_dtype=np.int, features_dtype=np.float64) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.register("type", "bool", lambda v: v.lower() == "true") parser.add_argument( "--train_data", type=str, default="", help="Path to the training data.") parser.add_argument( "--test_data", type=str, default="", help="Path to the test data.") parser.add_argument( "--predict_data", type=str, default="", help="Path to the prediction data.") FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
When defining a model using one of tf.contrib.learn's provided classes, such as DNNClassifier
, you supply all the configuration parameters right in the constructor, e.g.:
my_nn = tf.contrib.learn.DNNClassifier(feature_columns=[age, height, weight], hidden_units=[10, 10, 10], activation_fn=tf.nn.relu, dropout=0.2, n_classes=3, optimizer="Adam")
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 DNNClassifier
.
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
:
nn = tf.contrib.learn.Estimator( model_fn=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 liketf.contrib.learn
's predefined regressors and classifiers, theEstimator
initializer also accepts the general configuration argumentsmodel_dir
andconfig
.
For the abalone age predictor, the model will accept one hyperparameter: learning rate. Define LEARNING_RATE
as a constant at the beginning of your code (highlighted in bold below), right after the logging configuration:
tf.logging.set_verbosity(tf.logging.INFO) # Learning rate for the model LEARNING_RATE = 0.001
Note: Here,LEARNING_RATE
is set to0.001
, but you can tune this value as needed to achieve the best results during model training.
Then, add the following code to main()
, which creates the dict model_params
containing the learning rate and instantiates the Estimator
:
# Set model params model_params = {"learning_rate": LEARNING_RATE} # Instantiate Estimator nn = tf.contrib.learn.Estimator(model_fn=model_fn, params=model_params)
model_fn
The basic skeleton for an Estimator
API model function looks like this:
def model_fn(features, targets, mode, params): # 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 ModelFnOps object return ModelFnOps(mode, predictions, loss, train_op, eval_metric_ops)
The model_fn
must accept three arguments:
features
: A dict containing the features passed to the model via fit()
, evaluate()
, or predict()
.targets
: A Tensor
containing the labels passed to the model via fit()
, evaluate()
, or predict()
. Will be empty for predict()
calls, as these are the values the model will infer.mode
: One of the following tf.contrib.learn.ModeKeys
string values indicating the context in which the model_fn was invoked:tf.contrib.learn.ModeKeys.TRAIN
The model_fn
was invoked in training mode—e.g., via a fit()
call.tf.contrib.learn.ModeKeys.EVAL
. The model_fn
was invoked in evaluation mode—e.g., via an evaluate()
call.tf.contrib.learn.ModeKeys.INFER
. The model_fn
was invoked in inference mode—e.g., 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).
The body of the function perfoms the following tasks (described in detail in the sections that follow):
optimizer
algorithm to minimize the loss values calculated by the loss function.The model_fn
must return a tf.contrib.learn.ModelFnOps
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
and EVAL
modes). A dict that maps key names of your choice to Tensor
s containing the predictions from the model, e.g.:
python predictions = {"results": tensor_of_predictions}
In INFER
mode, the dict that you return in ModelFnOps
will then be returned by predict()
, so you can construct it in the format in which you'd like to consume it.
In EVAL
mode, the dict is used by metric functions to compute metrics.
loss
(required in EVAL
and TRAIN
mode). 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
:
python eval_metric_ops = { "accuracy": tf.metrics.accuracy(labels, predictions) }
If you do not specify eval_metric_ops
, only loss
will be calculated during evaluation.
tf.contrib.layers
Constructing a neural network entails creating and connecting the input layer, the hidden layers, and the output layer.
The input layer is a series of nodes (one for each feature in the model) that will accept the feature data that is passed to the model_fn
in the features
argument. If features
contains an n-dimenional Tensor
with all your feature data (which is the case if x
and y
Dataset
s are passed to fit()
, evaluate()
, and predict()
directly), then it can serve as the input layer. If features
contains a dict of feature columns passed to the model via an input function, you can convert it to an input-layer Tensor
with the tf.contrib.layers.input_from_feature_columns
function in tf.contrib.layers
.
input_layer = tf.contrib.layers.input_from_feature_columns( columns_to_tensors=features, feature_columns=[age, height, weight])
As shown above, input_from_feature_columns()
takes two required arguments:
columns_to_tensors
. A mapping of the model's FeatureColumns
to the Tensors
containing the corresponding feature data. This is exactly what is passed to the model_fn
in the features
argument.feature_columns
. A list of all the FeatureColumns
in the model—age
, height
, and weight
in the above example.The input layer of the neural network then must be connected to one or more hidden layers via an activation function that performs a nonlinear transformation on the data from the previous layer. The last hidden layer is then connected to the output layer, the final layer in the model. tf.contrib.layers provides the following convenience functions for constructing fully connected layers:
relu(inputs, num_outputs)
. Create a layer of num_outputs
nodes fully connected to the previous layer inputs
with a ReLU activation function (tf.nn.relu
):
python hidden_layer = tf.contrib.layers.relu(inputs=input_layer, num_outputs=10)
relu6(inputs, num_outputs)
. Create a layer of num_outputs
nodes fully connected to the previous layer hidden_layer
with a ReLU 6 activation function (tf.nn.relu6
):
python second_hidden_layer = tf.contrib.layers.relu6(inputs=hidden_layer, num_outputs=20)
linear(inputs, num_outputs)
. Create a layer of num_outputs
nodes fully connected to the previous layer second_hidden_layer
with no activation function, just a linear transformation:
python output_layer = tf.contrib.layers.linear(inputs=second_hidden_layer, num_outputs=3)
All these functions are partials of the more general tf.contrib.layers.fully_connected
function, which can be used to add fully connected layers with other activation functions, e.g.:
output_layer = tf.contrib.layers.fully_connected(inputs=second_hidden_layer, num_outputs=10, activation_fn=tf.sigmoid)
The above code creates the neural network layer output_layer
, which is fully connected to second_hidden_layer
with a sigmoid activation function (tf.sigmoid
). For a list of predefined activation functions available in TensorFlow, see the API docs.
Putting it all together, the following code constructs a full neural network for the abalone predictor, and captures its predictions:
def model_fn(features, targets, mode, params): """Model function for Estimator.""" # Connect the first hidden layer to input layer # (features) with relu activation first_hidden_layer = tf.contrib.layers.relu(features, 10) # Connect the second hidden layer to first hidden layer with relu second_hidden_layer = tf.contrib.layers.relu(first_hidden_layer, 10) # Connect the output layer to second hidden layer (no activation fn) output_layer = tf.contrib.layers.linear(second_hidden_layer, 1) # Reshape output layer to 1-dim Tensor to return predictions predictions = tf.reshape(output_layer, [-1]) predictions_dict = {"ages": predictions} ...
Here, because you'll be passing the abalone Datasets
directly to fit()
, evaluate()
, and predict()
via x
and y
arguments, the input layer is the features
Tensor
passed to the model_fn
. The network contains two hidden layers, each with 10 nodes and a ReLU activation function. The output layer contains no activation function, and is tf.reshape
to a one-dimensional tensor to capture the model's predictions, which are stored in predictions_dict
.
The ModelFnOps
returned by the model_fn
must contain loss
: a Tensor
representing the loss value, which quantifies how well the model's predictions reflect the target values during training and evaluation runs. The tf.losses
module provides convenience functions for calculating loss using a variety of metrics, including:
absolute_difference(predictions, targets)
. Calculates loss using the absolute-difference formula (also known as L_{1} loss).
log_loss(predictions, targets)
. Calculates loss using the logistic loss forumula (typically used in logistic regression).
mean_squared_error(predictions, targets)
. Calculates loss using the mean squared error (MSE; also known as L_{2} loss).
The following example adds a definition for loss
to the abalone model_fn
using mean_squared_error()
(in bold):
def model_fn(features, targets, mode, params): """Model function for Estimator.""" # Connect the first hidden layer to input layer # (features) with relu activation first_hidden_layer = tf.contrib.layers.relu(features, 10) # Connect the second hidden layer to first hidden layer with relu second_hidden_layer = tf.contrib.layers.relu(first_hidden_layer, 10) # Connect the output layer to second hidden layer (no activation fn) output_layer = tf.contrib.layers.linear(second_hidden_layer, 1) # Reshape output layer to 1-dim Tensor to return predictions predictions = tf.reshape(output_layer, [-1]) predictions_dict = {"ages": predictions} # Calculate loss using mean squared error loss = tf.losses.mean_squared_error(targets, predictions) ...
See the API guide for a full list of loss functions and more details on supported arguments and usage.
Supplementary metrics for evaluation can be added to an eval_metric_ops
dict. The following code defines an rmse
metric, which calculates the root mean squared error for the model predictions. Note that the targets
tensor is cast to a float64
type to match the data type of the predictions
tensor, which will contain real values:
eval_metric_ops = { "rmse": tf.metrics.root_mean_squared_error( tf.cast(targets, tf.float64), predictions) }
The training op defines the optimization algorithm TensorFlow will use when fitting the model to the training data. Typically when training, the goal is to minimize loss. The tf.contrib.layers API provides the function optimize_loss
, which returns a training op that will do just that. optimize_loss
has four required arguments:
loss
. The loss value calculated by the model_fn
(see Defining Loss for the Model).global_step
. An integer tf.Variable
representing the step counter to increment for each model training run. Can easily be created/incremented in TensorFlow via the tf.train.get_global_step
function.learning_rate
. The learning rate (also known as step size) hyperparameter that the optimization algorithm uses when training.optimizer
. The optimization algorithm to use during training. optimizer
can accept any of the following string values, representing an optimization algorithm predefined in tf.contrib.layers.optimizers
:SGD
. Implementation of gradient descent (tf.train.GradientDescentOptimizer
)Adagrad
. Implementation of the AdaGrad optimization algorithm (tf.train.AdagradOptimizer
)Adam
. Implementation of the Adam optimization algorithm (tf.train.AdamOptimizer
)Ftrl
. Implementation of the FTRL-Proximal ("Follow The (Proximally) Regularized Leader") algorithm (tf.train.FtrlOptimizer
)Momentum
. Implementation of stochastic gradient descent with momentum (tf.train.MomentumOptimizer
)RMSProp
. Implementation of the RMSprop algorithm (tf.train.RMSPropOptimizer
)
Note: The optimize_loss
function supports additional optional arguments to further configure the optimizer, such as for implementing decay. See the API docs for more info.
The following code defines a training op for the abalone model_fn
using the loss value calculated in Defining Loss for the Model, the learning rate passed to the function in params
, and the SGD optimizer. For global_step
, the convenience function tf.train.get_global_step
in tf.contrib.framework takes care of generating an integer variable:
train_op = tf.contrib.layers.optimize_loss( loss=loss, global_step=tf.contrib.framework.get_global_step(), learning_rate=params["learning_rate"], optimizer="SGD")
model_fn
Here's the final, complete model_fn
for the abalone age predictor. The following code configures the neural network; defines loss and the training op; and returns a ModelFnOps
object containing mode
, predictions_dict
, loss
, and train_op
:
def model_fn(features, targets, mode, params): """Model function for Estimator.""" # Connect the first hidden layer to input layer # (features) with relu activation first_hidden_layer = tf.contrib.layers.relu(features, 10) # Connect the second hidden layer to first hidden layer with relu second_hidden_layer = tf.contrib.layers.relu(first_hidden_layer, 10) # Connect the output layer to second hidden layer (no activation fn) output_layer = tf.contrib.layers.linear(second_hidden_layer, 1) # Reshape output layer to 1-dim Tensor to return predictions predictions = tf.reshape(output_layer, [-1]) predictions_dict = {"ages": predictions} # Calculate loss using mean squared error loss = tf.losses.mean_squared_error(targets, predictions) # Calculate root mean squared error as additional eval metric eval_metric_ops = { "rmse": tf.metrics.root_mean_squared_error( tf.cast(targets, tf.float64), predictions) } train_op = tf.contrib.layers.optimize_loss( loss=loss, global_step=tf.contrib.framework.get_global_step(), learning_rate=params["learning_rate"], optimizer="SGD") return model_fn_lib.ModelFnOps( mode=mode, predictions=predictions_dict, loss=loss, train_op=train_op, eval_metric_ops=eval_metric_ops)
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.
Add the following code to the end of main()
to fit the neural network to the training data and evaluate accuracy:
def get_train_inputs(): x = tf.constant(training_set.data) y = tf.constant(training_set.target) return x, y # Fit nn.fit(input_fn=get_train_inputs, steps=5000) def get_test_inputs(): x = tf.constant(test_set.data) y = tf.constant(test_set.target) return x, y # Score accuracy ev = nn.evaluate(input_fn=get_test_inputs, steps=1) print("Loss: %s" % ev["loss"]) print("Root Mean Squared Error: %s" % ev["rmse"])
Note: The above code uses input functions to feed feature (x
) and label (y
)Tensor
s into the model for both training (get_train_inputs()
) and evaluation (get_test_inputs()
). To learn more about input functions, see the tutorial Building Input Functions with tf.contrib.learn.
Then run the code. You should see output like the following:
... INFO:tensorflow:loss = 4.86658, step = 4701 INFO:tensorflow:loss = 4.86191, step = 4801 INFO:tensorflow:loss = 4.85788, step = 4901 ... INFO:tensorflow:Saving evaluation summary for 5000 step: loss = 5.581 Loss: 5.581
The loss score reported is the mean squared error returned from the model_fn
when run on the ABALONE_TEST
data set.
To predict ages for the ABALONE_PREDICT
data set, add the following to main()
:
# Print out predictions predictions = nn.predict(x=prediction_set.data, as_iterable=True) for i, p in enumerate(predictions): print("Prediction %s: %s" % (i + 1, p["ages"]))
Here, the predict()
function returns results in predictions
as an iterable. The for
loop enumerates and prints out the results. Rerun the code, and you should see output similar to the following:
... Prediction 1: 4.92229 Prediction 2: 10.3225 Prediction 3: 7.384 Prediction 4: 10.6264 Prediction 5: 11.0862 Prediction 6: 9.39239 Prediction 7: 11.1289
Congrats! You've successfully built a tf.contrib.learn Estimator
from scratch. For additional reference materials on building Estimator
s, see the following sections of the API guides:
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/extend/estimators