Model evaluation (TFMA) in TensorFlow - Model Pipeline Trace

Model Pipeline - Model evaluation (TFMA)

This pipeline shows how a trained TensorFlow model is evaluated using TensorFlow Model Analysis (TFMA). It helps us understand how well the model performs on different slices of data and overall.

Data Flow - 4 Stages

1Raw data input

1000 rows x 5 columns→Load raw dataset with features and labels→1000 rows x 5 columns

{"feature1": 3.5, "feature2": 1.2, "label": 0}

↓

2Data preprocessing

1000 rows x 5 columns→Normalize features, convert labels to integers→1000 rows x 5 columns

{"feature1": 0.7, "feature2": 0.24, "label": 0}

↓

3Model prediction

1000 rows x 5 columns→Run model to get prediction probabilities→1000 rows x 2 columns

{"prob_class_0": 0.85, "prob_class_1": 0.15}

↓

4TFMA evaluation

1000 rows x 7 columns (features + labels + predictions)→Calculate metrics like accuracy, precision, recall overall and by slices→Summary metrics report

{"overall_accuracy": 0.92, "slice_age<30_accuracy": 0.89}

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |*   
0.2 |*   
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning, loss high, accuracy low
2	0.48	0.75	Loss decreases, accuracy improves
3	0.35	0.85	Model converging, better predictions
4	0.28	0.90	Loss continues to decrease, accuracy high
5	0.25	0.92	Training stabilizes with good accuracy

Prediction Trace - 3 Layers

Layer 1: Input features

Layer 2: Model inference

Layer 3: TFMA metric calculation

Model Quiz - 3 Questions

What does TFMA primarily help us understand?

A How to train the model faster

B How to preprocess data

C How well the model performs on different data slices

D How to deploy the model

Key Insight

TFMA helps us evaluate a trained model's performance in detail, including overall accuracy and how it behaves on different groups of data. This ensures the model is fair and reliable before deployment.

Practice

1. What is the main purpose of TensorFlow Model Analysis (TFMA)?

easy

A. To preprocess data before training

B. To train machine learning models faster

C. To visualize neural network architectures

D. To evaluate machine learning models with detailed metrics and data slices

Solution

1. Step 1: Understand TFMA's role

   TFMA is designed to evaluate models, not train or preprocess them.

2. Step 2: Identify TFMA's key feature

   It provides detailed metrics and supports evaluation on data slices.

3. Final Answer:

   To evaluate machine learning models with detailed metrics and data slices -> Option D

4. Quick Check:

   TFMA = Model evaluation [OK]

Hint: TFMA is for evaluation, not training or preprocessing [OK]

Common Mistakes:

• Confusing TFMA with training tools
• Thinking TFMA is for data preprocessing
• Assuming TFMA visualizes model structure

2. Which of the following is the correct way to specify metrics in an EvalConfig for TFMA?

easy

A. eval_config = tfma.EvalConfig(metrics_specs=[tfma.MetricsSpec(metrics=[tfma.MetricConfig(class_name='ExampleCount')])])

B. eval_config = tfma.EvalConfig(metrics='accuracy')

C. eval_config = tfma.EvalConfig(metrics=['loss', 'accuracy'])

D. eval_config = tfma.EvalConfig(metrics=tf.keras.metrics.Accuracy())

Solution

1. Step 1: Check TFMA metric config syntax

   TFMA expects metrics_specs as a list of MetricsSpec objects containing MetricConfig, not strings or Keras metrics.

2. Step 2: Validate each option

   eval_config = tfma.EvalConfig(metrics_specs=[tfma.MetricsSpec(metrics=[tfma.MetricConfig(class_name='ExampleCount')])]) uses the correct structure; others use incorrect types or formats.

3. Final Answer:

   eval_config = tfma.EvalConfig(metrics_specs=[tfma.MetricsSpec(metrics=[tfma.MetricConfig(class_name='ExampleCount')])]) -> Option A

4. Quick Check:

   TFMA metrics = metrics_specs with MetricsSpec and MetricConfig [OK]

Hint: Use metrics_specs=[MetricsSpec(metrics=[MetricConfig(...)])] for TFMA metrics [OK]

Common Mistakes:

• Passing metric names as strings directly
• Using Keras metric objects instead of MetricConfig
• Not wrapping metrics in a list

3. Given the following TFMA code snippet, what will be the output type of eval_result?

import tensorflow_model_analysis as tfma

eval_result = tfma.run_model_analysis(
    model_location='saved_model_dir',
    data_location='eval_data.tfrecord',
    eval_config=tfma.EvalConfig(
        metrics_specs=[tfma.MetricsSpec(metrics=[tfma.MetricConfig(class_name='Accuracy')])]
    )
)

medium

A. A dictionary containing evaluation metrics and slicing results

B. A TensorFlow model object

C. A list of raw prediction tensors

D. A string path to saved evaluation results

Solution

1. Step 1: Understand tfma.run_model_analysis output

   This function returns a dictionary with metrics and slice keys describing evaluation results.

2. Step 2: Eliminate other options

   It does not return a model object, raw tensors, or just a path string.

3. Final Answer:

   A dictionary containing evaluation metrics and slicing results -> Option A

4. Quick Check:

   tfma.run_model_analysis returns dict [OK]

Hint: tfma.run_model_analysis returns dict with metrics [OK]

Common Mistakes:

• Expecting a model object instead of results
• Thinking output is raw tensors
• Assuming output is a file path string

4. You run TFMA evaluation but get an error: AttributeError: 'EvalConfig' object has no attribute 'metrics'. What is the likely cause?

medium

A. You used a wrong metric class name

B. You forgot to import tensorflow_model_analysis

C. You passed metrics as a parameter directly to EvalConfig constructor instead of inside a 'metrics_specs' field

D. Your saved model path is incorrect

Solution

1. Step 1: Understand EvalConfig structure

   EvalConfig expects metrics inside the metrics_specs field, not directly as a constructor argument named 'metrics'.

2. Step 2: Identify error cause

   Passing 'metrics' directly causes AttributeError because EvalConfig has no such direct attribute.

3. Final Answer:

   You passed metrics as a parameter directly to EvalConfig constructor instead of inside a 'metrics_specs' field -> Option C

4. Quick Check:

   EvalConfig metrics must be inside metrics_specs [OK]

Hint: Put metrics inside metrics_specs, not directly in EvalConfig [OK]

Common Mistakes:

• Passing metrics directly to EvalConfig constructor
• Ignoring correct EvalConfig structure
• Misreading error as import or path issue

5. You want to evaluate your model's accuracy separately for male and female users using TFMA. Which EvalConfig snippet correctly sets up slicing by a feature named gender?

hard

A. eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec()],
slicing_specs=[tfma.SlicingSpec(columns=['gender'])],
metrics_specs=[tfma.MetricsSpec(metrics=[tfma.MetricConfig(class_name='Accuracy')])]
)

B. eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec()],
slicing_specs=[tfma.SlicingSpec(feature_keys=['gender'])],
metrics_specs=[tfma.MetricsSpec(metrics=[tfma.MetricConfig(class_name='Accuracy')])]
)

C. eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec()],
slicing_specs=[tfma.SlicingSpec(feature_keys=['gender', 'age'])],
metrics_specs=[tfma.MetricsSpec(metrics=[tfma.MetricConfig(class_name='Accuracy')])]
)

D. eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec()],
slicing_specs=[tfma.SlicingSpec(feature_keys=['gender'])],
metrics_specs=[tfma.MetricsSpec(metrics=['Accuracy'])]
)

Solution

1. Step 1: Identify correct slicing key

   TFMA uses 'feature_keys' in SlicingSpec to specify features for slicing.

2. Step 2: Check metrics specification

   Metrics must be wrapped in MetricConfig inside MetricsSpec; eval_config = tfma.EvalConfig( model_specs=[tfma.ModelSpec()], slicing_specs=[tfma.SlicingSpec(feature_keys=['gender'])], metrics_specs=[tfma.MetricsSpec(metrics=[tfma.MetricConfig(class_name='Accuracy')])] ) does this correctly.

3. Step 3: Validate other options

   eval_config = tfma.EvalConfig( model_specs=[tfma.ModelSpec()], slicing_specs=[tfma.SlicingSpec(columns=['gender'])], metrics_specs=[tfma.MetricsSpec(metrics=[tfma.MetricConfig(class_name='Accuracy')])] ) uses 'columns' which is incorrect; C slices by two features, not just gender; D passes metrics as strings, which is invalid.

4. Final Answer:

   EvalConfig with slicing_specs using feature_keys=['gender'] and metrics_specs with MetricConfig metrics -> Option B

5. Quick Check:

   Slicing by feature_keys and MetricConfig metrics [OK]

Hint: Use feature_keys for slicing and MetricConfig for metrics [OK]

Common Mistakes:

• Using 'columns' instead of 'feature_keys' for slicing
• Passing metrics as strings instead of MetricConfig
• Adding extra features in slicing unintentionally