Principle:Tensorflow Serving Model Training

Knowledge Sources	TensorFlow Training MNIST Tutorial
Domains	Deep_Learning, Training
Last Updated	2026-02-13 17:00 GMT

Overview

A supervised learning process that optimizes model parameters by minimizing a loss function over labeled training data.

Description

Model training is the fundamental process of adjusting neural network weights through iterative optimization. In the context of TensorFlow Serving, training produces a model with learned parameters that can be serialized and exported for inference. The training process involves defining a computation graph, feeding batched training data, computing loss via forward passes, and updating weights via backpropagation with gradient descent.

For the MNIST digit classification task, a simple softmax regression model learns a linear mapping from 784-dimensional input (28x28 pixel images) to 10 output classes (digits 0-9). While simple, this pattern generalizes to arbitrarily complex architectures.

Usage

Use this principle as the first step before serving any model. Training must produce a TensorFlow session (or SavedModel-compatible artifact) with finalized weights. The trained session is then passed to the export stage for serialization into SavedModel format.

Theoretical Basis

The softmax regression model computes:

$y=\text{softmax}(Wx+b)$

Where:

• $W$ is the weight matrix (784 x 10)
• $b$ is the bias vector (10,)
• $x$ is the input image vector (784,)

Training minimizes the cross-entropy loss:

$L=-\sum _{i}y{\text{'}}_i\log (y_i)$

Using gradient descent with learning rate 0.01:

Pseudo-code:

# Abstract training loop (NOT real implementation)
for iteration in range(num_iterations):
    batch_x, batch_y = get_next_batch(training_data, batch_size=50)
    predictions = softmax(matmul(batch_x, W) + b)
    loss = cross_entropy(predictions, batch_y)
    W, b = gradient_descent_update(W, b, loss, learning_rate=0.01)

Related Pages

Implemented By

• Implementation:Tensorflow_Serving_Mnist_Training_Example