5 Effective Methods to Train a TensorFlow Model on Fashion MNIST Dataset in Python

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πŸ’‘ Problem Formulation: This article explores how TensorFlow can be harnessed to train machine learning models for classifying items in the Fashion MNIST dataset, a collection of 28×28 grayscale images representing different fashion products. We will look into distinct techniques to process and model this data with TensorFlow to achieve accurate predictions. The input is an image from the dataset, and the output is the model’s prediction for the fashion category.

Method 1: Using a Basic Dense Neural Network

A simple yet effective approach is to use a dense neural network. In TensorFlow, this can be achieved using the Sequential API to stack layers densely connected. These layers consist of neurons learning the intricate patterns within the Fashion MNIST data. The model usually includes a Flatten layer to convert 2D images into 1D arrays, followed by a series of Dense layers with activation functions such as ReLU.

Here’s an example:

import tensorflow as tf
from tensorflow.keras import layers, models

model = models.Sequential([
    layers.Flatten(input_shape=(28, 28)),
    layers.Dense(128, activation='relu'),


The output is a compiled TensorFlow model ready to be trained on the Fashion MNIST data.

This code snippet constructs a TensorFlow model using the Keras API. It initializes a Sequential model, adds layers to it, then compiles the model with an optimizer and loss function. The Flatten layer reshapes input data, while Dense layers are used for learning from the features.

Method 2: Implementing Convolutional Neural Networks (CNN)

Convolutional Neural Networks are particularly suited for image data. In TensorFlow, a CNN can capture spatial hierarchies in the data by employing convolutional and pooling layers. These layers automatically and adaptively learn spatial hierarchies of features from the input images. A CNN for the Fashion MNIST may include several Conv2D and MaxPooling2D layers followed by a Flatten and Dense layers.

Here’s an example:

model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Dense(64, activation='relu'),
    layers.Dense(10, activation='softmax')


The output is a compiled TensorFlow CNN model ready for training.

In this method, the model uses convolutional layers to extract patterns in the image data, max pooling layers to reduce dimensionality, and dense layers for classification. The code provided sets up the layers and compiles the model, preparing it for training on the Fashion MNIST dataset.

Method 3: Using Data Augmentation

Data augmentation is a technique to increase the diversity of training data by applying random transformations that generate plausible images. TensorFlow offers the ImageDataGenerator class that can augment image data in real-time during model training. This helps prevent overfitting and makes the model robust to variations it might encounter in real-world data.

Here’s an example:

from tensorflow.keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator(

# Assume X_train and y_train are already defined and preprocessed.
model.fit(datagen.flow(X_train, y_train, batch_size=32), steps_per_epoch=len(X_train) / 32, epochs=10)

The output is an enhanced trained model with improvements in generalization.

This snippet configures the ImageDataGenerator with several augmentation parameters, applies these augmentations to the training data, and then trains the model with the augmented dataset. This enhanced training procedure aids the model in learning more general and robust representations.

Method 4: Tuning Hyperparameters

Hyperparameter tuning is pivotal for optimizing model performance. TensorFlow provides tools such as Keras Tuner to automate the search for the best hyperparameter settings. It allows different configurations to be tested systematically, identifying the architecture that best fits the data.

Here’s an example:

import kerastuner as kt

def build_model(hp):
    model = models.Sequential()
    model.add(layers.Flatten(input_shape=(28, 28)))
        units=hp.Int('units', min_value=32, max_value=512, step=32), 
    model.add(layers.Dense(10, activation='softmax'))
    return model

tuner = kt.Hyperband(build_model,

tuner.search(X_train, y_train, epochs=10, validation_split=0.2)

The output is a model with a set of hyperparameters that have been tuned to potentially increase performance on the validation set.

By using Keras Tuner, the code defines a model-building function that creates a Sequential model, where the number of units in the Dense layer is a hyperparameter to be tuned. The Hyperband tuner then systematically evaluates different configurations to find the best one.

Bonus One-Liner Method 5: Transfer Learning

Transfer learning leverages a pre-trained model and adapts it to the new task with minimum training. TensorFlow Hub provides access to many pre-trained models that can be employed for various machine learning tasks, including image classification.

Here’s an example:

model = tf.keras.Sequential([
    hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4', input_shape=(28, 28, 1), trainable=False),
    layers.Dense(10, activation='softmax')

The output is a model employing a pre-trained feature extractor with a new final layer for the Fashion MNIST classes.

The code leverages TensorFlow Hub to include a pre-trained MobileNetV2 feature extractor in a Sequential model, followed by a Dense layer for classification. This is a quick way to set up a powerful image classifier for the Fashion MNIST dataset.


  • Method 1: Basic Dense Neural Network. Simple to implement. May not capture complex features as effectively as CNNs.
  • Method 2: Convolutional Neural Networks (CNN). Powerful for image data. More computationally intensive than basic neural networks.
  • Method 3: Data Augmentation. Improves generalization. Adds complexity to the training process.
  • Method 4: Tuning Hyperparameters. Optimizes model performance. Time-consuming due to the trial-and-error nature.
  • Method 5: Transfer Learning. Quick and effective. Requires domain knowledge to select an appropriate pre-trained model.