TensorFlow MNIST Classifier

This repository contains a simple TensorFlow-based neural network model for classifying MNIST handwritten digits dataset. The model is implemented using TensorFlow 2.9.2.

Setup

Before running the code, make sure you have the required dependencies installed. You can install them using the following command:

pip install tensorflow

Usage Import the TensorFlow library and check the version:

import tensorflow as tf
print("TensorFlow version:", tf.__version__)

Expected output: TensorFlow version: 2.9.2

Load and preprocess the MNIST dataset:

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

Define the neural network model:

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10)
])

Make predictions and apply softmax:

predictions = model(x_train[:1]).numpy()
softmax_predictions = tf.nn.softmax(predictions).numpy()

Define the loss function:

loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

Calculate the loss for a sample:

loss = loss_fn(y_train[:1], predictions).numpy()

Compile the model:

model.compile(optimizer='adam',
              loss=loss_fn,
              metrics=['accuracy'])

Train the model:

model.fit(x_train, y_train, epochs=5)

Evaluate the model on the test set:

test_loss, test_accuracy = model.evaluate(x_test, y_test, verbose=2)

Create a probability model:

probability_model = tf.keras.Sequential([
  model,
  tf.keras.layers.Softmax()
])

Make predictions using the probability model:

prediction_probabilities = probability_model(x_test[:5])

License This project is licensed under the MIT License - see the LICENSE file for details. Feel free to modify the content according to your needs, and make sure to update any necessary links or additional information.