Neural Network Model

 

Expt No: 11                                         Neural Network Model

Date:

 

Aim: To write a program to demonstrate simple Neural Network model.

 

Program

import numpy as np

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

import tensorflow as tf

from tensorflow.keras import layers, models, Input

 

# Load the MNIST dataset

train_data = pd.read_csv('mnist_train.csv')

test_data = pd.read_csv('mnist_test.csv')

 

# Extract features and labels

X_train = train_data.drop('label', axis=1).values

y_train = train_data['label'].values

X_test = test_data.drop('label', axis=1).values

y_test = test_data['label'].values

 

# Normalize the features

scaler = StandardScaler()

X_train = scaler.fit_transform(X_train)

X_test = scaler.transform(X_test)

 

# Convert labels to one-hot encoded vectors

y_train = tf.keras.utils.to_categorical(y_train)

y_test = tf.keras.utils.to_categorical(y_test)

 

 

# Define the neural network architecture

inputs = Input(shape=(784,))

x = layers.Dense(128, activation='relu')(inputs)

outputs = layers.Dense(10, activation='softmax')(x)

 

model = models.Model(inputs=inputs, outputs=outputs)

 

# Compile the model

model.compile(optimizer='adam',

              loss='categorical_crossentropy',

              metrics=['accuracy'])

 

# Train the model and capture history

history = model.fit(X_train, y_train, epochs=10, batch_size=128, validation_split=0.1)

 

# Plot training and validation loss

plt.plot(history.history['loss'], label='Training Loss')

plt.plot(history.history['val_loss'], label='Validation Loss')

plt.xlabel('Epoch')

plt.ylabel('Loss')

plt.title('Training and Validation Loss')

plt.legend()

plt.show()

 

# Plot training and validation accuracy

plt.plot(history.history['accuracy'], label='Training Accuracy')

plt.plot(history.history['val_accuracy'], label='Validation Accuracy')

plt.xlabel('Epoch')

plt.ylabel('Accuracy')

plt.title('Training and Validation Accuracy')

plt.legend()

plt.show()

 

# Evaluate the model on the test set

*test_loss, test_accuracy = model.evaluate(X_test, y_test)

print(f'Test Accuracy: {test_accuracy:.4f}')

 

# Save the model

model.save('mnist_model.keras')

 

 

 

 

 

 

Result: Thus the program to demonstrate simple Neural Network model was written and executed.

 

 

 

 

 

Sample Output

Epoch 1/10

422/422 ━━━━━━━━━━━━━━━━━━━━ 26s 10ms/step - accuracy: 0.8402 - loss: 0.5427 - val_accuracy: 0.9618 - val_loss: 0.1429

 

Epoch 2/10

422/422 ━━━━━━━━━━━━━━━━━━━━ 3s 6ms/step - accuracy: 0.9613 - loss: 0.1285 - val_accuracy: 0.9690 - val_loss: 0.1144

 

Epoch 3/10

422/422 ━━━━━━━━━━━━━━━━━━━━ 5s 6ms/step - accuracy: 0.9759 - loss: 0.0826 - val_accuracy: 0.9710 - val_loss: 0.1069

 

Epoch 4/10

422/422 ━━━━━━━━━━━━━━━━━━━━ 2s 5ms/step - accuracy: 0.9833 - loss: 0.0571 - val_accuracy: 0.9745 - val_loss: 0.1028

 

Epoch 5/10

422/422 ━━━━━━━━━━━━━━━━━━━━ 2s 5ms/step - accuracy: 0.9894 - loss: 0.0397 - val_accuracy: 0.9750 - val_loss: 0.0975

 

Epoch 6/10

422/422 ━━━━━━━━━━━━━━━━━━━━ 3s 6ms/step - accuracy: 0.9919 - loss: 0.0303 - val_accuracy: 0.9765 - val_loss: 0.1026

Epoch 7/10

422/422 ━━━━━━━━━━━━━━━━━━━━ 3s 6ms/step - accuracy: 0.9945 - loss: 0.0233 - val_accuracy: 0.9762 - val_loss: 0.1061

 

Epoch 8/10

422/422 ━━━━━━━━━━━━━━━━━━━━ 5s 6ms/step - accuracy: 0.9961 - loss: 0.0176 - val_accuracy: 0.9757 - val_loss: 0.1038

 

Epoch 9/10

422/422 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.9969 - loss: 0.0136 - val_accuracy: 0.9758 - val_loss: 0.1071

 

Epoch 10/10

422/422 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.9984 - loss: 0.0097 - val_accuracy: 0.9767 - val_loss: 0.1134

 

 

 

 

313/313 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9711 - loss: 0.1274

Test Accuracy: 0.9738