Deep Learning Neural Network Model

 

Expt No: 12                                         Deep Learning Neural Network Model

Date:

 

Aim: To write a program to demonstrate deep learning neural network model

 

Program

import numpy as np

import tensorflow as tf

from tensorflow.keras.preprocessing.image import ImageDataGenerator

from tensorflow.keras.models import Sequential, load_model

from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Input

from tensorflow.keras.preprocessing import image

import matplotlib.pyplot as plt

import matplotlib.image as mpimg

 

# Set random seed for reproducibility

np.random.seed(42)

tf.random.set_seed(42)

 

# Path to the Fruits 360 dataset

train_dir = 'Fruits360/Training'

test_dir = 'Fruits360/Test'

 

# Define image size and batch size

img_width, img_height = 100, 100

batch_size = 32

 

# Data augmentation and normalization

train_datagen = ImageDataGenerator(

    rescale=1./255,

    shear_range=0.2,

    zoom_range=0.2,

    horizontal_flip=True)

 

test_datagen = ImageDataGenerator(rescale=1./255)

 

train_generator = train_datagen.flow_from_directory(

    train_dir,

    target_size=(img_width, img_height),

    batch_size=batch_size,

    class_mode='categorical')

 

test_generator = test_datagen.flow_from_directory(

    test_dir,

    target_size=(img_width, img_height),

    batch_size=batch_size,

    class_mode='categorical')

 

# Build CNN model

model = Sequential()

 

model.add(Input(shape=(img_width, img_height, 3)))

 

model.add(Conv2D(32, (3, 3), activation='relu'))

model.add(MaxPooling2D((2, 2)))

 

model.add(Conv2D(64, (3, 3), activation='relu'))

model.add(MaxPooling2D((2, 2)))

 

model.add(Conv2D(128, (3, 3), activation='relu'))

model.add(MaxPooling2D((2, 2)))

 

model.add(Flatten())

 

model.add(Dense(128, activation='relu'))

model.add(Dense(train_generator.num_classes, activation='softmax'))

 

# Compile the model

model.compile(optimizer='adam',

              loss='categorical_crossentropy',

              metrics=['accuracy'])

 

# Train the model

model.fit(

    train_generator,

    steps_per_epoch=train_generator.samples // batch_size,

    epochs=10,

    validation_data=test_generator,

    validation_steps=test_generator.samples // batch_size)

 

# Save the trained model

model.save('fruits_cnn_model.keras')

 

# Evaluate the model on the testing data

test_loss, test_accuracy = model.evaluate(test_generator)

print("Test Loss:", test_loss)

print("Test Accuracy:", test_accuracy)

 

# Function to load the model

def load_trained_model(model_path='fruits_cnn_model.keras'):

    global model

    model = load_model(model_path)

 

# Load the model

load_trained_model()

 

# Function to predict fruit from an image

def predict_fruit(image_path):

    # Load and preprocess the image

    img = image.load_img(image_path, target_size=(img_width, img_height))

    img_array = image.img_to_array(img)

    img_array = np.expand_dims(img_array, axis=0) / 255.

 

    # Predict the class of the fruit

    prediction = model.predict(img_array)

    predicted_class = np.argmax(prediction, axis=1)

 

    # Get the class label

    class_labels = train_generator.class_indices

    reverse_class_labels = {v: k for k, v in class_labels.items()}

    predicted_fruit = reverse_class_labels[predicted_class[0]]

 

    return predicted_fruit

 

# Example usage

image_path = 'Fruits/L1.jpeg'

predicted_fruit = predict_fruit(image_path)

 

# Display the image

img = mpimg.imread(image_path)

plt.imshow(img)

plt.axis('off')

plt.show()

 

# Print the identified fruit name

print("Identified fruit:", predicted_fruit)

 

Result: Thus the program to demonstrate deep learning neural network model was written and executed.

 

Sample Output

Found 981 images belonging to 3 classes.

Found 332 images belonging to 3 classes.

Epoch 1/10

30/30 ━━━━━━━━━━━━━━━━━━━━ 32s 866ms/step - accuracy: 0.7610 - loss: 0.5012 - val_accuracy: 1.0000 - val_loss: 2.0452e-07

Epoch 2/10

30/30 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 1.0000 - loss: 2.3431e-05 - val_accuracy: 1.0000 - val_loss: 0.0000e+00

Epoch 3/10

30/30 ━━━━━━━━━━━━━━━━━━━━ 15s 441ms/step - accuracy: 1.0000 - loss: 7.7499e-06 - val_accuracy: 1.0000 - val_loss: 0.0000e+00

Epoch 4/10

30/30 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 1.0000 - loss: 2.9802e-08 - val_accuracy: 1.0000 - val_loss: 0.0000e+00

Epoch 5/10

30/30 ━━━━━━━━━━━━━━━━━━━━ 15s 436ms/step - accuracy: 1.0000 - loss: 3.5505e-07 - val_accuracy: 1.0000 - val_loss: 0.0000e+00

Epoch 6/10

30/30 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 1.0000 - loss: 1.1176e-08 - val_accuracy: 1.0000 - val_loss: 0.0000e+00

Epoch 7/10

30/30 ━━━━━━━━━━━━━━━━━━━━ 15s 438ms/step - accuracy: 1.0000 - loss: 1.1372e-06 - val_accuracy: 1.0000 - val_loss: 1.4901e-09

Epoch 8/10

30/30 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - accuracy: 1.0000 - loss: 1.8104e-06 - val_accuracy: 1.0000 - val_loss: 0.0000e+00

Epoch 9/10

30/30 ━━━━━━━━━━━━━━━━━━━━ 15s 436ms/step - accuracy: 1.0000 - loss: 6.8103e-06 - val_accuracy: 1.0000 - val_loss: 0.0000e+00

Epoch 10/10

30/30 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 1.0000 - loss: 5.6766e-09 - val_accuracy: 1.0000 - val_loss: 0.0000e+00

11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 112ms/step - accuracy: 1.0000 - loss: 0.0000e+00

Test Loss: 0.0

Test Accuracy: 1.0

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 219ms/step

 

Identified fruit: Banana