import tensorflow as tf from tensorflow.keras import layers, models, optimizers, regularizers from tensorflow.keras.utils import to_categorical from tensorflow.keras.datasets import cifar100 import matplotlib.pyplot as plt (x_train, y_train), (x_val, y_val) = cifar100.load_data() x_train , x_val = x_train/255.0, x_val/255.0 y_train, y_val = to_categorical(y_train,100), to_categorical(y_val,100) x_train.shape y_train.shape x_train = x_train.reshape((x_train.shape[0], 32, 32 3)) x_val = x_val.reshape((x_val.shape[0], 32, 32 3)) model = models.Sequential() model.add(layers.Conv2D(32,(3,3), activation='relu',input_shape=(32,32,3))) model.add(layers.MaxPooling2D(2,2)) model.add(layers.Flatten()) model.add(layers.Dense(100, activation = 'softmax')) model.summary() model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy']) a = model.fit(x_train, y_train, epochs=1, validation_data=(x_val,y_val), verbose=2) print("Training Accuracy:", a.history['accuracy']) print("Validation Accuracy:", a.history['val_accuracy'])

Print training and validation loss

print("Training Loss:", a.history['loss']) print("Validation Loss:", a.history['val_loss']) plt.plot(a.history['accuracy'], label='accuracy') plt.plot(a.history['val_accuracy'], label='val_accuracy') plt.xlabel('epochs') plt.ylabel('accuracy') plt.legend() plt.show() plt.plot(a.history['loss'], label='loss') plt.plot(a.history['val_loss'], label='val_loss') plt.xlabel('epochs') plt.ylabel('loss') plt.legend() plt.show() import numpy as np

y_pred = model.predict(x_val) y_pred_classes = np.argmax(y_pred, axis=1) y_true_classes = np.argmax(y_val, axis=1)

Calculate the confusion matrix

cm = confusion_matrix(y_true_classes, y_pred_classes)

Print the confusion matrix

print("Confusion Matrix:") print(cm)

Sample images: import tensorflow as tf import matplotlib.pyplot as plt

Load MNIST dataset

(x_train, y_train), (_, _) = tf.keras.datasets.mnist.load_data()

Display sample images

num_samples = 5 plt.figure(figsize=(10, 2)) for i in range(num_samples): plt.subplot(1, num_samples, i + 1) plt.imshow(x_train[i], cmap='gray') # Use 'gray' colormap for grayscale images plt.title(str(y_train[i])) plt.axis('off')

plt.show()

import tensorflow as tf import matplotlib.pyplot as plt

Load CIFAR-100 dataset

(x_train, y_train), (_, _) = tf.keras.datasets.cifar100.load_data(label_mode='fine')

Display sample images

num_samples = 5 plt.figure(figsize=(10, 2)) for i in range(num_samples): plt.subplot(1, num_samples, i + 1) plt.imshow(x_train[i]) plt.title(str(y_train[i])) plt.axis('off')

plt.show()

import tensorflow as tf import matplotlib.pyplot as plt

Load CIFAR-10 dataset

(x_train, y_train), (_, _) = tf.keras.datasets.cifar10.load_data()

Define class labels

class_labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

Display sample images

num_samples = 5 plt.figure(figsize=(10, 2)) for i in range(num_samples): plt.subplot(1, num_samples, i + 1) plt.imshow(x_train[i]) plt.title(class_labels[int(y_train[i])]) plt.axis('off')

plt.show()

import tensorflow as tf import matplotlib.pyplot as plt

Load MNIST dataset

(x_train, y_train), (_, _) = tf.keras.datasets.fashion_mnist.load_data()

Display sample images

num_samples = 5 plt.figure(figsize=(10, 2)) for i in range(num_samples): plt.subplot(1, num_samples, i + 1) plt.imshow(x_train[i], cmap='gray') # Use 'gray' colormap for grayscale images plt.title(str(y_train[i])) plt.axis('off')

plt.show()

Confusion Matrix: import numpy as np import sklearn.metrics import matplotlib.pyplot as plt from sklearn.metrics import classification_report

Evaluation metrics

test_loss, test_accuracy = model.evaluate(x_train, y_train) print('Test Loss:', test_loss) print('Test Accuracy:', test_accuracy)

predictions = model.predict(x_train) predicted_labels = np.argmax(predictions, axis=1) true_labels = np.argmax(y_train, axis=1)

confusion_matrix = tf.math.confusion_matrix(true_labels, predicted_labels) print('Confusion Matrix:') print(confusion_matrix)

precision = sklearn.metrics.precision_score(true_labels, predicted_labels, average='weighted') recall = sklearn.metrics.recall_score(true_labels, predicted_labels, average='weighted') f1_score = sklearn.metrics.f1_score(true_labels, predicted_labels, average='weighted')

print('Precision:', precision) print('Recall:', recall) print('F1 Score:', f1_score)

report = classification_report(true_labels, predicted_labels) print('Classification Report:') print(report)

Data Augmentation: import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Flatten, Dropout from tensorflow.keras.datasets import mnist from tensorflow.keras.preprocessing.image import ImageDataGenerator import numpy as np import sklearn.metrics import matplotlib.pyplot as plt

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

train_images, test_images = train_images / 255.0, test_images / 255.0

train_images = train_images[..., tf.newaxis] test_images = test_images[..., tf.newaxis]

data_augmentation_model = models.Sequential()

Add Convolutional layers

data_augmentation_model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1))) # Change input_shape based on your data data_augmentation_model.add(layers.MaxPooling2D((2, 2)))

data_augmentation_model.add(layers.Conv2D(64, (3, 3), activation='relu')) data_augmentation_model.add(layers.MaxPooling2D((2, 2)))

data_augmentation_model.add(layers.Conv2D(128, (3, 3), activation='relu')) data_augmentation_model.add(layers.MaxPooling2D((2, 2)))

Flatten the output and add Dense layers

data_augmentation_model.add(layers.Flatten()) data_augmentation_model.add(layers.Dense(128, activation='relu')) data_augmentation_model.add(layers.Dense(10, activation='softmax')) # Change the output size based on your problem

Compile the data augmentation model

data_augmentation_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Define data augmentation parameters using ImageDataGenerator

datagen = ImageDataGenerator( rotation_range=20, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True )

Fit the data augmentation model on the original training data

You can adjust batch_size and epochs as needed

history1 = data_augmentation_model.fit(datagen.flow(train_images, train_labels, batch_size=32), epochs=5,validation_data=(test_images, test_labels))

Evaluation metrics

test_loss, test_accuracy = data_augmentation_model.evaluate(test_images, test_labels) print('Test Loss:', test_loss) print('Test Accuracy:', test_accuracy)

Predictions and Metrics

predictions = data_augmentation_model.predict(test_images) predicted_labels = np.argmax(predictions, axis=1)

Note: test_labels contains the actual class labels directly, so there's no need for np.argmax here

true_labels = test_labels

confusion_matrix = tf.math.confusion_matrix(true_labels, predicted_labels) print('Confusion Matrix:') print(confusion_matrix)

precision = sklearn.metrics.precision_score(true_labels, predicted_labels, average='weighted') recall = sklearn.metrics.recall_score(true_labels, predicted_labels, average='weighted') f1_score = sklearn.metrics.f1_score(true_labels, predicted_labels, average='weighted')

print('Precision:', precision) print('Recall:', recall) print('F1 Score:', f1_score)

plt.figure(figsize=(12, 4))

plt.subplot(1, 2, 1) plt.plot(history1.history['accuracy']) plt.plot(history1.history['val_accuracy']) plt.title('Model Accuracy') plt.xlabel('Epochs') plt.ylabel('Accuracy') plt.legend(['Train', 'Validation'], loc='upper left')

plt.subplot(1, 2, 2) plt.plot(history1.history['loss']) plt.plot(history1.history['val_loss']) plt.title('Model Loss') plt.xlabel('Epochs') plt.ylabel('Loss') plt.legend(['Train', 'Validation'], loc='upper left')

plt.show()