CNN-RNN

# Import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, SimpleRNN
from tensorflow.keras.utils import to_categorical
from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix
import seaborn as sns
from brian2 import *

# Load MNIST dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Preprocess the data
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1).astype('float32') / 255
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1).astype('float32') / 255
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

num_classes = 10

# Define the CNN model
cnn_model = Sequential([
    Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)),
    MaxPooling2D(pool_size=(2, 2)),
    Flatten(),
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')
])

cnn_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the CNN model
cnn_history = cnn_model.fit(x_train, y_train, batch_size=128, epochs=20, validation_split=0.1)

# Evaluate the CNN model
cnn_loss, cnn_accuracy = cnn_model.evaluate(x_test, y_test)
cnn_predictions = cnn_model.predict(x_test)
cnn_predictions = np.argmax(cnn_predictions, axis=1)
cnn_true_labels = np.argmax(y_test, axis=1)

cnn_precision = precision_score(cnn_true_labels, cnn_predictions, average='weighted')
cnn_recall = recall_score(cnn_true_labels, cnn_predictions, average='weighted')
cnn_f1 = f1_score(cnn_true_labels, cnn_predictions, average='weighted')
cnn_conf_matrix = confusion_matrix(cnn_true_labels, cnn_predictions)

print(f'CNN Test Accuracy: {cnn_accuracy}')
print(f'CNN Precision: {cnn_precision}')
print(f'CNN Recall: {cnn_recall}')
print(f'CNN F1 Score: {cnn_f1}')

# Plot confusion matrix for CNN
plt.figure(figsize=(8, 6))
sns.heatmap(cnn_conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=range(num_classes), yticklabels=range(num_classes))
plt.title('Confusion Matrix - CNN')
plt.xlabel('Predicted')
plt.ylabel('True')
plt.show()

# Define the RNN model
rnn_model = Sequential([
    SimpleRNN(128, input_shape=(28, 28), activation='relu', return_sequences=False),
    Dense(10, activation='softmax')
])

rnn_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the RNN model
rnn_history = rnn_model.fit(x_train, y_train, batch_size=128, epochs=20, validation_split=0.1)

# Evaluate the RNN model
rnn_loss, rnn_accuracy = rnn_model.evaluate(x_test, y_test)
rnn_predictions = rnn_model.predict(x_test)
rnn_predictions = np.argmax(rnn_predictions, axis=1)
rnn_true_labels = np.argmax(y_test, axis=1)

rnn_precision = precision_score(rnn_true_labels, rnn_predictions, average='weighted')
rnn_recall = recall_score(rnn_true_labels, rnn_predictions, average='weighted')
rnn_f1 = f1_score(rnn_true_labels, rnn_predictions, average='weighted')
rnn_conf_matrix = confusion_matrix(rnn_true_labels, rnn_predictions)

print(f'RNN Test Accuracy: {rnn_accuracy}')
print(f'RNN Precision: {rnn_precision}')
print(f'RNN Recall: {rnn_recall}')
print(f'RNN F1 Score: {rnn_f1}')

# Plot confusion matrix for RNN
plt.figure(figsize=(8, 6))
sns.heatmap(rnn_conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=range(num_classes), yticklabels=range(num_classes))
plt.title('Confusion Matrix - RNN')
plt.xlabel('Predicted')
plt.ylabel('True')
plt.show()

# Plot training history for CNN and RNN
plt.plot(cnn_history.history['accuracy'], label='CNN Train Accuracy')
plt.plot(cnn_history.history['val_accuracy'], label='CNN Validation Accuracy')
plt.plot(rnn_history.history['accuracy'], label='RNN Train Accuracy')
plt.plot(rnn_history.history['val_accuracy'], label='RNN Validation Accuracy')
plt.title('CNN and RNN Model Training History')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()