[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

machine_learning/neural_networks/cnn_mnist.py

@@ -12,7 +12,6 @@
 
 """
 
-
 import tensorflow as tf
 from tensorflow.keras import layers, models
 from tensorflow.keras.datasets import mnist
@@ -22,8 +21,8 @@
 (X_train, y_train), (X_test, y_test) = mnist.load_data()
 
 # Normalize the pixel values (0 to 1)
-X_train = X_train.reshape(-1, 28, 28, 1).astype('float32') / 255
-X_test = X_test.reshape(-1, 28, 28, 1).astype('float32') / 255
+X_train = X_train.reshape(-1, 28, 28, 1).astype("float32") / 255
+X_test = X_test.reshape(-1, 28, 28, 1).astype("float32") / 255
 
 # Convert labels to one-hot encoding
 y_train = to_categorical(y_train, 10)
@@ -33,38 +32,40 @@
 model = models.Sequential()
 
 # 1st Convolutional Layer
-model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
+model.add(layers.Conv2D(32, (3, 3), activation="relu", input_shape=(28, 28, 1)))
 model.add(layers.MaxPooling2D((2, 2)))
 model.add(layers.BatchNormalization())
 
 # 2nd Convolutional Layer
-model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.Conv2D(64, (3, 3), activation="relu"))
 model.add(layers.MaxPooling2D((2, 2)))
 model.add(layers.BatchNormalization())
 
 # 3rd Convolutional Layer
-model.add(layers.Conv2D(128, (3, 3), activation='relu'))
+model.add(layers.Conv2D(128, (3, 3), activation="relu"))
 model.add(layers.BatchNormalization())
 
 # Flattening the data before fully connected layers
 model.add(layers.Flatten())
 
 # Fully Connected (Dense) Layer with Dropout for regularization
-model.add(layers.Dense(128, activation='relu'))
+model.add(layers.Dense(128, activation="relu"))
 model.add(layers.Dropout(0.5))
 
 # Output Layer for classification
-model.add(layers.Dense(10, activation='softmax'))
+model.add(layers.Dense(10, activation="softmax"))
 
 # Compile the model
-model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
 
 # Display the model summary
 model.summary()
 
 # Train the model
-history = model.fit(X_train, y_train, epochs=5, batch_size=32, validation_data=(X_test, y_test))
+history = model.fit(
+    X_train, y_train, epochs=5, batch_size=32, validation_data=(X_test, y_test)
+)
 
 # Evaluate the model on test data
 test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
-print(f'\nTest accuracy: {test_acc}')
+print(f"\nTest accuracy: {test_acc}")

machine_learning/neural_networks/fully_connected_mnist.py

@@ -11,7 +11,6 @@
 - Train the model, evaluate its accuracy and loss at each epoch, and predict sample outputs.
 """
 
-
 import tensorflow as tf
 from tensorflow.keras import layers, models
 import numpy as np
@@ -35,26 +34,28 @@
 model.add(layers.Flatten(input_shape=(28, 28)))
 
 # First hidden layer with 128 neurons and ReLU activation
-model.add(layers.Dense(128, activation='relu'))
+model.add(layers.Dense(128, activation="relu"))
 
 # Dropout layer to prevent overfitting (randomly drops 20% of neurons)
 model.add(layers.Dropout(0.2))
 
 # Second hidden layer with 64 neurons and ReLU activation
-model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dense(64, activation="relu"))
 
 # Output layer with 10 neurons (one for each digit class 0-9), softmax for probabilities
-model.add(layers.Dense(10, activation='softmax'))
+model.add(layers.Dense(10, activation="softmax"))
 
 # Compile the model
-model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+model.compile(
+    optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
+)
 
 # Train the model on the MNIST training data
 model.fit(X_train, y_train, epochs=5, batch_size=32)
 
 # Evaluate the model on the test set
 test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
-print(f'\nTest accuracy: {test_acc}')
+print(f"\nTest accuracy: {test_acc}")
 
 # Make a prediction on a random test image
 random_index = np.random.randint(0, len(X_test))
@@ -63,4 +64,4 @@
 predicted_digit = np.argmax(prediction)
 
 # Print the predicted result and actual label
-print(f'Predicted digit: {predicted_digit}, Actual digit: {y_test[random_index]}')
+print(f"Predicted digit: {predicted_digit}, Actual digit: {y_test[random_index]}")