AI开发之基础快速入门主流深度学习框架介绍Keras

import numpy as npimport tensorflow as tffrom tensorflow.keras import Sequentialfrom tensorflow.keras.layers import DenseRED    = "\033[31m"GREEN  = "\033[32m"YELLOW = "\033[33m"BOLD   = "\033[1m"END    = "\033[0m"# =========================================================================#    Construct traffic samples and standard data preprocessing#    Feature format: [Request frequency, 404 count, Is robot UA, Page size MB]#    Label definition: 0 = Normal user access, 1 = Malicious attack behavior# =========================================================================if __name__ == "__main__":    # Raw Web traffic feature matrix    X_data = np.array([        [2.1,  0.0,  0, 0.1],   # Normal traffic        [95.4, 42.0, 1, 8.5],   # Vulnerability scanning (Attack)        [4.5,  1.0,  0, 0.5],   # Normal traffic        [1.2,  0.0,  0, 0.0],   # Normal traffic        [88.0, 35.0, 1, 12.1],  # Credential stuffing (Attack)    ], dtype=np.float32)    # True black and white labels (Ground truth)    y_data = np.array([[0], [1], [0], [0], [1]], dtype=np.float32)    visitor_names = ["User A", "User B", "User C", "User D", "User E"]    # Feature normalization: Scale data to a uniform magnitude to prevent large numbers from dominating training    mean = X_data.mean(axis=0)    std  = X_data.std(axis=0) + 1e-8    X_data_scaled = (X_data - mean) / std    # =========================================================================    # 2) Build the neural network layer by layer (Keras Sequential base)    # =========================================================================    # Sequential acts as a linear stack of layers    model = Sequential([        # Layer 1 (Hidden Layer): 8 neurons, takes 4-dimensional input features, uses ReLU activation        Dense(units=8, input_dim=4, activation='relu', name="Hidden_Layer"),        # Layer 2 (Output Layer): 1 neuron, uses Sigmoid activation to output a risk probability between 0 and 1        Dense(units=1, activation='sigmoid', name="Output_Layer")    ])    # =========================================================================    # 3) Configure loss function, optimizer, and metrics    # =========================================================================    model.compile(        optimizer=tf.keras.optimizers.SGD(learning_rate=0.1), # Optimizer: Updates parameters based on gradients        loss='binary_crossentropy',                          # Loss function: Binary cross-entropy for binary classification        metrics=['accuracy']                                 # Metric to monitor during training    )    # =========================================================================    # 4) Start the training loop to iteratively optimize parameters    # =========================================================================    print("-" * 75)    print(f"{BOLD}{YELLOW}===== AI Threat Perception System: Training Keras Sequential Model ====={END}")    print("-" * 75)    print("Keras model initialized, AI pipeline training loop starting...")    # epochs=1000 means the model iterates over the 5 samples 1000 times    model.fit(X_data_scaled, y_data, epochs=1000, verbose=0)     print(f"\n{BOLD}{GREEN}Model training completed! Model parameters have successfully converged.{END}\n")    # =========================================================================    # 5) Online inference and real-time Web gateway interception    # =========================================================================    # Call the predict method to execute the forward pass through the network    probabilities = model.predict(X_data_scaled, verbose=0).flatten()    threshold = 0.5  # Risk control interception threshold    print("-" * 75)    print(f"{BOLD}{YELLOW}===== AI WAF Gateway: Real-Time Live Traffic Inspection Active ====={END}")    print("-" * 75)    for name, score in zip(visitor_names, probabilities):        if score > threshold:            action = f"{BOLD}{RED}[BLOCK & DROP] - Intercepted by AI Neural Network! (Risk Prob: {score*100:.2f}%){END}"        else:            action = f"{BOLD}{GREEN}[PASS] - Clear Traffic. (Risk Prob: {score*100:.2f}%){END}"        print(f"Live Flow: {name:<12} -> Predicted: {action}")    print("-" * 75)