lab_8

lab_2.py

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+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.models import Sequential, Model
+from tensorflow.keras.layers import Dense, SimpleRNN, Input, Add
+import matplotlib.pyplot as plt
+from sklearn.metrics import r2_score
+
+
+def generate_data(n_samples):
+    X = np.random.uniform(-5, 5, (n_samples, 2))
+    Y = X[:, 0] * np.sin(X[:, 1]) + X[:, 1] * np.cos(X[:, 0])
+    return X, Y
+
+# @tf.function()
+def train_and_evaluate(model, X_train, Y_train, X_test, Y_test, epochs=100, batch_size=10):
+    model.compile(optimizer='adam', loss='mse')
+    history = model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0, validation_data=(X_test, Y_test))
+    Y_pred = model.predict(X_test)
+    error = np.mean(np.abs((Y_test - Y_pred) / Y_test))
+    r2 = r2_score(Y_test, Y_pred)
+    return history, error, Y_pred, r2
+
+X, Y = generate_data(1000)
+X_train, X_test = X[:800], X[800:]
+Y_train, Y_test = Y[:800], Y[800:]
+
+inputs = Input(shape=(2,))
+hidden1 = Dense(20, activation='relu')(inputs)
+output = Dense(1)(hidden1)
+output_cascade = Dense(1)(inputs)
+final_output = Add()([output, output_cascade])
+cascade_model_1 = Model(inputs=inputs, outputs=final_output)
+
+inputs_2 = Input(shape=(2,))
+hidden1_2 = Dense(10, activation='relu')(inputs_2)
+hidden2_2 = Dense(10, activation='relu')(hidden1_2)
+output_2 = Dense(1)(hidden2_2)
+output_cascade_2 = Dense(1)(inputs_2)
+final_output_2 = Add()([output_2, output_cascade_2])
+cascade_model_2 = Model(inputs=inputs_2, outputs=final_output_2)
+
+models = {
+    "FeedForward (10 нейронів)": Sequential([Input(shape=(2,)), Dense(10, activation='relu'), Dense(1)]),
+    "FeedForward (20 нейронів)": Sequential([Input(shape=(2,)), Dense(20, activation='relu'), Dense(1)]),
+    "Cascade (20 нейронів)": cascade_model_1,
+    "Cascade (2x10 нейронів)": cascade_model_2,
+    "Elman (15 нейронів)": Sequential([Input((2, 1)), SimpleRNN(15, activation='relu'), Dense(1)]),
+    "Elman (3x5 нейронів)": Sequential([
+        Input((2, 1)), SimpleRNN(5, activation='relu', return_sequences=True),
+        SimpleRNN(5, activation='relu', return_sequences=True),
+        SimpleRNN(5, activation='relu'), Dense(1)
+    ])
+}
+
+errors = {}
+predictions = {}
+r2_scores = {}
+plt.figure(figsize=(10, 6))
+
+for name, model in models.items():
+    print(f"Навчання {name}...")
+    if "Elman" in name:
+        X_train_rnn = X_train.reshape((X_train.shape[0], X_train.shape[1], 1) )
+        X_test_rnn = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
+        history, error, Y_pred, r2 = train_and_evaluate(model, X_train_rnn, Y_train, X_test_rnn, Y_test)
+    else:
+        history, error, Y_pred, r2 = train_and_evaluate(model, X_train, Y_train, X_test, Y_test)
+
+    errors[name] = error
+    predictions[name] = Y_pred
+    r2_scores[name] = r2
+    plt.plot(history.history['loss'], label=name)
+
+plt.title("Зміна MSE під час навчання")
+plt.xlabel("Епохи")
+plt.ylabel("MSE")
+plt.legend()
+plt.show()
+
+plt.figure(figsize=(10, 5))
+for name, Y_pred in predictions.items():
+    plt.scatter(Y_test, Y_pred, label=name, alpha=0.5)
+    plt.plot(Y_test, Y_test, 'k--', label="Ідеальна лінія")
+    plt.xlabel("Реальні значення")
+    plt.ylabel("Передбачені значення")
+    plt.legend()
+    plt.title("Порівняння передбачень різних моделей")
+    plt.show()
+
+for name, error in errors.items():
+    print(f"{name}: середня відносна помилка = {error:.4f}, R² = {r2_scores[name]:.4f}")
+
+

neuro-lab8

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+Subproject commit 47a5a196e26913986874387052be0a24e5544ee7