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106c9f7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | import os
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from scikeras.wrappers import KerasRegressor
from model_creator import create_model3
# ===== Load Dataset =====
concrete_data = pd.read_csv("data/Concrete_Data_Yeh.csv")
X = concrete_data.iloc[:, :-1].values
y = concrete_data.iloc[:, -1].values
# ===== Standardize =====
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
print("===== ===== ===== ===== =====")
# Define the best-known hyperparameters manually
model3_params = {
'dropout_rate': 0.0,
'momentum': 0.7,
}
# Build the model using the best params
model3 = KerasRegressor(
model=create_model3,
epochs=50,
batch_size=32,
verbose=3,
**model3_params
)
# Split and train
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
history = model3.fit(X_train, y_train, validation_data=(X_test, y_test))
# Plot training and validation loss
plt.figure(figsize=(10, 6))
plt.plot(history.history_['loss'], label='Train MSE')
plt.plot(history.history_['val_loss'], label='Test MSE')
plt.title('MSE Loss Over Epochs (Best Model)')
plt.xlabel('Epoch')
plt.ylabel('MSE Loss')
plt.legend()
plt.grid(True)
plt.tight_layout()
# Save the plot to outputs folder
# plt.savefig("outputs/model3_loss_plot.png")
# plt.show()
# Predict on test set
y_train_pred = model3.predict(X_train)
y_test_pred = model3.predict(X_test)
# Calculate MSE
train_mse = mean_squared_error(y_train, y_train_pred)
test_mse = mean_squared_error(y_test, y_test_pred)
print(f"Final Train MSE: {train_mse:.4f}")
print(f"Final Test MSE : {test_mse:.4f}")
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