| import tensorflow as tf | |
| from tensorflow.keras.models import Sequential | |
| from tensorflow.keras.layers import Dense | |
| import numpy as np | |
| class MyModel: | |
| def __init__(self): | |
| # Initialize the model | |
| self.model = Sequential([ | |
| Dense(16, activation='relu', input_shape=(10,)), # Adjust input_shape as needed | |
| Dense(8, activation='relu'), | |
| Dense(4, activation='relu'), | |
| Dense(2, activation='relu'), | |
| Dense(1) | |
| ]) | |
| self.model.compile(loss='mse', optimizer='adam', metrics=[tf.keras.metrics.MeanSquaredError()]) | |
| def load_model(self, path): | |
| # Load the model weights | |
| self.model.load_weights(path) | |
| def predict(self, input_data): | |
| # Make predictions | |
| input_data = np.array(input_data) | |
| return self.model.predict(input_data).tolist() | |