【PYTHON OPENCV】Testing a linear regression model using Keras

 """

Testing a linear regression model using Keras """ # Import required packages from keras.models import Sequential from keras.layers import Dense from keras.optimizers import Adam import numpy as np import matplotlib.pyplot as plt # Number of points: N = 50 # Number of points to predict: M = 3 # Define 'M' more points to get the predictions using the trained model: new_x = np.linspace(N + 1, N + 10, M) # Make random numbers predictable: np.random.seed(101) # Generate random data composed by 50 (N = 50) points: x = np.linspace(0, N, N) y = 3 * np.linspace(0, N, N) + np.random.uniform(-10, 10, N) def get_weights(model): m = model.get_weights()[0][0][0] b = model.get_weights()[1][0] return m, b def create_model(): """Create the model using Sequential model""" # Create a sequential model: model = Sequential() # All we need is a single connection so we use a Dense layer with linear activation: model.add(Dense(input_dim=1, units=1, activation="linear", kernel_initializer="uniform")) # Compile the model defining mean squared error(mse) as the loss model.compile(optimizer=Adam(lr=0.1), loss='mse') # Return the created model return model # Get the created model linear_reg_model = create_model() # Load weights: linear_reg_model.load_weights('my_model.h5') # Show weights when the training is done (learned parameters): m_final, b_final = get_weights(linear_reg_model) print('Linear regression model is trained with weights w: {}, b: {}'.format(m_final, b_final)) # Get the predictions of the training data: predictions = linear_reg_model.predict(x) # Get new predictions: new_predictions = linear_reg_model.predict(new_x) # Create the dimensions of the figure and set title: fig = plt.figure(figsize=(12, 5)) plt.suptitle("Linear regression using Keras", fontsize=14, fontweight='bold') fig.patch.set_facecolor('silver') # Plot training data: plt.subplot(1, 3, 1) plt.plot(x, y, 'ro', label='Original data') plt.xlabel('x') plt.ylabel('y') plt.title("Training Data") # Plot results: plt.subplot(1, 3, 2) plt.plot(x, y, 'ro', label='Original data') plt.plot(x, predictions, label='Fitted line') plt.xlabel('x') plt.ylabel('y') plt.title('Linear Regression Result') plt.legend() # Plot new predicted data: plt.subplot(1, 3, 3) plt.plot(x, y, 'ro', label='Original data') plt.plot(x, predictions, label='Fitted line') plt.plot(new_x, new_predictions, 'bo', label='New predicted data') plt.xlabel('x') plt.ylabel('y') plt.title('Predicting new points') plt.legend() # Show the Figure: plt.show()