【PYTHON OPENCV】Training a linear regression model using Keras

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Training 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 # 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): """Get weights of w and b""" w = model.get_weights()[0][0][0] b = model.get_weights()[1][0] return w, 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() # Show weights at beginning (initialization values): w_init, b_init = get_weights(linear_reg_model) print('Linear regression model is initialized with weights w: {}, b: {}'.format(w_init, b_init)) # Feed the data using fit function: linear_reg_model.fit(x, y, epochs=100, validation_split=0.2, verbose=1) # Show weights when the training is done (learned parameters): w_final, b_final = get_weights(linear_reg_model) print('Linear regression model is trained with weights w: {}, b: {}'.format(w_final, b_final)) # Calculate the predictions: predictions = w_final * x + b_final # Saving weights: linear_reg_model.save_weights("my_model.h5") # Create the dimensions of the figure and set title: fig = plt.figure(figsize=(8, 5)) plt.suptitle("Linear regression using Keras", fontsize=14, fontweight='bold') fig.patch.set_facecolor('silver') # Plot training data: plt.subplot(1, 2, 1) plt.plot(x, y, 'ro', label='Original data') plt.xlabel('x') plt.ylabel('y') plt.title("Training Data") # Plot results: plt.subplot(1, 2, 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() # Show the Figure: plt.show()