【PYTHON OPENCV】Testing a linear regression model using TensorFlow

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Testing a linear regression model using TensorFlow """ # Import required packages: import numpy as np import tensorflow as tf import matplotlib.pyplot as plt # Number of points: N = 50 # Make random numbers predictable: np.random.seed(101) tf.set_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) # 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) # Restore the model. # First step when loading a model is to load the graph from '.meta': tf.reset_default_graph() imported_meta = tf.train.import_meta_graph("linear_regression.meta") # The second step when loading a model is to load the values of the variables: # Note that values only exist within a session with tf.Session() as sess: imported_meta.restore(sess, './linear_regression') # Run the model to get the values of the variables W, b and new prediction values: W_estimated = sess.run('W:0') b_estimated = sess.run('b:0') new_predictions = sess.run(['y_model:0'], {'X:0': new_x}) # Reshape for proper visualization: new_predictions = np.reshape(new_predictions, (M, -1)) # Calculate the predictions: predictions = W_estimated * x + b_estimated # Create the dimensions of the figure and set title: fig = plt.figure(figsize=(12, 5)) plt.suptitle("Linear regression using TensorFlow", 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") plt.legend() # 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()