【PYTHON OPENCV】Training a linear regression model using TensorFlow

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Training a linear regression model using TensorFlow """ # Import required packages: import numpy as np import tensorflow as tf import matplotlib.pyplot as plt # Path to the folder that we want to save the logs for Tensorboard: logs_path = "./logs" # 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) # You can check the shape of the created training data: print(x.shape) print(y.shape) # Create the placeholders in order to feed our training examples into the optimizer while training: X = tf.placeholder("float", name='X') Y = tf.placeholder("float", name='Y') # Declare two trainable TensorFlow Variables for the Weights and Bias # We are going to initialize them randomly. Another way can be to set '0.0': W = tf.Variable(np.random.randn(), name="W") b = tf.Variable(np.random.randn(), name="b") # Define the hyperparameters of the model: learning_rate = 0.01 training_epochs = 1000 # This will be used to show results after every 25 epochs: disp_step = 100 # Construct a linear model: y_model = tf.add(tf.multiply(X, W), b, name="y_model") # Define cost function, in this case, the Mean squared error # (Note that other cost functions can be defined) cost = tf.reduce_sum(tf.pow(y_model - Y, 2)) / (2 * N) # Create the gradient descent optimizer that is going to minimize the cost function modifying the # values of the variables W and b: optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Initialize all variables: init = tf.global_variables_initializer() # Create a Saver object: saver = tf.train.Saver() # Start the training procedure inside a TensorFlow Session: with tf.Session() as sess: # Run the initializer: sess.run(init) # Uncomment if you want to see the created graph # summary_writer = tf.summary.FileWriter(logs_path, sess.graph) # Iterate over all defined epochs: for epoch in range(training_epochs): # Feed each training data point into the optimizer: for (_x, _y) in zip(x, y): sess.run(optimizer, feed_dict={X: _x, Y: _y}) # Display the results every 'display_step' epochs: if (epoch + 1) % disp_step == 0: # Calculate the actual cost, W and b: c = sess.run(cost, feed_dict={X: x, Y: y}) w_est = sess.run(W) b_est = sess.run(b) print("epoch {}: cost = {} W = {} b = {}".format(epoch + 1, c, w_est, b_est)) # Save the final model saver.save(sess, './linear_regression') # Storing necessary values to be used outside the session training_cost = sess.run(cost, feed_dict={X: x, Y: y}) weight = sess.run(W) bias = sess.run(b) print("Training finished!") # Calculate the predictions: predictions = weight * x + bias # Create the dimensions of the figure and set title: fig = plt.figure(figsize=(8, 5)) plt.suptitle("Linear regression using TensorFlow", 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()