Mastering TensorFlow 1.x

Ridge regularization

The following is the complete code for ridge regularized regression to train the model in order to predict Boston house pricing:

num_outputs = y_train.shape[1]num_inputs = X_train.shape[1]x_tensor = tf.placeholder(dtype=tf.float32,                          shape=[None, num_inputs], name='x')y_tensor = tf.placeholder(dtype=tf.float32,                          shape=[None, num_outputs], name='y')w = tf.Variable(tf.zeros([num_inputs, num_outputs]),                dtype=tf.float32, name='w')b = tf.Variable(tf.zeros([num_outputs]),                dtype=tf.float32, name='b')model = tf.matmul(x_tensor, w) + bridge_param = tf.Variable(0.8, dtype=tf.float32)ridge_loss = tf.reduce_mean(tf.square(w)) * ridge_paramloss = tf.reduce_mean(tf.square(model - y_tensor)) + ridge_losslearning_rate = 0.001optimizer = ...