# Lab 4 Multi-variable linear regression import tensorflow as tf import numpy as np tf.set_random_seed(777) # for reproducibility xy = np.loadtxt('data-01-test-score.csv', delimiter=',', dtype=np.float32) x_data = xy[:, 0:-1] y_data = xy[:, [-1]] # Make sure the shape and data are OK print(x_data.shape, x_data, len(x_data)) print(y_data.shape, y_data) # placeholders for a tensor that will be always fed. X = tf.placeholder(tf.float32, shape=[None, 3]) Y = tf.placeholder(tf.float32, shape=[None, 1]) W = tf.Variable(tf.random_normal([3, 1]), name='weight') b = tf.Variable(tf.random_normal([1]), name='bias') # Hypothesis hypothesis = tf.matmul(X, W) + b # Simplified cost/loss function cost = tf.reduce_mean(tf.square(hypothesis - Y)) # Minimize optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5) train = optimizer.minimize(cost) # Launch the graph in a session. sess = tf.Session() # Initializes global variables in the graph. sess.run(tf.global_variables_initializer()) for step in range(2001): cost_val, hy_val, _ = sess.run( [cost, hypothesis, train], feed_dict={X: x_data, Y: y_data}) if step % 10 == 0: print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val) # Ask my score print("Your score will be ", sess.run( hypothesis, feed_dict={X: [[100, 70, 101]]})) print("Other scores will be ", sess.run(hypothesis, feed_dict={X: [[60, 70, 110], [90, 100, 80]]})) ''' Your score will be [[ 181.73277283]] Other scores will be [[ 145.86265564] [ 187.23129272]]
lab-04-3-file_input_linear_regression
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