import tensorflow as tf # Build your graph x = tf.constant([[37.0, -23.0], [1.0, 4.0]], name="inputs") w = tf.Variable(tf.random_uniform([2, 2]), name="weights") _y = tf.matmul(x, w, name="predict_y") y = tf.constant([[74.0, -46.0], [2.0, 8.0]], name="target_y") loss = tf.losses.mean_squared_error(y, _y, w) train_op = tf.train.AdagradOptimizer(0.01).minimize(loss) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) # 'sess.graph' provides access to the graph used in a 'tf.Session' writer = tf.summary.FileWriter("tmp/log/", sess.graph) # Perform your computation... for i in range(10000): _, loss_op = sess.run([train_op, loss]) print("The loss on step " + str(i) + " is " + str(loss_op)) if(loss_op<=0.1): break; writer.close()
