with tf.Session() as sess: sess.run(init) # run the training part # ===================== print('Begin training: ', datetime.now()) # retrieve training set trainSet = cf.ipss_app.getTrainSet(train_points) train_x, train_y = cf.transfer2PyArrays(trainSet) #print2DArray(train_x, 'train_xSet', 'train_x') #print2DArray(train_y, 'train_ySet', 'train_y') train_x, aver_x, ran_x = cf.normalization(train_x) train_y, aver_y, ran_y = cf.normalization(train_y) # run the training part for i in range(1000): if (i % 100 == 0): print('Training step: ', i) sess.run(train, {x: train_x, y: train_y}) print('End training: ', datetime.now()) # run the verification part # ========================= # retrieve a test case # retrieve a test case
with tf.Session() as sess: sess.run(init) # run the training part # ===================== print('Begin training: ', datetime.now()) # retrieve training set trainSet = cf.ipss_app.getTrainSet(train_points) train_x, train_y = cf.transfer2PyArrays(trainSet) #print2DArray(train_x, 'train_xSet', 'train_x') #print2DArray(train_y, 'train_ySet', 'train_y') train_x, aver_x, ran_x = cf.normalization(train_x) train_y, aver_y, ran_y = cf.normalization(train_y) # run the training part for i in range(1000): if (i % 100 == 0): print('Training step: ', i) sess.run(train, {x: train_x, y: train_y}) print('End training: ', datetime.now()) # run the verification part # ========================= # retrieve a test case # retrieve a test case