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