train_x, train_y = transfer2PyArrays(trainSet)
#print2DArray(train_x, 'train_xSet', 'train_x')
#print2DArray(train_y, 'train_ySet', 'train_y')
# run the training part
for i in range(train_steps):
if (i % 1000 == 0): print('Training step: ', i)
sess.run(train, {x: train_x, y: train_y})
print('End training: ', datetime.now())
#print('W1: ', sess.run(W1))
#print('b1: ', sess.run(b1))
# run the verification part
# =========================
for factor in [0.7, 1.0, 1.2]:
# retrieve a test case
testCase = ipss_app.getTestCase(factor)
test_x, test_y = transfer2PyArrays(testCase)
#printArray(test_x, 'test_x')
#printArray(test_y, 'test_y')
# compute model output (network voltage)
model_y = sess.run(nn_model(x), {x: [test_x]})
#printArray(model_y[0], 'model_y')
print('max error: ',
np.sqrt(np.max(np.abs(np.square(model_y - test_y)))))
#print2DArray(train_x, 'train_xSet', 'train_x')
#print2DArray(train_y, 'train_ySet', 'train_y')
# run the training part
for i in range(train_steps):
if (i % 1000 == 0): print('Training step: ', i)
sess.run(train, {x: train_x, y: train_y})
print('End training: ', datetime.now())
'''
print('W1: ', sess.run(W1))
print('b1: ', sess.run(b1))
'''
# run the verification part
# =========================
# retrieve a test case
testCase = ipss_app.getTestCase()
test_x, test_y = np.array([testCase])[0]
#printArray(test_x, 'test_x')
#printArray(test_y, 'test_y')
# compute model output (network voltage)
model_y = sess.run(nn_model(x), {x: [test_x]})
#printArray(model_y[0], 'model_y')
netVoltage = transfer2JavaDblAry(model_y[0], size)
print('model out mismatch: ', ipss_app.getMismatchInfo(netVoltage))