train_x, train_y = cf.transfer2PyArrays(trainSet)
# run the training part
for i in range(cf.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
for factor in [
0.45, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.55
]:
#for factor in [0.45, 1.0, 1.55] :
testCase = cf.ipss_app.getTestCase(factor)
test_x, test_y = cf.transfer2PyArrays(testCase)
# compute model output (network voltage)
model_y = sess.run(nn_model(x), {x: test_x})
#printArray(model_y, 'model_y')
netVoltage = cf.transfer2JavaDblAry(model_y[0], size)
print('model out mismatch: ', cf.ipss_app.getMismatchInfo(netVoltage))
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
# =========================
testSize = 100
mismatchSet = np.zeros((testSize, 2))
# retrieve a test case
for i in range(testSize):
#for factor in [0.45, 1.0, 1.55] :
testCase = cf.ipss_app.getTestCase()
test_x, test_y = cf.transfer2PyArrays(testCase)
test_x = np.divide(np.subtract(test_x, aver_x), ran_x)
# compute model output (network voltage)
model_y = sess.run(nn_model(x), {x: test_x})
#printArray(model_y, 'model_y')
netVoltage = cf.transfer2JavaDblAry(model_y[0] * ran_y + aver_y, size)
mismatchSet[i] = np.array([
cf.ipss_app.getMismatch(netVoltage)[0],
cf.ipss_app.getMismatch(netVoltage)[1]
])
train_m, aver_m, ran_m = cf.normalization(mismatchSet)
print('model out mismatch(aver): ', aver_m)
print('model out mismatch(range): ', ran_m)