LOG = logging.getLogger('ANN Example')
def load_data(train_path='./data/data712pc_train.csv', test_path='./data/data712pc_test.csv'):
InputIndex = ['X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9', 'X10',
'X11', 'X12', 'X13', 'X14', 'X15']
OutputIndex = ['Y']
data_train = pd.read_csv(train_path)
train_X = data_train[InputIndex]
train_Y = data_train[OutputIndex]
data_test = pd.read_csv(test_path)
test_X = data_test[InputIndex]
test_Y = data_test[OutputIndex]
minmax = MinMaxScaler(feature_range=(0, 1))
X_train = minmax.fit_transform(train_X)
X_test = minmax.transform(test_X)
Y_train = minmax.fit_transform(train_Y)
Y_test = minmax.transform(test_Y)
return X_train, X_test, Y_train, Y_test
def default_params():
params = {
'activation': 'relu',
'optimizer': 'Adam',
'learning_rate': 0.001,
'momentum': 0.9
}
return params
def create_model(PARAMS):
layers = [
Dense(8, activation=PARAMS.get('activation')),
Dense(4, activation=PARAMS.get('activation')),
Dense(1, activation=PARAMS.get('activation'))
]
model = Sequential(layers)
if PARAMS.get('optimizer') == 'Adam':
optimizer = Adam(lr = PARAMS.get('learning_rate'))
elif PARAMS.get('optimizer') == 'SGD':
optimizer = SGD(lr = PARAMS.get('learning_rate'), momentum = PARAMS.get('momentum'))
else:
optimizer = RMSprop(lr = PARAMS.get('learning_rate'), momentum = PARAMS.get('momentum'))
model.compile(loss=keras.losses.mean_squared_error, optimizer=optimizer)
return model
def run(X_train, X_test, Y_train, Y_test, model):
model.fit(X_train, Y_train, batch_size=64, epochs=100, verbose=1)
loss = model.evaluate(X_test, Y_test, verbose=0)
y_pred = model.predict(X_test)
rmse = mean_squared_error(Y_test, y_pred) ** 0.5
print('The rmse of prediction is:', rmse)
nni.report_final_result(rmse)
if __name__ == '__main__':
X_train, X_test, Y_train, Y_test = load_data()
try:
# get parameters from tuner
RECEIVED_PARAMS = nni.get_next_parameter()
LOG.debug(RECEIVED_PARAMS)
PARAMS = default_params()
PARAMS.update(RECEIVED_PARAMS)
LOG.debug(PARAMS)
model = create_model(PARAMS)
run(X_train, X_test, Y_train, Y_test, model)
except Exception as exception:
LOG.exception(exception)
raise
Describe the issue:
I can use the source code to run nni on my computer,but when I was trying to modify the code to meet my needs,it occurred some mistakes. I referred some examples to modify my code(like regression/classification of sklearn,mnist-keras)，because the framework is different,when I first try to modify my code and use powershell to run it,it occurred a lot mistakes.
Today I modified my code once again,it could run successfully in pycharm,but it failed in powershell.
crying,I think I need some help.
Environment:
code
search space
config
some files
error
