Question

我的神经网络回归模型预测所有测试样本的一个值。使用epochs，batch_size，图层数，隐藏单位，学习率等超参数只会将预测值更改为新常量。

对于测试，如果我测试训练数据本身，我会得到几乎准确的结果和RMSE~1。

注意：任务是根据其运行至故障时间序列数据预测机器的剩余寿命。我使用tsfresh库从原始时间序列数据生成1045个特征，只有24个特征。

应该导致此行为的原因是什么？我应该如何可视化神经网络模型开发以确保事情朝着正确的方向发展？

print "Shape of training_features is", train_X.shape
print "Shape of train_labels is", train_Y.shape
print "Shape of test_features is", test_X.shape
print "shape of test_labels is", test_Y.shape

input_dim = train_X.shape[1]
# Function to create model, required for KerasRegressor
def create_model(h1=50, h2=50, act1='sigmoid', act2='sigmoid', init='he_normal', learn_rate=0.001, momentum=0.1, loss='mean_squared_error'):
    # create model
    model = Sequential()
    model.add(Dense(h1, input_dim=input_dim, init=init, activation=act1))
    model.add(Dense(h2, init=init, activation=act2))
    model.add(Dense(1, init=init))
    # Compile model
    optimizer = SGD(lr=learn_rate, momentum=momentum)
    model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy'])
    return model

''' THE REAL THING '''
# create model
model = KerasRegressor(build_fn=create_model, verbose=0)

# SCORING FUNCTION
grid_scorer = make_scorer(mean_squared_error, greater_is_better=False)
# Grid Search
batch_size = [8]
epochs = [500]
init_mode = ['glorot_uniform']
learn_rate = [0.0001]
momentum = [0.1]

hidden_layer_1 = [75]
activation_1 = ['sigmoid']
hidden_layer_2 = [15]
activation_2 = ['sigmoid']

param_grid = dict(batch_size=batch_size, nb_epoch=epochs, init=init_mode, h1=hidden_layer_1, h2=hidden_layer_2, act1 = activation_1, act2=activation_2, learn_rate=learn_rate, momentum=momentum)

print "\n...BEGIN SEARCH..."
grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, scoring=grid_scorer, verbose=1)

print "\nLet's fit the training data..."
grid_result = grid.fit(train_X, train_Y)

# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
    print("%f (%f) with: %r" % (mean, stdev, param))

predicted = grid.predict(test_X)  
print "\nPrediction array is\n", predicted
rmse = numpy.sqrt(((predicted - test_Y) ** 2).mean(axis=0))
print "Test RMSE is", rmse

输出：

Shape of training_features is (249, 1045)
Shape of train_labels is (249,)
Shape of test_features is (248, 1045)
shape of test_labels is (248,)

...BEGIN SEARCH...

Let's fit the training data...
Fitting 5 folds for each of 1 candidates, totalling 5 fits
Best: -891.761863 using {'learn_rate': 0.0001, 'h2': 15, 'act1': 'sigmoid', 'act2': 'sigmoid', 'h1': 75, 'batch_size': 8, 'init': 'glorot_uniform', 'nb_epoch': 500, 'momentum': 0.1}
-891.761863 (347.253351) with: {'learn_rate': 0.0001, 'h2': 15, 'act1': 'sigmoid', 'act2': 'sigmoid', 'h1': 75, 'batch_size': 8, 'init': 'glorot_uniform', 'nb_epoch': 500, 'momentum': 0.1}

Prediction array is
[ 295.72067261  295.72067261  295.72067261  295.72067261  295.72067261
  295.72067261  295.72067261  ...
                              295.72067261  295.72067261  295.72067261
  295.72067261  295.72067261  295.72067261]
Test RMSE is 95.0019297411

Answer 1

您应该尝试扩展数据。当要素未缩放时，通常会发生这种情况。

神经网络回归预测所有测试样本的值相同

1 个答案: