损失函数不显示

Question

我想开始尝试神经网络，发现 keras 在语法上非常简单。我的设置是 X_train 是一个形状为 (3516, 6) 的数组 y_train 的形状为 (3516,)

X_train 看起来像这样：

[[ 888.          900.5         855.          879.311       877.00266667
   893.5008    ]
 [ 875.          878.5         840.          880.026       874.56933333
   890.7948    ]
 [ 860.          870.          839.5         880.746       870.54333333
   887.6428    ]....]

预测一个输出是6个金融数据的输入。我知道它不会准确，但至少在我开始使用 RNN 之前，它会让我继续做某事我的问题是每个时期的损失函数都显示为 nan，准确度显示为 0%，validation_accuracy 显示为零，就好像说数据甚至没有通过模型传递一样，我的意思是即使它是一个糟糕的模型输入甚至应该由大量损失表示，对吗？这是模型：（见下文）

无论如何，伙计们，我确定我做错了什么，非常感谢你们的意见非常感谢 S

编辑：完整的工作代码：

def load_data(keyword):

    df = pd.read_csv('%s_x.csv' %keyword)
    df2 = pd.read_csv('%s_y.csv' %keyword)

    df2 = df2['label']

    try:
        df.drop('Unnamed: 0', axis = 1, inplace=True)
    except:
        print('wouldnt let drop unnamed column')

    X = df.as_matrix()
    y = df2.as_matrix()

    X_len = len(X)
    test_size = 0.2
    test_split = int(test_size * X_len)
    X_train = X[:-test_split]
    y_train = y[:-test_split]

    X_test = X[-test_split:]
    y_test = y[-test_split:]

def keras():
    model = Sequential( [
        Dense(input_dim=3, output_dim=3),
        Dense(output_dim=60, activation='linear'),
        core.Dropout(p=0.1),
        Dense(60, activation='linear'),
        core.Dropout(p=0.1),
        Dense(1, activation='linear')
    ])
    return model


def training(epoch):
    #  start the program off by loading some data into it
    X_train, X_test, y_train, y_test = load_data('admiral')
    y_train = y_train.reshape(len(y_train), 1)
    y_test = y_test.reshape(len(y_test), 1)


    model = keras()


    # optimizer will go into the compile function
    # RMSpop is apparently a pretty decent choice for recurrent neural networks although we will start it on a simple nn too.
    rms = optimizers.RMSprop(lr=0.001, rho = 0.9, epsilon =1e-08)


    model.compile(optimizer= rms, loss='mean_squared_error ', metrics = ['accuracy'])
    model.fit(X_train, y_train, nb_epoch=epoch, batch_size =500, validation_split=0.01)

    score = model.evaluate(X_test, y_test, batch_size=50)
    print(score)

training(300)

Answer 1

准确率真的很低，因为显示准确率没有意义，对于回归问题，更适合分类

正在通过它的数据太少了，这是一个 Nan 问题的回答

损失函数不显示

Loss function not showing

python

neural-network

keras

recurrent-neural-network