Question

我需要计算多变量RNN LSTM Keras模型的roc曲线。

这是我的数据集

（患者编号，以毫米/秒为单位的时间，X Y和Z的标准化，   分别为峰度，偏度，俯仰，滚动和偏航，标签。

1.15，-0.248010047716,0.00378335508419，-0.0152548459993，-86.3738760481,0.872322164158，-3.51314800063,0

1,31，-0.248010047716,0.00378335508419，-0.0152548459993，-86.3738760481,0.872322164158，-3.51314800063,0

1,46，-0.267422664673,0.0051143782875，-0.0191247001961，-85.7662354031,1.0928406847，-4.08015176908,0

1.62，-0.267422664673,0.0051143782875，-0.0191247001961，-85.7662354031,1.0928406847，-4.08015176908,0

这就是我的尝试：

n = x_train.shape[0]
n2 = x_test.shape[0]
L = 32
X_train_seq = []
Y_train_seq = []
for k in range(n - L + 1):
    X_train_seq.append(x_train[k : k + L])
    Y_train_seq.append(y_train[k : k + L])

X_test_seq = []
Y_test_seq = []
for k in range(n2 - L + 1):
    X_test_seq.append(x_test[k : k + L])
    Y_test_seq.append(y_test[k : k + L])

X_train_seq = np.array(X_train_seq)
Y_train_seq = np.array(Y_train_seq)
X_test_seq = np.array(X_test_seq)
Y_test_seq = np.array(Y_test_seq)

model = Sequential()
model.add(LSTM(64, input_shape=(32, 5),
               activation='tanh'))
model.add(Dense(32, activation='sigmoid' ))
model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

#ROC Curve
y_pred_keras = model.predict(X_test_seq, batch_size = 200).ravel()
fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, y_pred_keras)
#AUC
auc_keras = auc(fpr_keras, tpr_keras)

但它给了我以下错误：

ValueError: Found input variables with inconsistent numbers of samples: [502203, 16069504]

另一个问题是我的训练样本是1,415,684，测试是502,203

但是在打印以下x和y的形状时

X_train_seq = np.array(X_train_seq)
Y_train_seq = np.array(Y_train_seq)
X_test_seq = np.array(X_test_seq)
Y_test_seq = np.array(Y_test_seq)

它给我1,415,653个训练和502,172个测试

如何计算多变量RNN模型的ROC？

0 个答案: