python sklearn库 rnn_如何使用Tensorflow计算RNN和LSTM模型的AUC并生成ROC曲线？

我使用一个自定义的预定义函数trainDNN运行RNN和LSTM模型import tensorflow as tf

from tensorflow.contrib.layers import fully_connected

import h5py

import time

from sklearn.utils import shuffle

def trainDNN(path, n_days, n_features, n_neurons,

train_sequences, train_lengths, train_y,

test_sequences, test_y, test_lengths,

lstm=False, n_epochs=50, batch_size=256,

learning_rate=0.0003, TRAIN_REC=8, TEST_REC=8):

# we're doing binary classification

n_outputs = 2

# this is the initial learning rate

# adam optimzer decays the learning rate automatically

# learning_rate = 0.0001

#learning rate decay is determined by epsilon

epsilon = 0.001

# setup the graph

tf.reset_default_graph()

# inputs to the network

X = tf.placeholder(tf.float32, [None, n_days, n_features])

y = tf.placeholder(tf.int32, [None])

seq_length = tf.placeholder(tf.int32, [None])

# the network itself

cell = tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons) if lstm else tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)

outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32, sequence_length=seq_length)

logits = fully_connected(states[-1] if lstm else sta