如何在 TensorFlow 中获取 ROC_Curve 和混淆矩阵答案

【问题标题】：How to get ROC_Curve and Confusion Matrix in TensorFlow如何在 TensorFlow 中获取 ROC_Curve 和混淆矩阵
【发布时间】：2017-10-02 00:13:29
【问题描述】：

我正在尝试在 Tensorflow 中获取 roc_curve 和混淆矩阵。我使用了 sklearn.metrics 函数，但出现错误。我的代码如下：从 sklearn.metrics 导入 roc_curve, auc

n_inputs = x_train.shape[1]
n_hidden1 = 500
n_hidden2 = 200
n_outputs = 2
learning_rate = 0.01

X = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X")
y = tf.placeholder(tf.int64, shape=(None), name="y")

hidden1 = tf.layers.dense(X, n_hidden1, activation=None)
hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=None)
logits = tf.layers.dense(hidden2, n_outputs)

loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits))

training_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)

correct = tf.nn.in_top_k(logits, y, 1)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))

init = tf.global_variables_initializer()

n_epochs = 20

with tf.Session() as sess:
    init.run()
    for epoch in range(n_epochs):
        sess.run(training_op, feed_dict={X: x_train, y: y_train})
        acc_train = accuracy.eval(feed_dict={X: x_train, y: y_train})
    acc_test = accuracy.eval(feed_dict={X: x_test, y: y_test})
    print("Epoch:", epoch, "Train accuracy:", acc_train, "Test accuracy:", acc_test)

    y_score = np.array(logits)
    roc_curve(y_test, y_score)

我得到的错误如下：

TypeError: Singleton array array(<tf.Tensor 'dense_26/BiasAdd:0' shape=(?, 2) dtype=float32>, dtype=object) cannot be considered a valid collection.

任何帮助将不胜感激。谢谢！

【问题讨论】：

标签： tensorflow scikit-learn confusion-matrix auc

【解决方案1】：

当你打电话时

sess.run(training_op, feed_dict={X: x_train, y: y_train})

你需要请求网络返回logits张量的值，改成这样：

training_op_result, logits_result = sess.run([training_op, logits], feed_dict={X: x_train, y: y_train})
y_score = np.array(logits_result)
roc_curve(y_test, y_score)

张量是一个图形对象。您可以通过sess.run 访问张量或计算的值/结果。

【讨论】：