如何在TensorFlow中获得ROC_Curve和混乱矩阵

我想在Tensorflow中获取roc_curve和混淆矩阵。我使用了sklearn.metrics函数，并且出现错误。我的代码如下：从sklearn.metrics进口roc_curve，AUC如何在TensorFlow中获得ROC_Curve和混乱矩阵

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)

我得到下面的错误是：

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！

来源

2017-05-04 Andy G

当你调用

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

您需要请求网络返回logits张量的值，将其更改为这样：

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。您通过sess.run访问张量或计算的值/结果。

来源

2017-05-04 02:57:34

如何在TensorFlow中获得ROC_Curve和混乱矩阵

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