用于多类分类的 Tensorflow 混淆矩阵

谢谢你的帮助。我正在为面部动作（例如扬起的眉毛、分开的嘴唇）编写一个多类二元分类器，我想制作一个混淆矩阵。有6个面部动作和593个样本。我收到此错误：我收到此错误：“形状 (?, 2, 6) must have rank 2”。从文档中， tf.confusion_matrix 采用一维向量，但我认为应该有一种方法可以根据TensorBoard 中的 Tensorflow Confusion Matrix 来调整来自 feed_dict 的输入数据，使其工作。标签和预测如下：

# Rows are samples, columns are classes, and the classes shows a facial
# action which is either 1 for detection or 0 for no detection. 
[[0, 0, 1, 0, 1, 0],
[1, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 1, 1],...]

我正在使用前馈 MLP，变量“pred”是预测，阈值强制选择 0 或 1。我尝试将预测和标签乘以 np.arange(1,7) 以获得正值匹配索引，但我被困在参数的形状上。

还有更多代码，但我正在展示我认为相关的内容。

sess = tf.Session()

x = tf.placeholder(tf.float32, [None, n_input], name = "x")
y = tf.placeholder(tf.float32, [None, n_output], name = "labels")

#2 fully connected layers
fc1 = fc_layer(x, n_input, n_hidden_1, "fc1")
relu = tf.nn.relu(fc1)
tf.summary.histogram("fc1/relu", relu)
logits = fc_layer(relu, n_hidden_1, n_output, "fc2")

# Calculate loss function
with tf.name_scope("xent"):
    xent = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=logits, labels=y, name="xent"))

with tf.name_scope("train"):
    train_step = tf.train.AdamOptimizer(learning_rate).minimize(xent)


# Choose between 0 and 1
onesMat = tf.ones_like(logits)
zerosMat = tf.zeros_like(logits)   
pred = tf.cast(tf.where(logits>=zero,onesMat,zerosMat),dtype=tf.float32, name = "op_to_restore")

# Problem occurs when I add this line. 
confusion = tf.confusion_matrix(predictions = pred*np.arange(1,7), labels = y*np.arange(1,7), num_classes = n_output, name = "confusion")

# Save and visualize results
saver = tf.train.Saver()
init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init)

writer = tf.summary.FileWriter(LOGDIR + hparam + '/train')
writer.add_graph(sess.graph)


# Train
for i in range(2001):
    if i % 5 == 0:
      [train_accuracy, s] = sess.run([accuracy, summ], feed_dict={x: train_x, y: train_y})
      writer.add_summary(s, i)
    if i % 50 == 0:
      [acc,s] = sess.run([accuracy, summ],feed_dict={x: test_x, y: test_y})
    sess.run(train_step, feed_dict={x: train_x, y: train_y})

谢谢！