了解二元分类的张量流混淆矩阵

Question

我有一个二进制分类问题，我试图在张量流中解决。我正在使用一个简单的多层感知器。我试图理解我得到的混淆矩阵。示例输出是：

Epoch: 0001 cost=882.103631592 
Epoch: 0002 cost=496.739675903 
Epoch: 0003 cost=403.711282349 
Epoch: 0004 cost=389.798379517 
Epoch: 0005 cost=324.857388306  
Optimization Finished! 
Accuracy: 0.889306 
CM=  
[[797 260] 
[ 0 1071]] 

标签是AWAKE和NOT_AWAKE。它看起来像从一个单一的热门编码，我有[1,0] AWAKE和[0,1] NOT_AWAKE（我只是将数组保存到一个文件，并目测检查）。
如何解释混淆矩阵？ 相信这样的输出：

CM=  
[[797 260] 
[ 0 1071]] 

可被解释为：

   Pred: 0 | Pred: 1 
Actual 0: 797 | 260     
Actual 1: 0  | 1071 

不[1,0]（一个热为AWAKE编码）成为第1行中的混淆矩阵？ 大部分运行mlp的代码如下。

# Parameters 
learning_rate = 0.00001 
training_epochs = 4 
display_step = 1 
keep_prob_training = 0.75 

# Network Parameters 
n_hidden_1 = 2048 # 1st layer number of neurons 
n_hidden_2 = 2048 # 2nd layer number of neurons 
n_input = 9 # channels 
n_classes = 2 # total classes 

print("Some hyper params: training_epochs = %s,learning_rate = %f,keep_prob_training = %s, n_hidden_1 = %s,n_hidden_2 = %s" % (training_epochs, learning_rate, keep_prob_training, n_hidden_1, n_hidden_2)) 
print ("Misc shape info: X_train.shape = %s, X_test.shape = %s, y_train.shape = %s, y_test.shape = %s" % (np.shape(X_train), np.shape(X_test), np.shape(y_train), np.shape(y_test))) 

# tf Graph input 
X = tf.placeholder("float", [None, n_input]) 
Y = tf.placeholder("float", [None, n_classes]) 
keep_prob = tf.placeholder(tf.float32) 
# placeholder for confusion matrix 
y_ = tf.placeholder(tf.float32, shape = [None, 2]) 


# Store layers weight & bias 
weights = { 
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), 
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), 
    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes])) 
} 
biases = { 
    'b1': tf.Variable(tf.random_normal([n_hidden_1])), 
    'b2': tf.Variable(tf.random_normal([n_hidden_2])), 
    'out': tf.Variable(tf.random_normal([n_classes])) 
} 


# Create model 
def multilayer_perceptron(x): 
    # Hidden fully connected layer 
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) 
    layer_1 = tf.nn.relu(layer_1) 
    # apply DropOut to hidden layer 
    drop_out_1 = tf.nn.dropout(layer_1, keep_prob) 
    # Hidden fully connected layer 
    layer_2 = tf.add(tf.matmul(drop_out_1, weights['h2']), biases['b2']) 
    layer_2 = tf.nn.relu(layer_2) 
    drop_out_2 = tf.nn.dropout(layer_2, keep_prob) 
    # Output fully connected layer with a neuron for each class 
    out_layer = tf.matmul(drop_out_2, weights['out']) + biases['out'] 
    return out_layer 

# Construct model 
logits = multilayer_perceptron(X) 

# obtain cm after training 
confusion_matrix_tf = tf.confusion_matrix(tf.argmax(logits, 1), tf.argmax(y_, 1)) 

# Define loss and optimizer 
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = Y)) 
optimizer = tf.train.AdamOptimizer(learning_rate) 
train_op = optimizer.minimize(loss_op) 

# Initializing the variables 
init = tf.global_variables_initializer() 

with tf.Session() as sess: 
    sess.run(init) 

    # Training cycle 
    for epoch in range(training_epochs): 
     avg_cost = 0. 

     # Run optimization op (backprop) and cost op (to get loss value) 
     _, c = sess.run([train_op, loss_op], feed_dict = {X: X_train, Y: y_train, keep_prob : keep_prob_training}) 
     # Compute average loss 
     # Display logs per epoch step 
     if epoch % display_step == 0: 
      print("Epoch:", '%04d' % (epoch + 1) , "cost={:.9f}".format(c)) 
    print("Optimization Finished!") 

    # Test model 
    pred = tf.nn.softmax(logits) # Apply softmax to logits 
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(Y, 1)) 
    # Calculate accuracy 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) 
    print("Accuracy:", accuracy.eval({X: X_test, Y: y_test, keep_prob : 1.0})) 

    cm = confusion_matrix_tf.eval(feed_dict = {X: X_train, y_: y_train, keep_prob: 1.0}) 
    print("CM=\n", cm) 

这是我如何编码我的标签：

label_encoder = LabelEncoder() 
    integer_encoded = label_encoder.fit_transform(df_combined['Label']) 

    # binary encode 
    onehot_encoder = OneHotEncoder(sparse = False) 
    integer_encoded = integer_encoded.reshape(len(integer_encoded), 1) 
    all_y = onehot_encoder.fit_transform(integer_encoded) 

Answer 1

关于Tensorflow confusion matrix，您它是如何解释的假设是正确的。

例如：

number of classes = 2 
Predicted labels = [0, 1, 1, 1, 0, 0, 0, 0, 1, 1] 
Actual labels = [0, 1, 1, 1, 1, 1, 1, 1, 0, 0] 

所以你Tensorflow混淆矩阵将是：

   Pred: 0 | Pred: 1 
Actual 0: 1  | 2     
Actual 1: 4  | 3 

接着，在是AWAKE解释为[0,1]或[1,0]取决于什么标签你分配给AWAKE之前，你做了一个热门的编码（你没有附上那部分代码）。例如，如果你已经将AWAKE赋值为0，并且因为只有两个类，那么单热编码会给你[1,0]。

希望这个答案可以帮助你！