Question

我正在尝试为多类分类问题找到一个混淆矩阵。但是我总是这样： ValueError：分类指标无法处理multilabel-indicator和二进制目标的混合。不知道该如何进行...

读取数据集

            x1 = pd.read_csv('x daten encoder.csv')
            y1 = pd.read_csv('y daten encodieren.csv')

            df=pd.DataFrame(X1)
            X = df
            df1=pd.DataFrame(Y1)

编码

            encoder = LabelEncoder()
            encoder.fit(df1)
            y2 = encoder.transform(df1)

            Y = to_categorical(y2)

X，Y随机播放数据集

            X, Y = shuffle(X, Y, random_state=1)  #um meine x und y daten zu mischen

在培训中和考试日期

            train_x, test_x, train_y, test_y = train_test_split(X, Y, test_size=0.20, random_state=101)
            print(train_x.shape)
            print(train_y.shape)
            print(test_x.shape)
            #model_path = "C: ...."

变节狂欢节

            learning_rate = 0.3
            training_epochs = 20
            cost_history = np.empty(shape=[1], dtype=float)
            n_dim = X.shape[1]   # wie viele neuronen # also 1024
            print("n_dim:", n_dim)
            n_class = 6

ein NN定义：隐藏层 n_hidden = 10

            x = tf.placeholder(tf.float32, [None, n_dim])
            W = tf.Variable(tf.zeros([n_dim, n_class]))
            b = tf.Variable(tf.zeros([n_class]))
            y_ = tf.placeholder(tf.float32, [None, n_class])

克雷耶尔的权重和偏见重物建立模型：

            def multilayer_perceptron(x, weights, biases):

                # hiddenlayer mit tanh aktivierungsfkt
                layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
                layer_1 = tf.nn.tanh(layer_1)

                #output-layer mit linearer aktivierungsfkt
                out_layer = tf.matmul(layer_1, weights['out'] + biases['out'])
                return out_layer

定义每层的权重和偏差

            weights = {
                    'h1' : tf.Variable(tf.truncated_normal([n_dim, n_hidden])),
                    'out' : tf.Variable(tf.truncated_normal([n_hidden, n_class]))
            }

            biases = {
                    'b1' : tf.Variable(tf.truncated_normal([n_hidden])),
                    'out' : tf.Variable(tf.truncated_normal([n_class]))
            }

初始化所有变量

            init = tf.global_variables_initializer()

            saver = tf.train.Saver()

我的定义模型aufrufen，dort wird trainiert

            y = multilayer_perceptron(x, weights, biases)

定义和优化程序

            cost_function = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=y, labels=y_))
            training_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)

饮食会议kreieren

            sess = tf.Session()
            sess.run(init)

berechne die kosten und genauigkeitfürjede epoche

            mse_history = []
            accuracy_history = []

            for epoch in range(training_epochs):
                sess.run(training_step, feed_dict = {x: train_x, y_: train_y})
                cost = sess.run(cost_function, feed_dict={x: train_x, y_: train_y})
                cost_history = np.append(cost_history, cost)
                correct_prediction = tf.equal(tf.argmax(y, 1), tf.arg_max(y_, 1))
                accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
                print ("Accuracy: ", (sess.run(accuracy, feed_dict={x: test_x, y_: test_y})))
                pred_y = sess.run(y, feed_dict={x: test_x})
                mse = tf.reduce_mean(tf.square(pred_y - test_y))
                mse_ = sess.run(mse)
                mse_history.append(mse_)
                accuracy = (sess.run(accuracy, feed_dict={x: train_x, y_: train_y}))
                accuracy_history.append(accuracy)

                print('epoch: ', epoch, '-', 'cost: ', cost, "-MSE: ", mse_, "Train-accuracy: ", accuracy)

绘制一个混淆矩阵

            from sklearn.metrics import confusion_matrix
            cm = confusion_matrix(test_y, pred_y)

ValueError：分类指标无法处理多标签指标和连续多输出目标的混合情况

0 个答案: