我正在使用TensorFlow的子类化方法进行二分类。我的代码如下：
class ChurnClassifier(Model): def __init__(self): super(ChurnClassifier, self).__init__() self.layer1 = layers.Dense(20, input_dim = 20, activation = 'relu') self.layer2 = layers.Dense(41, activation = 'relu') self.layer3 = layers.Dense(83, activation = 'relu') self.layer4 = layers.Dense(2, activation = 'sigmoid') def call(self, inputs): x = self.layer1(inputs) x = self.layer2(x) x = self.layer3(x) return self.layer4(x) ChurnClassifier = ChurnClassifier()ChurnClassifier.compile(optimizer = 'adam', loss=tf.keras.losses.CategoricalCrossentropy(), metrics = ['accuracy'])现在我已经拟合了模型：
history = ChurnClassifier.fit(X_train_nur, Y_train_nur, epochs=20, batch_size=512, validation_data=(X_val_nur, Y_val_nur), shuffle=True)现在，我想预测类别是0还是1，所以我使用了以下代码 – prediction = ChurnClassifier.predict(X_val_nur)
现在我想查看有多少是0和1，以便计算TN、FN、TP、FP。因此，我创建了一个预测的DataFrame。代码如下：
pred_y = pd.DataFrame(prediction , columns=['pred_y']) 但我得到的DataFrame如下：
我的样本X_train：
array([[2.02124594e+08, 3.63743942e+04, 2.12000000e+02, ..., 4.30000000e+01, 0.00000000e+00, 1.00000000e+00], [4.93794595e+08, 6.66593354e+02, 4.22000000e+02, ..., 2.60000000e+01, 0.00000000e+00, 1.00000000e+00], [7.28506124e+08, 1.17953696e+04, 1.14000000e+03, ..., 2.50000000e+01, 0.00000000e+00, 1.00000000e+00], ..., [4.63797916e+08, 1.19273275e+03, 4.10000000e+02, ..., 9.00000000e+00, 0.00000000e+00, 1.00000000e+00], [4.04285400e+08, 1.87350825e+04, 3.01000000e+02, ..., 1.60000000e+01, 0.00000000e+00, 1.00000000e+00], [5.08433538e+08, 3.19289528e+03, 4.18000000e+02, ..., 9.00000000e+00, 0.00000000e+00, 1.00000000e+00]])我的样本y_train- array([0, 0, 0, ..., 0, 0, 0], dtype=int64)
Y_train_nur只包含0和1
问题出在哪里？
提前感谢！
回答：
对于二分类，模型的最后一层必须包含一个神经元，并且模型需要使用以下方式编译：
loss = tf.keras.losses.BinaryCrossentropy(from_logits=True),修改后的代码如下：
class ChurnClassifier(Model): def __init__(self): super(ChurnClassifier, self).__init__() self.layer1 = layers.Dense(20, input_dim = 20, activation = 'relu') self.layer2 = layers.Dense(41, activation = 'relu') self.layer3 = layers.Dense(83, activation = 'relu') self.layer4 = layers.Dense(1) def call(self, inputs): x = self.layer1(inputs) x = self.layer2(x) x = self.layer3(x) return self.layer4(x) ChurnClassifier = ChurnClassifier()ChurnClassifier.compile(optimizer = 'adam', loss =tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics = ['accuracy'])history = ChurnClassifier.fit(X_train_nur, Y_train_nur, epochs=20, batch_size=512, validation_data=(X_val_nur, Y_val_nur), shuffle=True)