Error: Received a label value of 1 which is outside the valid range of [0, 1)-Python,Keras

Error: Received a label value of 1 which is outside the valid range of [0, 1)-Python,Keras

由于keras在做二分类损失的时候，激活函数选择的是sigmod，所以此处不能超过1

当选择的损失函数对应的取值超出范围的时候就会报错。

如下合理的loss使用方式供参考：

# 由于此处为二分类问题，此处选择sigmoid作为激活函数out1 = layers.Dense(units=1, activation="sigmoid", name='out1')(features)# 为多分类，此处选择softmax作为激活函数out2 = layers.Dense(units=6, activation="softmax", name='out2')(features)# Received a label value of 1 which is outside the valid range of [0, 1) - Python, Keras# In the last Dense layer you used model.add(Dense(1, activation='softmax')).# Here 1 restricts its value from [0, 1) change its shape to the maximum output label.# For eg your output is from label [0,7) then use model.add(Dense(7, activation='softmax'))def focal_loss(y_true, y_pred): print('focal_loss', y_true, y_pred) return sparse_categorical_focal_loss(y_true, y_pred, gamma=2, from_logits=True)model = tf.keras.Model(inputs=inputs, outputs=[out1, out2])model = create_model(encoding_size)model.compile( optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate), loss={'out1': tf.keras.losses.BinaryCrossentropy(), "out2": focal_loss}, loss_weights={'out1': 0.5, "out2": 0.5}, metrics=["accuracy" # tf.keras.metrics.SparseCategoricalAccuracy(name="accuracy"), # tf.keras.metrics.AUC(name="auc") ])

参考：​​https://stackoverflow.com/questions/44151760/received-a-label-value-of-1-which-is-outside-the-valid-range-of-0-1-python​​

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