Question

假设你有一个带有n神经元的Keras模型作为输出，其中每个神经元与一个回归变量（例如汽车的速度，汽车的高度......）相关联，如下所示代码段：

# define Keras model
input_layer = Input(shape=shape)
... # e.g. conv layers
x = Dense(n, activation='linear')(x)
model = Model(inputs=input_layer, outputs=x)

model.compile(loss='mean_absolute_error', optimizer='sgd', metrics=['mean_squared_error'])

history = model.fit_generator(...)

现在，存储在历史字典中的MAE损失是一个数字，它是根据n - 维y_pred和y_true计算的阵列。因此，单个损失值是n标签的单个损失的平均值，因为它可以在Keras MAE函数中看到：

def mean_absolute_error(y_true, y_pred):
    return K.mean(K.abs(y_pred - y_true), axis=-1)

但是，我想获取一个历史对象，其中包含每个n标签的损失，即{loss: {'speed': loss_value_speed, 'height': loss_value_height}}。理想情况下，培训期间的进度条也应显示个人损失而不是合并损失。

我该怎么做？

我认为可以为每个输出神经元编写一个自定义指标，它只计算y_pred和y_true向量中单个索引的损失，但这感觉就像一个解决方法：

def mean_absolute_error_label_0(y_true, y_pred):
    # calculate the loss only for the first label, label_0
    return K.mean(K.abs(y_pred[0] - y_true[0]), axis=-1)

Answer 1

一种可能的解决方案是为每个目标使用单独的输出层，并为每个目标分配name（即Dense(1, name='...')）。在你的情况下，它与使用Dense(n)输出层的训练相同，因为总损失只是个人损失的总和。

例如，

input_layer = Input(shape=(1000,))
x = Dense(100)(input_layer)

# name each output layer
target_names = ('speed', 'height')
outputs = [Dense(1, name=name)(x) for name in target_names]

model = Model(inputs=input_layer, outputs=outputs)
model.compile(loss='mean_absolute_error', optimizer='sgd', metrics=['mean_squared_error'])

现在，当您适应模型时，您应该能够分别看到每个目标的损失（和指标）。

X = np.random.rand(10000, 1000)
y = [np.random.rand(10000) for _ in range(len(outputs))]
history = model.fit(X, y, epochs=3)

Epoch 1/1
10000/10000 [==============================] - 1s 127us/step - loss: 0.9714 - speed_loss: 0.4768 - height_loss: 0.4945 - speed_mean_squared_error: 0.5253 - height_mean_squared_error: 0.5939
Epoch 2/3
10000/10000 [==============================] - 1s 101us/step - loss: 0.5109 - speed_loss: 0.2569 - height_loss: 0.2540 - speed_mean_squared_error: 0.0911 - height_mean_squared_error: 0.0895
Epoch 3/3
10000/10000 [==============================] - 1s 107us/step - loss: 0.5040 - speed_loss: 0.2529 - height_loss: 0.2511 - speed_mean_squared_error: 0.0873 - height_mean_squared_error: 0.0862

保存到返回的history对象的损失也将被命名。

print(history.history)

{'height_loss': [0.49454938204288484, 0.2539591451406479, 0.25108356306552887],
 'height_mean_squared_error': [0.5939331066846848,
  0.08951960142850876,
  0.08619525188207626],
 'loss': [0.9713814586639404, 0.5108571118354798, 0.5040025643348693],
 'speed_loss': [0.47683207807540895, 0.25689796624183653, 0.25291900217533114],
 'speed_mean_squared_error': [0.5252606071352959,
  0.09107607080936432,
  0.0872862442612648]}

编辑：如果输出height的丢失取决于speed的值，您可以：

连接输出，因为您需要两个值来计算自定义损失
将Concatenate图层命名为＆＃34; height＆＃34;，这将是历史记录对象中height的输出
为model.compile()提供两个损失函数（一个用于speed，一个用于连接输出height）

def custom_loss(y_true, y_pred):
    y_pred_height = y_pred[:, 0]
    y_pred_speed = y_pred[:, 1]

    # some loss which depends on the value of `speed`
    loss = losses.mean_absolute_error(y_true, y_pred_height * y_pred_speed)
    return loss

input_layer = Input(shape=(1000,))
x = Dense(100, activation='relu')(input_layer)

output_speed = Dense(1, activation='relu', name='speed')(x)
output_height = Dense(1, activation='relu')(x)
output_merged = Concatenate(name='height')([output_height, output_speed])

model = Model(inputs=input_layer, outputs=[output_speed, output_merged])
model.compile(loss={'speed': 'mean_absolute_error', 'height': custom_loss},
              optimizer='sgd',
              metrics={'speed': 'mean_squared_error'})

输出将是：

X = np.random.rand(10000, 1000)
y = [np.random.rand(10000), np.random.rand(10000)]
history = model.fit(X, y, epochs=3)

Epoch 1/3
10000/10000 [==============================] - 5s 501us/step - loss: 1.0001 - speed_loss: 0.4976 - height_loss: 0.5026 - speed_mean_squared_error: 0.3315
Epoch 2/3
10000/10000 [==============================] - 2s 154us/step - loss: 0.9971 - speed_loss: 0.4960 - height_loss: 0.5011 - speed_mean_squared_error: 0.3285
Epoch 3/3
10000/10000 [==============================] - 1s 149us/step - loss: 0.9971 - speed_loss: 0.4960 - height_loss: 0.5011 - speed_mean_squared_error: 0.3285.

print(history.history)
{'height_loss': [0.502568191242218, 0.5011419380187988, 0.5011419407844544],
 'loss': [1.0001451692581176, 0.9971360887527466, 0.9971360870361328],
 'speed_loss': [0.4975769768714905, 0.4959941484451294, 0.4959941472053528],
 'speed_mean_squared_error': [0.33153974375724793,
                              0.32848617186546325,
                              0.32848617215156556]}

Keras：在多标签回归中显示每个标签的损失

1 个答案: