TF.Keras 自定义 Scratch 训练中的多输出-多类分类

Question

我想从头开始训练多输出和多类分类模型（使用自定义fit() ）。 我想要一些建议。 为了学习机会，我在这里更详细地演示整个场景。 希望它可以对任何人有所帮助。

数据集和目标

我正在使用这里的数据； 这是一个孟加拉手写字符识别挑战，每个样本有3 个相互关联的输出以及每个的多个类。 请看下图：

在上图中，如您所见，ক্ট্রো 由 3 个组件（ক্ট 、 ো 、 ‍‍্র）组成，分别是字素根、元音变音符号和辅音变音符号，它们统称为Graphe 。 同样，字素根也有168 个不同的类别，也与其他类别相同（ 11和7 ）。 增加的复杂性导致了大约 13,000 个不同的字素变化（与英语的 250 个字素单位相比）。

目标是对每个图像中字素的组件进行分类。

初始方法（并且没有问题）

我在这里实现了一个训练管道， 在那里它使用旧的keras （不是tf.keras ）进行了演示，它具有一个方便的功能，例如model.compile 、 callbacks等。我定义了一个自定义数据生成器并定义了一个类似于下面的模型架构。

input_tensor = Input(input_dim)
curr_output = base_model(input_tensor)

oputput1 = Dense(168,  activation='softmax', name='gra') (curr_output)
oputput2 = Dense(11,   activation='softmax', name='vow') (curr_output)
oputput3 = Dense(7,    activation='softmax', name='cons') (curr_output)
output_tensor = [oputput1, oputput2, oputput3]
    
model = Model(input_tensor, output_tensor)

并编译模型如下：

model.compile(

        optimizer = Adam(learning_rate=0.001), 

        loss = {'gra' : 'categorical_crossentropy', 
                'vow' : 'categorical_crossentropy', 
                'cons': 'categorical_crossentropy'},

        loss_weights = {'gra' : 1.0,
                        'vow' : 1.0,
                        'cons': 1.0},

        metrics={'gra' : 'accuracy', 
                 'vow' : 'accuracy', 
                 'cons': 'accuracy'}
    )

如您所见，我可以通过特定的loss 、 loss_weights和accuracy明确控制每个输出。 并且使用.fit()方法，可以为模型使用任何callbacks函数。

新方法（以及它的一些问题）

现在，我想用tf.keras的新功能重新实现它。 例如模型子类化和自定义拟合训练。 但是，数据加载器没有变化。 该模型定义如下：

    def __init__(self, dim):
        super(Net, self).__init__()
        self.efnet  = EfficientNetB0(input_shape=dim,
                                     include_top = False, 
                                     weights = 'imagenet')
        self.gap     = KL.GlobalAveragePooling2D()
        self.output1 = KL.Dense(168,  activation='softmax', name='gra')
        self.output2 = KL.Dense(11,   activation='softmax', name='vow') 
        self.output3 = KL.Dense(7,    activation='softmax', name='cons') 
    
    def call(self, inputs, training=False):
        x     = self.efnet(inputs)
        x     = self.gap(x)
        y_gra = self.output1(x)
        y_vow = self.output2(x)
        y_con = self.output3(x)
        return [y_gra, y_vow, y_con]

现在我面临的主要问题是为我的每个输出正确定义metrics 、 loss和loss_weights函数。 但是，我是这样开始的：

optimizer        = tf.keras.optimizers.Adam(learning_rate=0.05)
loss_fn          = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
train_acc_metric = tf.keras.metrics.Accuracy()

@tf.function
def train_step(x, y):
    with tf.GradientTape(persistent=True) as tape:
        logits = model(x, training=True)  # Logits for this minibatch
        train_loss_value = loss_fn(y, logits)

    grads = tape.gradient(train_loss_value, model.trainable_weights)
    optimizer.apply_gradients(zip(grads, model.trainable_weights))
    train_acc_metric.update_state(y, logits)
    return train_loss_value


for epoch in range(2):
    # Iterate over the batches of the dataset.
    for step, (x_batch_train, y_batch_train) in enumerate(train_generator):
        train_loss_value = train_step(x_batch_train, y_batch_train)

    # Reset metrics at the end of each epoch
    train_acc_metric.reset_states()

除了上述设置之外，我还尝试了其他多种方法来处理此类问题案例。 例如，我定义了 3 个损失函数和 3 个指标，但事情无法正常工作。 loss/acc变成了nan类型的东西。

在这种情况下，这是我的几个直接查询：

• 如何定义loss 、 metrics和loss_weights
• 如何有效使用所有callbacks功能

只是为了学习机会，如果它有额外的回归类型输出（连同其余3 个多输出，总共4 个）； 如何对付它们都在定制fit ？ 我访问过这个SO ，为不同类型的输出（ classification + regression ）提供了一些提示。

Answer 1

你只需要做一个自定义的训练循环，但一切都需要做 3 次（如果你还有一个连续变量，+ 1）。 这是使用四路输出架构的示例：

import tensorflow as tf
import numpy as np

(xtrain, train_target), (xtest, test_target) = tf.keras.datasets.mnist.load_data()

# 10 categories, one for each digit
ytrain1 = tf.keras.utils.to_categorical(train_target, num_classes=10)
ytest1 = tf.keras.utils.to_categorical(test_target, num_classes=10)

# 2 categories, if the digit is odd or not
ytrain2 = tf.keras.utils.to_categorical((train_target % 2 == 0).astype(int), 
                                        num_classes=2)
ytest2 = tf.keras.utils.to_categorical((test_target % 2 == 0).astype(int), 
                                       num_classes=2)

# 4 categories, based on the interval of the digit
ytrain3 = tf.keras.utils.to_categorical(np.digitize(train_target, [3, 6, 8]), 
                                        num_classes=4)
ytest3 = tf.keras.utils.to_categorical(np.digitize(test_target, [3, 6, 8]), 
                                       num_classes=4)

# Regression, the square of the digit
ytrain4 = tf.square(tf.cast(train_target, tf.float32))
ytest4 = tf.square(tf.cast(test_target, tf.float32))

# train dataset
train_ds = tf.data.Dataset. \
    from_tensor_slices((xtrain, ytrain1, ytrain2, ytrain3, ytrain4)). \
    shuffle(32). \
    batch(32).map(lambda a, *rest: (tf.divide(a[..., None], 255), rest)). \
    prefetch(tf.data.experimental.AUTOTUNE)

# test dataset
test_ds = tf.data.Dataset. \
    from_tensor_slices((xtest, ytest1, ytest2, ytest3, ytest4)). \
    shuffle(32). \
    batch(32).map(lambda a, *rest: (tf.divide(a[..., None], 255), rest)). \
    prefetch(tf.data.experimental.AUTOTUNE)


# architecture
class Net(tf.keras.Model):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = tf.keras.layers.Conv2D(filters=16, kernel_size=(3, 3),
                                            strides=(1, 1), input_shape=(28, 28, 1),
                                            activation='relu')
        self.maxp1 = tf.keras.layers.MaxPool2D(pool_size=(2, 2))
        self.conv2 = tf.keras.layers.Conv2D(filters=32, kernel_size=(3, 3),
                                            strides=(1, 1),
                                            activation='relu')
        self.maxp2 = tf.keras.layers.MaxPool2D(pool_size=(2, 2))
        self.conv3 = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3),
                                            strides=(1, 1),
                                            activation='relu')
        self.maxp3 = tf.keras.layers.MaxPool2D(pool_size=(2, 2))
        self.gap = tf.keras.layers.Flatten()
        self.dense = tf.keras.layers.Dense(64, activation='relu')
        self.output1 = tf.keras.layers.Dense(10, activation='softmax')
        self.output2 = tf.keras.layers.Dense(2, activation='softmax')
        self.output3 = tf.keras.layers.Dense(4, activation='softmax')
        self.output4 = tf.keras.layers.Dense(1, activation='linear')

    def call(self, inputs, training=False, **kwargs):
        x = self.conv1(inputs)
        x = self.maxp1(x)
        x = self.conv2(x)
        x = self.maxp2(x)
        x = self.conv3(x)
        x = self.maxp3(x)
        x = self.gap(x)
        x = self.dense(x)
        out1 = self.output1(x)
        out2 = self.output2(x)
        out3 = self.output3(x)
        out4 = self.output4(x)
        return out1, out2, out3, out4


model = Net()

optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)

# the three losses
loss_1 = tf.losses.CategoricalCrossentropy()
loss_2 = tf.losses.CategoricalCrossentropy()
loss_3 = tf.losses.CategoricalCrossentropy()
loss_4 = tf.losses.MeanAbsoluteError()

# mean object that keeps track of the train losses
loss_1_train = tf.metrics.Mean(name='tr_loss_1')
loss_2_train = tf.metrics.Mean(name='tr_loss_2')
loss_3_train = tf.metrics.Mean(name='tr_loss_3')
loss_4_train = tf.metrics.Mean(name='tr_loss_4')

# mean object that keeps track of the test losses
loss_1_test = tf.metrics.Mean(name='ts_loss_1')
loss_2_test = tf.metrics.Mean(name='ts_loss_2')
loss_3_test = tf.metrics.Mean(name='ts_loss_3')
loss_4_test = tf.metrics.Mean(name='ts_loss_4')

# accuracies for printout
acc_1_train = tf.metrics.CategoricalAccuracy(name='tr_acc_1')
acc_2_train = tf.metrics.CategoricalAccuracy(name='tr_acc_2')
acc_3_train = tf.metrics.CategoricalAccuracy(name='tr_acc_3')

# accuracies for printout
acc_1_test = tf.metrics.CategoricalAccuracy(name='ts_acc_1')
acc_2_test = tf.metrics.CategoricalAccuracy(name='ts_acc_2')
acc_3_test = tf.metrics.CategoricalAccuracy(name='ts_acc_3')


# custom training loop
@tf.function
def train_step(x, y1, y2, y3, y4):
    with tf.GradientTape(persistent=True) as tape:
        out1, out2, out3, out4 = model(x, training=True)
        loss_1_value = loss_1(y1, out1)
        loss_2_value = loss_2(y2, out2)
        loss_3_value = loss_3(y3, out3)
        loss_4_value = loss_4(y4, out4)

    losses = [loss_1_value, loss_2_value, loss_3_value, loss_4_value]

    # a list of losses is passed
    grads = tape.gradient(losses, model.trainable_variables)

    # gradients are applied
    optimizer.apply_gradients(zip(grads, model.trainable_variables))

    # losses are updated
    loss_1_train(loss_1_value)
    loss_2_train(loss_2_value)
    loss_3_train(loss_3_value)
    loss_4_train(loss_4_value)

    # accuracies are updated
    acc_1_train.update_state(y1, out1)
    acc_2_train.update_state(y2, out2)
    acc_3_train.update_state(y3, out3)


@tf.function
def test_step(x, y1, y2, y3, y4):
    out1, out2, out3, out4 = model(x, training=False)
    loss_1_value = loss_1(y1, out1)
    loss_2_value = loss_2(y2, out2)
    loss_3_value = loss_3(y3, out3)
    loss_4_value = loss_4(y4, out4)

    loss_1_test(loss_1_value)
    loss_2_test(loss_2_value)
    loss_3_test(loss_3_value)
    loss_4_test(loss_4_value)

    acc_1_test.update_state(y1, out1)
    acc_2_test.update_state(y2, out2)
    acc_3_test.update_state(y3, out3)


for epoch in range(5):
    # train step
    for inputs, outputs1, outputs2, outputs3, outputs4 in train_ds:
        train_step(inputs, outputs1, outputs2, outputs3, outputs4)

    # test step
    for inputs, outputs1, outputs2, outputs3, outputs4 in test_ds:
        test_step(inputs, outputs1, outputs2, outputs3, outputs4)

    metrics = [acc_1_train, acc_1_test,
               acc_2_train, acc_2_test,
               acc_3_train, acc_3_test,
               loss_4_train, loss_4_test]

    # printing metrics
    for metric in metrics:
        print(f'{metric.name}:{metric.result():=6.4f}', end=' ')   
    print()

    # resetting the states of the metrics
    loss_1_train.reset_states()
    loss_2_train.reset_states()
    loss_3_train.reset_states()

    loss_1_test.reset_states()
    loss_2_test.reset_states()
    loss_3_test.reset_states()

    acc_1_train.reset_states()
    acc_2_train.reset_states()
    acc_3_train.reset_states()

    acc_1_test.reset_states()
    acc_2_test.reset_states()
    acc_3_test.reset_states()

ts_acc_1:0.9495 ts_acc_2:0.9685 ts_acc_3:0.9589 ts_loss_4:5.5617 
ts_acc_1:0.9628 ts_acc_2:0.9747 ts_acc_3:0.9697 ts_loss_4:4.8953 
ts_acc_1:0.9697 ts_acc_2:0.9758 ts_acc_3:0.9733 ts_loss_4:4.5209 
ts_acc_1:0.9715 ts_acc_2:0.9796 ts_acc_3:0.9745 ts_loss_4:4.2175 
ts_acc_1:0.9742 ts_acc_2:0.9834 ts_acc_3:0.9775 ts_loss_4:3.9825

我不知道如何在自定义训练循环中使用 Keras 回调，关于这个主题的最流行的问题也不知道。 如果您想使用 EarlyStopping，我个人使用collections.deque ，并在最小损失为倒数第 n 个时中断。 下面是一个例子：

from collections import deque
import numpy as np

epochs = 100
early_stopping = 5

loss_hist = deque(maxlen=early_stopping)

for epoch in range(epochs):
    loss_value = np.random.rand()
    loss_hist.append(loss_value)

    print('Last 5 values: ', *np.round(loss_hist, 3))

    if len(loss_hist) == early_stopping and loss_hist.popleft() < min(loss_hist):
        print('Early stopping. No loss decrease in %i epochs.\n' % early_stopping)
        break

Last 5 values:  0.456
Last 5 values:  0.456 0.153
Last 5 values:  0.456 0.153 0.2
Last 5 values:  0.456 0.153 0.2 0.433
Last 5 values:  0.456 0.153 0.2 0.433 0.528
Last 5 values:  0.153 0.2 0.433 0.528 0.349
Early stopping. No loss decrease in 5 epochs.

可以看到，最后一次，最里面的值是最小的，所以验证损失没有增加。 这就是停止条件。