7 自定义损失函数

自定义损失函数

这个实验需要用到mnist.npz数据集
自定义训练和用自带的fit()函数训练好像差不多

自定义训练

头文件

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf

from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
import numpy as np

# 按需，OOM
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)

载入数据集并处理

mnist = np.load("mnist.npz")
x_train, y_train, x_test, y_test = mnist['x_train'],mnist['y_train'],mnist['x_test'],mnist['y_test']
# 归一化
x_train, x_test = x_train / 255.0, x_test / 255.0

# Add a channels dimension
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

y_train = tf.one_hot(y_train,depth=10)
y_test = tf.one_hot(y_test,depth=10)

train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

搭建网络

class MyModel(Model):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = Conv2D(32, 3, activation='relu')
        self.flatten = Flatten()
        self.d1 = Dense(128, activation='relu')
        self.d2 = Dense(10, activation='softmax')

    def call(self, x):
        x = self.conv1(x)
        x = self.flatten(x)
        x = self.d1(x)
        return self.d2(x)

定义损失函数，一个是用类实现的，一个是用函数实现的，都能用

# #多分类的focal loss 损失函数，类的实现
# class FocalLoss(tf.keras.losses.Loss):

# def __init__(self,gamma=2.0,alpha=0.25):
# self.gamma = gamma
# self.alpha = alpha
# super(FocalLoss, self).__init__()

# def call(self,y_true,y_pred):
# y_pred = tf.nn.softmax(y_pred,axis=-1)
# epsilon = tf.keras.backend.epsilon()#1e-7
# y_pred = tf.clip_by_value(y_pred, epsilon, 1.0)
        
# y_true = tf.cast(y_true,tf.float32)
        
# loss = - y_true * tf.math.pow(1 - y_pred, self.gamma) * tf.math.log(y_pred)
        
# loss = tf.math.reduce_sum(loss,axis=1)
# return loss

# 函数的方式实现
def FocalLoss(gamma=2.0,alpha=0.25):
    def focal_loss_fixed(y_true, y_pred):
        y_pred = tf.nn.softmax(y_pred,axis=-1)
        epsilon = tf.keras.backend.epsilon()
        y_pred = tf.clip_by_value(y_pred, epsilon, 1.0)

        y_true = tf.cast(y_true,tf.float32)

        loss = -  y_true * tf.math.pow(1 - y_pred, gamma) * tf.math.log(y_pred)

        loss = tf.math.reduce_sum(loss,axis=1)
        return  loss
    return focal_loss_fixed

选择优化器损失函数。。。。。

model = MyModel()

# 自带的损失函数
# loss_object = tf.keras.losses.CategoricalCrossentropy()
# 自己定义的损失函数
loss_object = FocalLoss(gamma=2.0,alpha=0.25)

optimizer = tf.keras.optimizers.Adam()

train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.CategoricalAccuracy(name='train_accuracy')

test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.CategoricalAccuracy(name='test_accuracy')


@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    train_loss(loss)
    train_accuracy(labels, predictions)


@tf.function
def test_step(images, labels):
    predictions = model(images)
    t_loss = loss_object(labels, predictions)

    test_loss(t_loss)
    test_accuracy(labels, predictions)

训练

epochs = 5
for epoch in range(epochs):
    # 在下一个epoch开始时，重置评估指标
    train_loss.reset_states()
    train_accuracy.reset_states()
    test_loss.reset_states()
    test_accuracy.reset_states()

    for images, labels in train_ds:
        train_step(images, labels)

    for test_images, test_labels in test_ds:
        test_step(test_images, test_labels)

    template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
    print(template.format(epoch + 1,
                          train_loss.result(),
                          train_accuracy.result() * 100,
                          test_loss.result(),
                          test_accuracy.result() * 100))

fit()训练

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
import numpy as np

from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession

config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)

mnist = np.load("mnist.npz")
x_train, y_train, x_test, y_test = mnist['x_train'],mnist['y_train'],mnist['x_test'],mnist['y_test']

x_train, x_test = x_train / 255.0, x_test / 255.0
y_train = np.int32(y_train)
y_test = np.int32(y_test)
# Add a channels dimension
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]
y_train = tf.one_hot(y_train,depth=10)
y_test = tf.one_hot(y_test,depth=10)
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).shuffle(100).batch(32)

# 定义模型
def MyModel():
    inputs = tf.keras.Input(shape=(28,28,1), name='digits')
    x = tf.keras.layers.Conv2D(32, 3, activation='relu')(inputs)
    x = tf.keras.layers.Flatten()(x)
    x = tf.keras.layers.Dense(128, activation='relu')(x)
    outputs = tf.keras.layers.Dense(10,activation='softmax', name='predictions')(x)
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    return model

# #多分类的focal loss 损失函数
class FocalLoss(tf.keras.losses.Loss):

    def __init__(self,gamma=2.0,alpha=0.25):
        self.gamma = gamma
        self.alpha = alpha
        super(FocalLoss, self).__init__()

    def call(self,y_true,y_pred):
        y_pred = tf.nn.softmax(y_pred,axis=-1)
        epsilon = tf.keras.backend.epsilon()
        y_pred = tf.clip_by_value(y_pred, epsilon, 1.0)
        
       
        y_true = tf.cast(y_true,tf.float32)
        
        loss = -  y_true * tf.math.pow(1 - y_pred, self.gamma) * tf.math.log(y_pred)
        
        loss = tf.math.reduce_sum(loss,axis=1)
        return loss

# def FocalLoss(gamma=2.0,alpha=0.25):
# def focal_loss_fixed(y_true, y_pred):
# y_pred = tf.nn.softmax(y_pred,axis=-1)
# epsilon = tf.keras.backend.epsilon()
# y_pred = tf.clip_by_value(y_pred, epsilon, 1.0)

# y_true = tf.cast(y_true,tf.float32)

# loss = - y_true * tf.math.pow(1 - y_pred, gamma) * tf.math.log(y_pred)

# loss = tf.math.reduce_sum(loss,axis=1)
# return loss
# return focal_loss_fixed

# 优化器损失函数评估指标那些
# 损失函数可以用自己定义的
model = MyModel()
model.compile(optimizer = tf.keras.optimizers.Adam(0.001), #优化器
              loss =  FocalLoss(gamma=2.0,alpha=0.25), #损失函数
              metrics = [tf.keras.metrics.CategoricalAccuracy()]
             ) #评估函数


# 训练
model.fit(train_ds, epochs=5,validation_data=test_ds)