tf.keras.metrics.sparse_categorical_accuracys是什么

tf.keras.metrics.sparse_categorical_accuracy是一个Keras指标函数，用于计算稀疏分类任务的准确性。它接受真实标签和模型预测标签作为输入，并计算它们之间的准确性。它可以用于评估模型的性能，并在训练期间监控模型的准确性。稀疏分类任务是指目标变量是整数，而不是二进制向量，例如图像分类任务中的标签。

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tf.keras.metrics.sparse_categorical_accuracy

tf.keras.metrics.sparse_categorical_accuracy is a metric used in Keras to calculate the accuracy of a classification model that uses sparse categorical cross-entropy as its loss function. It is typically used when the labels are integers and not one-hot encoded.

The sparse categorical accuracy calculates the accuracy of the model's predictions by comparing the highest predicted class index with the true class index for each input sample. It then averages the number of correct predictions over the total number of samples.

This metric is commonly used in multi-class classification tasks where the number of classes is large and one-hot encoding the labels is not practical due to memory constraints.

优化这段代码：import tensorflow as tf import numpy as np # 加载数据集 mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 # 定义模型 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax')]) # 编译模型 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # 训练模型 model.fit(x_train, y_train, epochs=5) # 生成对抗样本 epsilon = 0.1 x_adv = tf.Variable(x_test[:1000], dtype=tf.float32) y_true = tf.Variable(y_test[:1000], dtype=tf.int64) with tf.GradientTape() as tape: tape.watch(x_adv) y_pred = model(x_adv) loss = tf.keras.losses.sparse_categorical_crossentropy(y_true, y_pred) grad = tape.gradient(loss, x_adv) normed_grad = tf.sign(grad) x_adv = x_adv + epsilon * normed_grad # 评估模型在对抗样本上的性能 model.evaluate(x_adv, y_true)

下面是优化后的代码：

import tensorflow as tf 
import numpy as np 

# 加载数据集 
mnist = tf.keras.datasets.mnist 
(x_train, y_train), (x_test, y_test) = mnist.load_data() 
x_train, x_test = x_train / 255.0, x_test / 255.0 

# 定义模型 
model = tf.keras.models.Sequential([ 
    tf.keras.layers.Flatten(input_shape=(28, 28)), 
    tf.keras.layers.Dense(128, activation='relu'), 
    tf.keras.layers.Dropout(0.2), 
    tf.keras.layers.Dense(10, activation='softmax') 
]) 

# 编译模型 
model.compile(optimizer='adam',              
              loss='sparse_categorical_crossentropy',              
              metrics=['accuracy']) 

# 训练模型 
model.fit(x_train, y_train, epochs=5) 

# 生成对抗样本 
epsilon = 0.1 
x_adv = x_test[:1000]
y_true = y_test[:1000] 

# 使用tf.GradientTape计算梯度 
def get_grads(model, x, y):
    with tf.GradientTape() as tape:
        tape.watch(x)
        y_pred = model(x)
        loss = tf.keras.losses.sparse_categorical_crossentropy(y, y_pred)
    return tape.gradient(loss, x)

# 对抗样本生成函数 
def generate_adv_example(model, x, y, epsilon):
    grad = tf.sign(get_grads(model, x, y)) 
    return x + epsilon * grad

x_adv = generate_adv_example(model, x_adv, y_true, epsilon)

# 评估模型在对抗样本上的性能 
model.evaluate(x_adv, y_true)

优化后的代码主要做了以下几个方面的改进：

1. 对抗样本生成过程封装成了函数 `generate_adv_example()`，提高了代码的复用性。

2. 使用了 `get_grads()` 函数来计算梯度，避免了重复计算梯度的问题。

3. 去掉了 `tf.Variable` 的使用，避免了不必要的内存开销。

4. 代码风格更加规范，易于阅读。