Keras的精度、AUC和其他指标列于：https://keras.io/api/metrics/classification_metrics/
请尝试按以下方式使用它们：

local_model.compile(
    loss=tf.keras.losses.CategoricalCrossentropy(),
    optimizer=tf.keras.optimizers.Adam(learning_rate = 0.001),
    metrics=['acc',
             tf.keras.metrics.Precision(thresholds=0),
             tf.keras.metrics.Recall(thresholds=0),
             tf.keras.metrics.AUC(from_logits=True),
             #could add more metrics...
            ]
)

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threshold=0和from_logits=True是如果您的模型返回logits，如上页所述。
要绘制指标，它类似于：

# list all data in available in history
keys = list(history.history.keys())
print('Info in history is:\n\t', keys)

# select some metrics to plot from history
metricA = keys[0]
metricB = keys[1]
metricC = keys[2]

#create plot
plt.plot(history.history[metricA])
plt.plot(history.history[metricB])
plt.plot(history.history[metricC])

plt.title('History')
plt.ylabel('value')
plt.xlabel('epoch')
plt.legend([metricA, metricB, metricC], loc='upper left')
plt.show()

型

这是不正确的。我把它放在K.clear_session（）之前。首先我得到错误IndexError：列表索引超出范围，因为“历史中的信息是：['loss'，'accuracy']”。然后当我删除metricC = keys[2]时，我没有得到任何可视化

此代码为每个循环添加度量和绘图。注意，对于每个度量，每个时期将有一个测量。所以如果你有一个历元，你绘制的准确度，它只是1点。

from tf.keras import metrics

global_weights = global_model.get_weights()

scaled_local_weight_list = list()

client_names= list(clients_batched.keys())
random.shuffle(client_names)

for client in client_names:
    local_model = Transformer
    local_model.compile(
    loss=tf.keras.losses.CategoricalCrossentropy(),
    optimizer=tf.keras.optimizers.Adam(learning_rate = 0.001),
    metrics=['acc',
             metrics.Precision(),
             metrics.Recall(),
             metrics.AUC()]
    )

    global_model.set_weights(global_weights)

    local_model.set_weights(global_weights)

    history = local_model.fit(clients_batched[client], epochs=1, verbose=0, callbacks=[checkpoint_callback])

    scaling_factor = weight_scalling_factor(clients_batched, client)
    scaled_weights = scale_model_weights(local_model.get_weights(), scaling_factor)
    scaled_local_weight_list.append(scaled_weights)

    # list all data in available in history
    keys = list(history.history.keys())
    print(f'Information available in history for {client} is:\n\t', keys)
    
    # select some metrics to plot from history
    metricA = keys[0]
    metricB = keys[1]
    metricC = keys[2]
    
    #create plot
    plt.plot(history.history[metricA])
    plt.plot(history.history[metricB])
    plt.plot(history.history[metricC])

    plt.title('History')
    plt.ylabel('value')
    plt.xlabel('epoch')
    plt.legend([metricA, metricB, metricC], loc='upper left')
    plt.show()

    K.clear_session()

average_weights = sum_scaled_weights(scaled_local_weight_list)

global_model.set_weights(average_weights)

for(X_test, Y_test) in test_batched:
    global_acc, global_loss = test_model(test_x, test_y, global_model, comm_round + 1)

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