在 Keras 中使用 Eban 等人的召回损失精度

编程问答 2022-05-26

问题描述

我想使用 keras 测试非标准损失，例如 https://arxiv.org/abs/1608.04802 中描述的 precision_at_recall_loss。

这些损失在 loss_layers.py 和 util.py 中实现：https://github.com/tensorflow/models/tree/archive/research/global_objectives

以下代码是使用 MNIST 数据集的演示。

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

import loss_layers
import util 

def precision_recall_auc_loss(y_true,y_pred):
    y_true = keras.backend.reshape(y_true,(batch_size,1)) 
    y_pred = keras.backend.reshape(y_pred,1))   
    util.get_num_labels = lambda labels : 1
    return loss_layers.precision_recall_auc_loss(y_true,y_pred)[0]

(x_train,y_train),(x_test,y_test) = keras.datasets.mnist.load_data()

x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
x_train = np.expand_dims(x_train,-1)
x_test = np.expand_dims(x_test,-1)
input_shape = x_train.shape[1:]

num_classes = 10
y_train = keras.utils.to_categorical(y_train,num_classes)
y_test = keras.utils.to_categorical(y_test,num_classes)

model = keras.Sequential([
        layers.Conv2D(32,kernel_size=(3,3),activation='relu',input_shape=input_shape),\
        layers.MaxPooling2D(2,2),\
        layers.Flatten(),\
        layers.Dropout(0.25),\
        layers.Dense(num_classes,activation="softmax")
    ])

model.summary()
    
batch_size = 30
epochs = 10
target_recall = 0.9

model.compile(loss=precision_recall_auc_loss,optimizer=keras.optimizers.Adam(lr=0.001))

model.fit(x_train,y_train,batch_size=batch_size,\
          epochs=epochs,validation_split=0.15)

模型编译并开始拟合。但是，我收到以下错误：

Train on 51000 samples,validate on 9000 samples
Epoch 1/10
FailedPreconditionError: Attempting to use uninitialized value precision_at_recall_1/lambdas
     [[{{node precision_at_recall_1/lambdas/read}}]]

解决方法

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auckeraskerasprecisionprecision-recalltensorflowtensorflowtensorflow