python - 在 keras 中使用数据生成器时如何输出 ypred 和 ytrue

Question

我正在使用数据生成器训练 keras 模型，该数据生成器从目录中批量读取数据。这适用于model.fit(). 但是在使用时model.predict()，我希望同时返回ypred和ytrue值。

我可以启用/修改 model.predict() 来执行此操作（可能使用自定义回调）吗？

class DataGenerator(tf.keras.utils.Sequence):
    def__init__(self, ids, batch_size=256):
        self.batch_size=batch_size
        self.ids = ids
    
    def __len__(self):
        return(self.ids)
    
    def __getitem__(self, index):
        X, y = np.load(f'data/{index}.npy', allow_pickle=True)
        return X, y
    
    def on_epoch_end(self):
        '''Shuffle ids in each epoch'''
        self.ids = np.random.choice(self.ids, len(self.ids), replace=False)



model = buildModel() #builds a multilayer perceptron
train_ids = np.arange(10000)  #training data are in data/0.npy, data/1.npy, ... data/9999.npy
val_ids = np.arange(10000, 12000)

train_generator = DataGenerator(train_ids)
val_generator = DataGenerator(val_ids)

# Train model
history = model.fit(x=train_generator, epochs=100)

# Validate model (but I don't have ytrue)
ypred = model.predict(x=val_generator).reshape(-1)


# What I would like to achieve
(ypred, ytrue) = model.predict(x=val_generator, callbacks=[some_custom_callback])

# Or
ypred = model.predict(x=val_generator)
ytrue = some_fancy_method(val_generator)

标签： pythontensorflowkerascallbackpredict

Answer 1

这可以通过向您的DataGenerator类添加一个方法来完成，该方法将拟合模型作为输入，将其应用于生成的数据批次，然后返回ytrue和ypred。

class DataGenerator(tf.keras.utils.Sequence):
    def__init__(self, ids, batch_size=256):
        self.batch_size=batch_size
        self.ids = ids
    
    def __len__(self):
        return(self.ids)
    
    def __getitem__(self, index):
        X, y = self.load_data(index)
        return X, y

    def load_data(self, index):
        X, y = np.load(f'data/{index}.npy', allow_pickle=True)
        return X, y

    def predict(self, model):
        ytrue, ypred = [], []
        for index in self.ids:
            X, y = self.load_data(index)
            pred = model.predict(X).reshape(-1)
            ytrue.extend(y)
            ypred.extend(pred)
        return ytrue, ypred

    def on_epoch_end(self):
        '''Shuffle ids in each epoch'''
        self.ids = np.random.choice(self.ids, len(self.ids), replace=False)



train_generator = DataGenerator(train_ids)
val_generator = DataGenerator(val_ids)

# Train model
history = model.fit(x=train_generator, epochs=100)

# Validate model 
ypred, ytrue = val_generator.predict(model)