python - 具有多个输入层的 Keras fit_generator

Question

我正在尝试为具有 3 个输入和一个处理文本数据的单个输出的模型实现自定义数据生成器，如下所示:

# dummy model
input_1 = Input(shape=(None,))
input_2 = Input(shape=(None,))
input_3 = Input(shape=(None,))     
combined = Concatenate(axis=-1)([input_1, input_2, input_3])
...
dense_1 = Dense(10, activation='relu')(combined)
output_1 = Dense(1, activation='sigmoid')(dense_1)

model = Model([input_1, input_2, input_3], output_1)
print(model.summary())

#Compile and fit_generator
model.compile(optimizer='adam', loss='binary_crossentropy')

train_data_gen = Generator([x1_train, x2_train, x3_train], y_train, batch_size)
test_data_gen = Generator([x1_test, x2_test, x3_test], y_test, batch_size)

model.fit_generator(generator=train_data_gen, validation_data = test_data_gen, epochs=epochs, verbose=1)

我找到的数据生成器代码here ，我想知道如何修改它以接受多个输入张量。

class Generator(Sequence):
    # Class is a dataset wrapper for better training performance
    def __init__(self, x_set, y_set, batch_size=256):
        self.x, self.y = x_set, y_set
        self.batch_size = batch_size
        self.indices = np.arange(self.x.shape[0])

    def __len__(self):
        return math.floor(self.x.shape[0] / self.batch_size)

    def __getitem__(self, idx):
        inds = self.indices[idx * self.batch_size:(idx + 1) * self.batch_size]
        batch_x = self.x[inds]
        batch_y = self.y[inds]
        return batch_x, batch_y

    def on_epoch_end(self):
        np.random.shuffle(self.indices)

Answer 1

您需要做的就是如下修改 Generator 类。

class Generator(Sequence):
    # Class is a dataset wrapper for better training performance
    def __init__(self, x_set, y_set, batch_size=256):
        self.x, self.y = x_set, y_set
        self.batch_size = batch_size
        self.indices = np.arange(self.x[0].shape[0])

    def __len__(self):
        return math.floor(self.x[0].shape[0] / self.batch_size)

    def __getitem__(self, idx):
        inds = self.indices[idx * self.batch_size:(idx + 1) * self.batch_size]
        batch_x = [self.x[0][inds],self.x[1][inds],self.x[2][inds]]
        batch_y = self.y[inds]
        return batch_x, batch_y

    def on_epoch_end(self):
        np.random.shuffle(self.indices)