Keras:如何将 fit_generator 与不同类型的多个输出一起使用 [英] Keras: How to use fit_generator with multiple outputs of different type

问题描述

在具有功能 API 的 Keras 模型中，我需要调用 fit_generator 以使用 ImageDataGenerator 训练增强图像数据.
问题是我的模型有两个输出:我试图预测的掩码和一个二进制值.
我显然只想增加输入和掩码输出，而不是二进制值.
我怎样才能做到这一点?

In a Keras model with the Functional API I need to call fit_generator to train on augmented images data using an ImageDataGenerator.
The problem is my model has two outputs: the mask I'm trying to predict and a binary value.
I obviously only want to augment the input and the mask output and not the binary value.
How can I achieve this?

推荐答案

下面的例子可能不言自明！虚拟"模型接受 1 个输入(图像)并输出 2 个值.该模型计算每个输出的 MSE.

The example below might be self-explanatory! The 'dummy' model takes 1 input (image) and it outputs 2 values. The model computes the MSE for each output.

x = Convolution2D(8, 5, 5, subsample=(1, 1))(image_input)
x = Activation('relu')(x)
x = Flatten()(x)
x = Dense(50, W_regularizer=l2(0.0001))(x)
x = Activation('relu')(x)

output1 = Dense(1, activation='linear', name='output1')(x)
output2 = Dense(1, activation='linear', name='output2')(x)

model = Model(input=image_input, output=[output1, output2])
model.compile(optimizer='adam', loss={'output1': 'mean_squared_error', 'output2': 'mean_squared_error'})

下面的函数生成批次以在训练期间为模型提供数据.它需要训练数据 x 和标签 y 其中 y=[y1, y2]

The function below generates batches to feed the model during training. It takes the training data x and the label y where y=[y1, y2]

def batch_generator(x, y, batch_size, is_train):
    sample_idx = 0
    while True:
       X = np.zeros((batch_size, input_height, input_width, n_channels), dtype='float32')
       y1 = np.zeros((batch_size, mask_height, mask_width), dtype='float32')
       y2 = np.zeros((batch_size, 1), dtype='float32')

       # fill up the batch
       for row in range(batch_sz):
           image = x[sample_idx]
           mask = y[0][sample_idx]
           binary_value = y[1][sample_idx]
           # transform/preprocess image
           image = cv2.resize(image, (input_width, input_height))
           if is_train:
               image, mask = my_data_augmentation_function(image, mask)
           X_batch[row, ;, :, :] = image
           y1_batch[row, :, :] = mask
           y2_batch[row, 0] = binary_value
           sample_idx += 1

       # Normalize inputs
       X_batch = X_batch/255.
       yield(X_batch, {'output1': y1_batch, 'output2': y2_batch} ))

最后，我们调用 fit_generator()

Finally, we call the fit_generator()

model.fit_generator(batch_generator(X_train, y_train, batch_size, is_train=1))

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