下面的示例可能是不言自明的！“虚拟”模型采用1个输入（图像），并输出2个值。该模型为每个输出计算MSE。
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过程中提供模型。它采用训练数据x和标签y，其中y = [y1，y2]
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（）
model.fit_generator(batch_generator(X_train, y_train, batch_size, is_train=1))
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