fit(train_x, train_y, batchsize, epochs)

fit_generator；常常使用的模型训练函数

train_on_batch(batchX, batchY)

keras.utils.Sequence：这个基类通常应用于数据集生成一个数据序列。使用时需构建一个python类继承自该
基类，并必须实现__len__和__getitem__两个函数，如果要在每个epoch间修改数据集则需要实现on_epoch_end
方法。
NOTE：特别注意，__getitem__要返回一个完整的batchsize数据，__len__统计的也是有多少个batch

class PascalVocGenerator(Generator)
class CocoGenerator(Generator)

from keras.applications.resnet50 import ResNet50

loss_ = Lambda(loss, name='centernet_loss')([y1, y2, y3, hm_input, wh_input, reg_input, reg_mask_input, index_input])
model = Model(inputs=[image_input, hm_input, wh_input, reg_input, reg_mask_input, index_input], outputs=[loss_])

model.load_weights(args.snapshot, by_name=True, skip_mismatch=True)

model.compile(optimizer=Adam(lr=1e-3), loss={'centernet_loss': lambda y_true, y_pred: y_pred})def compile(self, optimizer,loss=None,metrics=None,loss_weights=None,sample_weight_mode=None,weighted_metrics=None,target_tensors=None,**kwargs):"""Configures the model for training.# Argumentsoptimizer: String (name of optimizer) or optimizer instance.See [optimizers](/optimizers).loss: String (name of objective function) or objective function.See [losses](/losses).If the model has multiple outputs, you can use a different losson each output by passing a dictionary or a list of losses.The loss value that will be minimized by the modelwill then be the sum of all individual losses.metrics: List of metrics to be evaluated by the modelduring training and testing.Typically you will use `metrics=['accuracy']`.To specify different metrics for different outputs of amulti-output model, you could also pass a dictionary,such as `metrics={'output_a': 'accuracy'}`.loss_weights: Optional list or dictionary specifying scalarcoefficients (Python floats) to weight the loss contributionsof different model outputs.The loss value that will be minimized by the modelwill then be the *weighted sum* of all individual losses,weighted by the `loss_weights` coefficients.If a list, it is expected to have a 1:1 mappingto the model's outputs. If a dict, it is expected to mapoutput names (strings) to scalar coefficients.sample_weight_mode: If you need to do timestep-wisesample weighting (2D weights), set this to `"temporal"`.`None` defaults to sample-wise weights (1D).If the model has multiple outputs, you can use a different`sample_weight_mode` on each output by passing adictionary or a list of modes.weighted_metrics: List of metrics to be evaluated and weightedby sample_weight or class_weight during training and testing.target_tensors: By default, Keras will create placeholders for themodel's target, which will be fed with the target data duringtraining. If instead you would like to use your owntarget tensors (in turn, Keras will not expect externalNumpy data for these targets at training time), youcan specify them via the `target_tensors` argument. It can bea single tensor (for a single-output model), a list of tensors,or a dict mapping output names to target tensors.**kwargs: When using the Theano/CNTK backends, these argumentsare passed into `K.function`.When using the TensorFlow backend,these arguments are passed into `tf.Session.run`.