keras中models的Squential类的源码简介

keras中最重要的就是models的Sequential类了，下面我结合源码以及自己的理解，尽可能的去学习并能够说明白，源代码太多，先贴一个fit函数的实现：

def fit(self, x, y, batch_size=32, nb_epoch=10, verbose=1, callbacks=[],
validation_split=0., validation_data=None, shuffle=True,
class_weight=None, sample_weight=None, **kwargs):
'''

Args:
x: 表示输入可以是narray, 如果是多个输入,也可以是[narray, narray], 必须有
y: labels,only a narray, 必须有
batch_size: mini batch表示多少次更新一次权重,默认是32
nb_epoch: 需要迭代多少次去训练这个模型,默认是10
verbose: 是不是输出打印log到标准输出,默认是打印
callbacks: 回调函数(暂时不是很理解这个地方怎么用)
validation_split: 测试数据的比例,默认是0
validation_data: 测试数据,tuple(input , lable)默认是空
shuffle:不懂
class_weight:不懂
sample_weight:不懂
**kwargs: 只有一个候选项就是 'show_accuracy'

Returns:

'''
'''Trains the model for a fixed number of epochs.

# Arguments
x: input data, as a Numpy array or list of Numpy arrays
(if the model has multiple inputs).
y: labels, as a Numpy array.
batch_size: integer. Number of samples per gradient update.
nb_epoch: integer, the number of epochs to train the model.
verbose: 0 for no logging to stdout,
1 for progress bar logging, 2 for one log line per epoch.
callbacks: list of `keras.callbacks.Callback` instances.
List of callbacks to apply during training.
See [callbacks](/callbacks).
validation_split: float (0. < x < 1).
Fraction of the data to use as held-out validation data.
validation_data: tuple (X, y) to be used as held-out
validation data. Will override validation_split.
shuffle: boolean or str (for 'batch').
Whether to shuffle the samples at each epoch.
'batch' is a special option for dealing with the
limitations of HDF5 data; it shuffles in batch-sized chunks.
class_weight: dictionary mapping classes to a weight value,
used for scaling the loss function (during training only).
sample_weight: Numpy array of weights for
the training samples, used for scaling the loss function
(during training only). You can either pass a flat (1D)
Numpy array with the same length as the input samples
(1:1 mapping between weights and samples),
or in the case of temporal data,
you can pass a 2D array with shape (samples, sequence_length),
to apply a different weight to every timestep of every sample.
In this case you should make sure to specify
sample_weight_mode="temporal" in compile().

# Returns
A `History` object. Its `History.history` attribute is
a record of training loss values and metrics values
at successive epochs, as well as validation loss values
and validation metrics values (if applicable).
'''
if self.model is None:
raise Exception('The model needs to be compiled before being used.')
if 'show_accuracy' in kwargs:
kwargs.pop('show_accuracy')
warnings.warn('The "show_accuracy" argument is deprecated, '
'instead you should pass the "accuracy" metric to '
'the model at compile time:\n'
'`model.compile(optimizer, loss, '
'metrics=["accuracy"])`')
if kwargs:
raise Exception('Received unknown keyword arguments: ' +
str(kwargs))
return self.model.fit(x, y,
batch_size=batch_size,
nb_epoch=nb_epoch,
verbose=verbose,
callbacks=callbacks,
validation_split=validation_split,
validation_data=validation_data,
shuffle=shuffle,
class_weight=class_weight,
sample_weight=sample_weight)

主要是学会怎么使用，因为这段代码放到整个类中去看才有意义，所以，后续继续补充吧， 发现欠了好多债了，后续需要补充的东西太多了，逼我把源码看完的节奏。