一、Merge和merge
1、Merge和merge区别
Keras中提供了Merge和merge两个不同的功能（新的版本中可能Merge功能已经删除，亲测：Keras2.0.4中Merge和merge功能都是存在的，但是Keras2.2.4中Merge功能已经删除）。
关于这两者的区别，请详见链接：“Merge” versus “merge”, what is the difference?
这里主要提一下主要区别，Merge操作对象是layer， merge操作对象是tensor。具体为（来自上述链接）：
（1）Merge的使用
Merge is a layer.
Merge takes layers as input
Merge is usually used with Sequential models
示例：
left = Sequential()
left.add(...)
left.add(...)
right = Sequential()
right.ad(...)
right.add(...)
model = Sequential()
model.add(Merge([left, right]))
model.add(...)
（2）merge的使用
merge is a function.
merge takes tensors as input.
merge is a wrapper around Merge.
merge is used in Functional API
示例：
a = Input((10,))
b = Dense(10)(a)
c = Dense(10)(a)
d = merge([b, c])
model = Model(a, d)
2、遇到的问题：
在Keras2.2.4中，Merge已经删除，使用merge时会报错： TypeError: ‘module’ object is not callable。
解决方法一：（来自博客keras中merge用法下的评论）
将keras版本降到2.1.6之前（含2.1.6）
解决方法二：
若实现的是concat的功能，则可使用以下方法替代merge([tensor1, tensor2], mode='concat', concat_axis=3)：
from keras.layers import concatenate
merge = concatenate([tensor1, tensor2], axis=3)
二、Model()和Sequencial()
1、Model()和Sequencial()区别
参考博客：Keras Models: Sequential vs. Functional
Sequencial()可以用于逐层创建网络。
Model() 比Sequencial()更灵活，其除了可以逐层创建网络之外，还可以进行2个网络的合并，网络的拼接等。
（1）Sequencial()模型
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(2, input_dim=1))
model.add(Dense(1))
Sequencial()可以用来满足基本的网络训练的功能，也简单易实现，但是其仍具有以下缺点：
①不支持多输入
②不支持多输出
③不支持模型网络层重用
（2）Model()模型
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
# Define the input
# Unlike the Sequential model, you must create and define
# a standalone "Input" layer that specifies the shape of input
# data. The input layer takes a "shape" argument, which is a
# tuple that indicates the dimensionality of the input data.
# When input data is one-dimensional, such as the MLP, the shape
# must explicitly leave room for the shape of the mini-batch size
# used when splitting the data when training the network. Hence,
# the shape tuple is always defined with a hanging last dimension.
# For instance, "(2,)", as in the example below:
visible = Input(shape=(2,))
# Connecting layers
# The layers in the model are connected pairwise.
# This is done by specifying where the input comes from when
# defining each new layer. A bracket notation is used, such that
# after the layer is created, the layer from which the input to
# the current layer comes from is specified.
# Note how the "visible" layer connects to the "Dense" layer:
hidden = Dense(2)(visible)
# Create the model
# After creating all of your model layers and connecting them
# together, you must then define the model.
# As with the Sequential API, the model is the thing that you can
# summarize, fit, evaluate, and use to make predictions.
# Keras provides a "Model" class that you can use to create a model
# from your created layers. It requires that you only specify the
# input and output layers. For example:
model = Model(inputs=visible, outputs=hidden)
Model()模型更灵活，可以很好地解决上述Sequencial()存在的问题。