《python深度学习》学习笔记与代码实现（第七章：7.1：不用Sequential模型的解决方案）_sequential()模型无法使用

python深度学习第七章----高级的深度学习最佳实践

7.1 不用Sequential模型的解决方案

Sequential模型的特点：

1.只有一种输入

例如，我们要同时处理文本数据（全连接层），图片数据（2D卷积层），就不能使用Sequential模型，我们就需要多模态输入，例如，我们的输入数据为元数据，文本描述，图片，来预测一个一个商品的价格，我们就可以将Dense模块，RNN模块，卷积神经网络模块结合起来

2.一种输出

例如，给定一部小说，我们将要他归类，还要预测其写作时间，这时就要两个输出，一个输出用来判别其类别，另一种输出来预测其写作时间

函数式API

from keras.models import Sequential,Model
from keras import layers
from keras import Input

# 之前学习过的Sequential模型
seq_model = Sequential()
seq_model.add(layers.Dense(32,activation = 'relu',input_shape = ((64,))))
seq_model.add(layers.Dense(32,activation = 'relu'))
seq_model.add(layers.Dense(10,activation = 'softmax'))
seq_model.summary()


# 对应的函数式API实现
input_tensor = Input(shape = (64,))
x = layers.Dense(32,activation = 'relu')(input_tensor)
x = layers.Dense(32,activation = 'relu')(x)
output_tensor = layers.Dense(10,activation = 'softmax')(x)

# 后台检索，从输入张量一直到输出张量，检索其每一层，检测输入张量，经过一系列变换，到达输出张量
model = Model(input_tensor,output_tensor)

model.summary()


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_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_4 (Dense)              (None, 32)                2080      
_________________________________________________________________
dense_5 (Dense)              (None, 32)                1056      
_________________________________________________________________
dense_6 (Dense)              (None, 10)                330       
=================================================================
Total params: 3,466
Trainable params: 3,466
Non-trainable params: 0
_________________________________________________________________
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 64)                0         
_________________________________________________________________
dense_7 (Dense)              (None, 32)                2080      
_________________________________________________________________
dense_8 (Dense)              (None, 32)                1056      
_________________________________________________________________
dense_9 (Dense)              (None, 10)                330       
=================================================================
Total params: 3,466
Trainable params: 3,466
Non-trainable params: 0
_________________________________________________________________
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多输入模型

举例：一个问答问题，输入为一个问题和所有问题的文本数据集合，输出为一个回答

from keras.models import Model
from keras import layers
from keras import Input
import keras

text_vocabulary_size = 10000
question_vocabulary_size = 10000
answer_vocabulary_size = 500

# 第一个输入
text_input = Input(shape = (None,),dtype = 'int32',name = 'text')  #文本输入是一个长度可变的整数序列
embedded_text = layers.Embedding(text_vocabulary_size,64)(text_input)
encode_text = layers.LSTM(32)(embedded_text)

# 第二个输入
question_input = Input(shape = (None,),dtype = 'int32',name = 'question')
embedded_question = layers.Embedding(question_vocabulary_size,32)(question_input)
encode_question = layers.LSTM(16)(embedded_question)


# 将两个不同的输入，经过不同的网络，得到的结果级联起来
concatenated = layers.concatenate([encode_text,encode_question],axis = -1)

# 最后添加一个softmax分类器
answer = layers.Dense(answer_vocabulary_size,activation = 'softmax')(concatenated)


model = Model([text_input,question_input],answer)
model.compile(optimizer = 'rmsprop',loss = 'categorical_crossentropy',metrics = ['acc'])

# 将数据输入到多输入模型中

import numpy as np
num_samples= 1000
max_length = 100

# 生成虚构的numpy数据
text = np.random.randint(1,text_vocabulary_size,size = (num_samples,max_length))  #参数，low，high，size
question = np.random.randint(1,question_vocabulary_size,size = (num_samples,max_length))
answers = np.random.randint(answer_vocabulary_size,size = (num_samples))
# 回答进行独热编码
answers = keras.utils.to_categorical(answers,answer_vocabulary_size)

# 两种方法进行模型训练
# 1.使用输入组成的列表
model.fit([text,question],answers,epochs = 10,batch_size = 128)

# 2.使用输入组成的字典
model.fit({'text':text,'question':question},answers,epochs = 10,batch_size = 128)
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Epoch 1/10
1000/1000 [==============================] - 6s 6ms/step - loss: 6.2141 - acc: 0.0020
Epoch 2/10
1000/1000 [==============================] - 3s 3ms/step - loss: 6.1979 - acc: 0.0420
Epoch 3/10
1000/1000 [==============================] - 3s 3ms/step - loss: 6.1546 - acc: 0.0220
Epoch 4/10
1000/1000 [==============================] - 3s 3ms/step - loss: 6.0582 - acc: 0.0060
Epoch 5/10
1000/1000 [==============================] - 3s 3ms/step - loss: 5.9883 - acc: 0.0080
Epoch 6/10
1000/1000 [==============================] - 3s 3ms/step - loss: 5.9284 - acc: 0.0080
Epoch 7/10
1000/1000 [==============================] - 3s 3ms/step - loss: 5.8564 - acc: 0.0100
Epoch 8/10
1000/1000 [==============================] - 3s 3ms/step - loss: 5.7803 - acc: 0.0130
Epoch 9/10
1000/1000 [==============================] - 3s 3ms/step - loss: 5.6730 - acc: 0.0160
Epoch 10/10
1000/1000 [==============================] - 3s 3ms/step - loss: 5.6041 - acc: 0.0230
Epoch 1/10
1000/1000 [==============================] - 3s 3ms/step - loss: 5.5310 - acc: 0.0370
Epoch 2/10
1000/1000 [==============================] - 3s 3ms/step - loss: 5.4517 - acc: 0.0410
Epoch 3/10
1000/1000 [==============================] - 3s 3ms/step - loss: 5.4116 - acc: 0.0430
Epoch 4/10
1000/1000 [==============================] - 3s 3ms/step - loss: 5.3387 - acc: 0.0530
Epoch 5/10
1000/1000 [==============================] - 3s 3ms/step - loss: 5.2483 - acc: 0.0550
Epoch 6/10
1000/1000 [==============================] - 3s 3ms/step - loss: 5.2013 - acc: 0.0540
Epoch 7/10
1000/1000 [==============================] - 3s 3ms/step - loss: 5.1401 - acc: 0.0740
Epoch 8/10
1000/1000 [==============================] - 3s 3ms/step - loss: 5.0840 - acc: 0.0900
Epoch 9/10
1000/1000 [==============================] - 3s 3ms/step - loss: 5.0057 - acc: 0.0870
Epoch 10/10
1000/1000 [==============================] - 3s 3ms/step - loss: 4.9580 - acc: 0.0920
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多输出模型

一个网络，同时预测数据的不同性质

from keras import layers
from keras import Input
from keras.models import Model

vocabulary_size = 50000
num_income_groups = 10

posts_input = Input(shape = (None,),dtype = 'int32',name = 'posts')
embedded_posts = layers.Embedding(256,vocabulary_size)(posts_input)
x = layers.Conv1D(128,5,activation = 'relu')(embedded_posts)
x = layers.MaxPooling1D(5)(x)

x = layers.Conv1D(256,5,activation = 'relu')(x)
x = layers.Conv1D(256,5,activation = 'relu')(x)
x = layers.MaxPooling1D(5)(x)

x = layers.Conv1D(256,5,activation = 'relu')(x)
x = layers.Conv1D(256,5,activation = 'relu')(x)
x = layers.GlobalMaxPooling1D()(x)

age_prediction = layers.Dense(1,name = 'age')(x)

income_prediction = layers.Dense(num_income_groups,activation = 'softmax',name = 'income')(x)

gender_prediction = layers.Dense(1,activation = 'sigmoid',name = 'gender')(x)

model = Model(posts_input,[age_prediction,income_prediction,gender_prediction])

# 将不同预测的损失加在一起，组成一个全局损失，优化这个全局损失

# 两种方法编译模型
# 1.列表级联,还可以给不同的损失加上不同的权重
model.compile(optimizer = 'rmsprop',
              loss = ['mse','categorical_crossentropy','binary_crossentropy'],
              loss_weights = [0.25,1.0,10.])

# 2.用字典
model.compile(optimizer = 'rmsprop',
              loss = {'age':'mse','income':'categorical_crossentropy','gender':'binary_crossentropy'},
             loss_weights = {'age':0.25,'income':1.,'gender':10.})


# 训练模型
# 
model.fit(posts,[age_targets,income_targets,gender_targets],epochs = 10,batch_size = 64)

model.fit(posts,{'age':age_targets,'income':income_targets.'gender':gender_targets},epochs = 10,batch_size = 64)
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层组成的有向无环图

# 1. Inception模块，一个输入，多个分支，一个输出
from keras import layers

x = Input(shape = (None,None,10))
branch_a = layers.Conv2D(128,1,activation = 'relu',strides = 2)(x)

branch_b = layers.Conv2D(128,1,activation = 'relu')(x)
branch_b = layers.Conv2D(128,3,activation = 'relu',strides = 2)(branch_b)

branch_c = layers.AveragePooling2D(3,strides = 2)(x)
branch_c = layers.Conv2D(128,3,activation = 'relu')(branch_c)

branch_d = layers.Conv2D(128,1,activation = 'relu')(x)
branch_d = layers.Conv2D(128,3,activation = 'relu')(branch_d)
branch_d = layers.Conv2D(128,3,activation = 'relu',strides = 2)(branch_d)

# 将不同的分支得到的结果级联起来oo
output = layers.concatenate([branch_a,branch_b,branch_c,branch_d],axis = -1)

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# 2.残差连接
# 假设输入张量是4维的

from keras import layers
# 特征图尺寸相同
x = ...
y = layers.Conv2D(128,3,activation = 'relu',padding = 'same')(x)
y = layers.Conv2D(128,3,activation = 'relu',paddingg = 'same')(y)
y = layers.Conv2D(128,3,activation = 'relu',padding = 'same')(y)

y = layers.add([y,x])  # 将原始x与输出y相加


# 特征图尺寸不同
x = ...
y = layers.Conv2D(128,3,activation = 'relu',padding = 'same')(x)
y = layers.Conv2D(128,3,activation = 'relu',paddingg = 'same')(y)
y = layers.MaxPooling2D(2,strides = 2)(y)

residual = layers.Conv2D(128,1,strides = 2,padding = 'same')(x)

y = layers.add([y,residual])
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共享层权重

from keras import layers
from keras import Input
from keras.models import Model

lstm = layers.LSTM(32)

left_input = Input(shape = (None,128))
left_output = lstm(left_input)

right_input = Input(shape= (None,128))
right_output = lstm(right_input)

merged = layers.concatenate([left_output,right_output],axis = -1)
predictions = layers.Dense(1,activation = 'sigmoid')(megred)

model = Model([left_input,right_input],predictions)
model.fit([left_data,right_data],targets)

# 一个层实例可能被多次重复使用，它可以被调用任意多次，每次都重复使用一组相同的权重

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将模型作为层来调用

# 模型可以看作是更大的层
from keras import layers
from keras import applications
from keras import Input

# 这是一个自带的模型
xception_base = applications.Xception(weights = None,include_top = False)

left_input = Input(shape = (250,250,3))
right_input = Input(shape = (250,250,3))

left_features = xception_base(left_input)
right_features = xception_base(right_input)

megred_features = layers.concatenate([left_features,right_features],axis = -1)

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小结

1.如果你需要实现的架构不仅仅是层的线性堆叠，就不要局限于sequential API

2.使用Keras函数式API来构建多输入模型，多输出模型，和具有复杂的内部网络拓扑结构的模型

3.通过多西调用相同的层实例或者模型实例，