参考书目：陈允杰.TensorFlow与Keras——Python深度学习应用实战.北京:中国水利水电出版社,2021
本系列基本不讲数学原理，只从代码角度去让读者们利用最简洁的Python代码实现深度学习方法。
Keras框架虽然很简单，但是封装得太好了，自定义的自由度不够高，其搭建神经网络像搭建积木一样容易，却难以更改自己想要的积木的形状和位置。好在其神经网络层可以当成函数调用，这就保证了Keras还是具有一定的自定义能力，可以面向对象编程，下面进行一定的神经网络中间层的张量形状的获取，还有怎么实现共享模型，做到多输入和输出。
取得神经网络层的信息
定义好模型后，我们可以显示神经网络每层的名称和输出张量。如果训练完了还可以显示神经网络的权重。
先导入包
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Dropout定义模型
# 定义模型
model = Sequential()
model.add(Conv2D(16, kernel_size=(5, 5), padding="same",input_shape=(28, 28, 1), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, kernel_size=(5, 5), padding="same",activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(128, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation="softmax"))
model.summary() # 显示模型摘要信息
查看有多少层
# 显示各神经层
print("神经层数: ", len(model.layers))
for i in range(len(model.layers)):
print(i, model.layers[i].name) 
print("每一层的输入张量 ")
for i in range(len(model.layers)):
print(i, model.layers[i].input) 
print("每一层的输出张量: ")
for i in range(len(model.layers)):
print(i, model.layers[i].output) 
#显示各层的权重形状
for i in range(len(model.layers)):
print(i, model.layers[i].name, ":")
weights = model.layers[i].get_weights()
for j in range(len(weights)):
print("==>", j, weights[j].shape)可以读取的模型，查看其训练的权重
model = Sequential()
model = load_model("imdb_gru.h5")
model.summary() # 显示模型摘要信息
# 编译模型
model.compile(loss="binary_crossentropy", optimizer="rmsprop", metrics=["accuracy"])
# 显示GRU层的权重形状
print(2, model.layers[2].name, ":")
weights = model.layers[2].get_weights()
for i in range(len(weights)):
print("==>", i,weights[i].shape) 
for i in range(len(model.layers)):
print(i, model.layers[i].name)
print(i, model.layers[i].input)
print(i, model.layers[i].output)
print('\n')
使用Functional API进行搭建
以前Kears搭建模型都是上面那种，类似搭积木，下面使用函数的形式去搭建。
搭建这样一个网络
# 定义模型
mnist_input = Input(shape=(28, 28, 1),
name="input")
conv1 = Conv2D(16, kernel_size=(5, 5), padding="same",
activation="relu", name="conv1")(mnist_input)
pool1 = MaxPooling2D(pool_size=(2, 2),
name="pool1")(conv1)
conv2 = Conv2D(32, kernel_size=(5, 5), padding="same",
activation="relu", name="conv2")(pool1)
pool2 = MaxPooling2D(pool_size=(2, 2),
name="pool2")(conv2)
drop1 = Dropout(0.5, name="drop1")(pool2)
flat = Flatten(name="flat")(drop1)
hidden1 = Dense(128, activation="relu", name="hidden1")(flat)
drop2 = Dropout(0.5, name="drop2")(hidden1)
output = Dense(10, activation="softmax",
name="output")(drop2)
model = Model(inputs=mnist_input, outputs=output)
model.summary() # 显示模型摘要信息
或者搭建这样一个LSTM网络
# 定义模型
imdb_input = Input(shape=(100,), dtype="int32",
name="imdb_input")
embed = Embedding(top_words, 32, input_length=max_words,
name="embed")(imdb_input)
drop1 = Dropout(0.25, name="drop1")(embed)
lstm = LSTM(32, name="lstm")(drop1)
drop2 = Dropout(0.25, name="drop2")(lstm)
output = Dense(1, activation="sigmoid",
name="output")(drop2)
model = Model(inputs=imdb_input, outputs=output)
model.summary() #显示模型摘要信息共享层模型
前面的模型都是序列模型，即一层一层的搭建，但是有的网络可以多层一起搭建，例如下面这个模型，然后代码如下：
from keras.models import Model
from keras.layers import Input, Dense, Flatten, Conv2D, MaxPooling2D
from keras.layers.merge import concatenate
#定义模型
shared_input = Input(shape=(64, 64, 1))
# 第1个共享输入层的卷积和池化层
conv1 = Conv2D(32, kernel_size=3, activation="relu")(shared_input)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
flat1 = Flatten()(pool1)
# 第2个共享输入层的卷积和池化层
conv2 = Conv2D(16, kernel_size=5, activation="relu")(shared_input)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
flat2 = Flatten()(pool2)
#合并 2 个共享输入层的卷积和池化层
merge = concatenate([flat1, flat2])
hidden1 = Dense(10, activation="relu")(merge)
output = Dense(1, activation="sigmoid")(hidden1)
model = Model(inputs=shared_input, outputs=output)
model.summary() # 显示模型摘要信息
from keras.utils import plot_model
plot_model(model, to_file="Ch16_4_1.png", show_shapes=True)
也可以不对称，两边随便弄几层，例如这样的
from keras.models import Model
from keras.layers import Input, Dense, LSTM
from keras.layers.merge import concatenate
#定义模型
model_input = Input(shape=(100, 1))
lstm = LSTM(32)(model_input)
# 第 1 个共享特征提取层的解释层
extract1 = Dense(16, activation="relu")(lstm)
# 第 2 个共享特征提取层的解释层
dense1 = Dense(16, activation="relu")(lstm)
dense2 = Dense(32, activation="relu")(dense1)
extract2 = Dense(16, activation='relu')(dense2)
# 合并 2 个共享特征提取层的解释层
merge = concatenate([extract1, extract2])
output = Dense(1, activation="sigmoid")(merge)
model = Model(inputs=model_input, outputs=output)
model.summary() #显示模型摘要信息
多输入和输出模型
更加复杂的模型，有多对输入数据，例如图片有彩色和灰白，销售量有季节、温度、折扣、成本等很多因素影响。这就涉及到需要多输入和输出了。下面构建一个对彩色和灰白图片一起输入的模型：
多输入模型为
from keras.models import Model
from keras.layers import Input, Dense, Flatten, Conv2D, MaxPooling2D
from keras.layers.merge import concatenate
# 定义模型
# 第 1 个灰度图片输入
input1 = Input(shape=(28, 28, 1))
conv11 = Conv2D(16, (3,3), activation="relu")(input1)
pool11 = MaxPooling2D(pool_size=(2,2))(conv11)
conv12 = Conv2D(32, (3,3), activation="relu")(pool11)
pool12 = MaxPooling2D(pool_size=(2,2))(conv12)
flat1 = Flatten()(pool12)
# 第 2 个彩色图片输入
input2 = Input(shape=(28, 28, 3))
conv21 = Conv2D(16, (3,3), activation="relu")(input2)
pool21 = MaxPooling2D(pool_size=(2,2))(conv21)
conv22 = Conv2D(32, (3,3), activation="relu")(pool21)
pool22 = MaxPooling2D(pool_size=(2,2))(conv22)
flat2 = Flatten()(pool22)
# 合并 2 个输入
merge = concatenate([flat1, flat2])
dense1 = Dense(512, activation="relu")(merge)
dense2 = Dense(128, activation="relu")(dense1)
dense3 = Dense(32, activation="relu")(dense2)
output = Dense(10, activation="softmax")(dense3)
# 定义多输入模型
model = Model(inputs=[input1, input2], outputs=output)
model.summary() # 显示模型摘要信息
多输出模型为：
from keras.models import Model
from keras.layers import Dense, Input
# 定义模型
model_input = Input(shape = (784,))
dense1 = Dense(512, activation="relu")(model_input)
dense2 = Dense(128, activation="relu")(dense1)
dense3 = Dense(32, activation ="relu")(dense2)
# 第 1 个分类输出
output = Dense(10, activation="softmax")(dense3)
# 第 2 个自编码器输出
up_dense1 = Dense(128, activation="relu")(dense3)
up_dense2 = Dense(512, activation="relu")(up_dense1)
decoded_outputs = Dense(784)(up_dense2)
# 定义多输出模型
model = Model(model_input, [output, decoded_outputs])
model.summary() # 显示模型摘要信息
mnist案例
使用手写数字数据集做上面两个模型的案例。
多输入
由于手写数字集没有彩色，那就改为两个黑白输入(这个案例里面这样做对精度没啥提升作用….只是演示)
from keras.models import Model
from keras.layers import Input, Dense, Flatten, Conv2D, MaxPooling2D
from keras.layers.merge import concatenate
from keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
# 定义模型
# 第 1 个灰度图片输入
input1 = Input(shape=(28, 28, 1))
conv11 = Conv2D(16, (3,3), activation="relu")(input1)
pool11 = MaxPooling2D(pool_size=(2,2))(conv11)
conv12 = Conv2D(32, (3,3), activation="relu")(pool11)
pool12 = MaxPooling2D(pool_size=(2,2))(conv12)
flat1 = Flatten()(pool12)
# 第 2 个彩色图片输入
input2 = Input(shape=(28, 28, 1))
conv21 = Conv2D(16, (3,3), activation="relu")(input2)
pool21 = MaxPooling2D(pool_size=(2,2))(conv21)
conv22 = Conv2D(32, (3,3), activation="relu")(pool21)
pool22 = MaxPooling2D(pool_size=(2,2))(conv22)
flat2 = Flatten()(pool22)
# 合并 2 个输入
merge = concatenate([flat1, flat2])
dense1 = Dense(512, activation="relu")(merge)
dense2 = Dense(128, activation="relu")(dense1)
dense3 = Dense(32, activation="relu")(dense2)
output = Dense(10, activation="softmax")(dense3)
# 定义多输入模型
model = Model(inputs=[input1, input2], outputs=output)
model.summary() # 显示模型摘要信息
import numpy as np
# 指定乱数种子
seed = 7
np.random.seed(seed)
# 载入数据集
(X_train, Y_train), (X_test, Y_test) = mnist.load_data()
# 将图片转换成 4D 张量
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype("float32")
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype("float32")
# 因为是固定范围, 所以执行正规化, 从 0-255 至 0-1
X_train = X_train / 255
X_test = X_test / 255
# One-hot编码
Y_train = to_categorical(Y_train)
Y_test = to_categorical(Y_test)
#编译模型
model.compile(loss="categorical_crossentropy", optimizer="adam",metrics=["accuracy"])
# 训练模型
history = model.fit([X_train,X_train], Y_train,validation_split=0.2,
epochs=10, batch_size=128, verbose=2)
model.evaluate([X_test,X_test],Y_test) 
多输出
from keras.models import Sequential
from keras.models import load_model
# 建立Keras的Sequential模型
model = Sequential()
model = load_model("mnist.h5")
model.summary() # 显示模型摘要信息
# 编译模型
model.compile(loss="categorical_crossentropy", optimizer="adam",metrics=["accuracy"])
# 使用 Model 建立 前4层的 Conv2D 和 MaxPooling 层
# 此模型是 1 个输入, 和 4 个输出
layer_outputs = [layer.output for layer in model.layers[:4]]
model_test = Model(inputs=model.input, outputs=layer_outputs)
outputs = model_test.predict(X_train[1].reshape(1,28,28,1))
# 取得第1个 Conv2D 的输出
output = outputs[0]
output.shape
import matplotlib.pyplot as plt
#绘出第1个 Conv2D 层的输出
plt.figure(figsize=(10,8))
for i in range(0,16):
plt.subplot(4,4,i+1)
plt.imshow(output[0,:,:,i], cmap="gray")
plt.axis("off") 
# 取得第1个 MaxPooling 的输出
output = outputs[1]
# 绘出第1个 MaxPooling 层的输出
plt.figure(figsize=(10,8))
for i in range(0,16):
plt.subplot(4,4,i+1)
plt.imshow(output[0,:,:,i], cmap="gray")
plt.axis("off")
# 取得第2个 Conv2D 的输出
output = outputs[2]
# 绘出第2个 Conv2D 层的输出
plt.figure(figsize=(10,8))
for i in range(0,32):
plt.subplot(6,6,i+1)
plt.imshow(output[0,:,:,i], cmap="gray")
plt.axis("off") 
# 取得第2个 MaxPooling 的输出
output = outputs[3]
# 绘出第2个 MaxPooling 层的输出
plt.figure(figsize=(10,8))
for i in range(0,32):
plt.subplot(6,6,i+1)
plt.imshow(output[0,:,:,i], cmap="gray")
plt.axis("off")
这个多输出模型是将每一步的中间层都输出了，可以查看模型学到了什么。算是模型中间层的可视化
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