文章目录

• 作业1：Keras教程
• • 1. 快乐的房子
  • 2. 用Keras建模
  • 3. 用你的图片测试
  • 4. 一些有用的Keras函数
• 作业2：残差网络 Residual Networks
• • 1. 深层神经网络的问题
  • 2. 建立残差网络
  • • 2.1 identity恒等模块
    • 2.2 卷积模块
  • 3. 建立你的第一个残差网络（50层）
  • 4. 用自己的照片测试

测试题：参考博文

笔记：04.卷积神经网络 W2.深度卷积网络：实例探究

作业1：Keras教程

Keras 是一个用 Python 编写的高级神经网络 API，它能够以 TensorFlow, CNTK, 或者 Theano 作为后端运行。
Keras 的开发重点是支持快速的实验。能够以最小的时延把你的想法转换为实验结果，是做好研究的关键。

Keras 是更高级的框架，对普通模型来说很友好，但是要实现更复杂的模型需要 TensorFlow 等低级的框架

• 导入一些包

import numpy as np
from keras import layers
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.models import Model
from keras.preprocessing import image
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import preprocess_input
import pydot
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
from kt_utils import *

import keras.backend as K
K.set_image_data_format('channels_last')
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow

%matplotlib inline

1. 快乐的房子

问题背景：快乐的房子的门口的摄像头会识别你的表情是否是 Happy 的，是 Happy 的，门才会打开，哈哈！

我们要建模自动识别表情是否快乐！

• 归一化图片数据，了解数据维度

X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()

# Normalize image vectors
X_train = X_train_orig/255.
X_test = X_test_orig/255.

# Reshape
Y_train = Y_train_orig.T
Y_test = Y_test_orig.T

print ("number of training examples = " + str(X_train.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(Y_train.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(Y_test.shape))

输出：

number of training examples = 600
number of test examples = 150
X_train shape: (600, 64, 64, 3)
Y_train shape: (600, 1)
X_test shape: (150, 64, 64, 3)
Y_test shape: (150, 1)

600个训练样本，150个测试样本，图片维度 64*64*3 = 12288

2. 用Keras建模

Keras 可以快速建模，且模型效果不错

举个例子：

def model(input_shape):
    # 定义输入的 placeholder 作为 tensor with shape input_shape. 
    # 想象这是你的图片输入
    X_input = Input(input_shape)

    # Zero-Padding: pads the border of X_input with zeroes
    X = ZeroPadding2D((3, 3))(X_input)

    # CONV -> BN -> RELU Block applied to X
    X = Conv2D(32, (7, 7), strides = (1, 1), name = 'conv0')(X)
    X = BatchNormalization(axis = 3, name = 'bn0')(X)
    X = Activation('relu')(X)

    # MAXPOOL
    X = MaxPooling2D((2, 2), name='max_pool')(X)

    # FLATTEN X (means convert it to a vector) + FULLYCONNECTED
    X = Flatten()(X)
    X = Dense(1, activation='sigmoid', name='fc')(X)

    # Create model. This creates your Keras model instance, 
    # you'll use this instance to train/test the model.
    model = Model(inputs = X_input, outputs = X, name='HappyModel')

    return model

本次作业很open，可以自由搭建模型，修改超参数，请注意各层之间的维度匹配

Keras Model 类参考链接

• 定义模型

# GRADED FUNCTION: HappyModel

def HappyModel(input_shape):
    """ Implementation of the HappyModel. Arguments: input_shape -- shape of the images of the dataset Returns: model -- a Model() instance in Keras """
    
    ### START CODE HERE ###
    # Feel free to use the suggested outline in the text above to get started, and run through the whole
    # exercise (including the later portions of this notebook) once. The come back also try out other
    # network architectures as well. 
    X_input = Input(input_shape)
    
    X = ZeroPadding2D((3,3))(X_input)
    
    X = Conv2D(32,(7,7), strides = (1,1), name='conv0')(X)
    X = BatchNormalization(axis = 3, name='bn0')(X)
    X = Activation('relu')(X)
    
    X = MaxPooling2D((2,2), name='max_pool')(X)
    
    X = Flatten()(X)
    X = Dense(1, activation='sigmoid', name='fc')(X)
    
    model = Model(inputs = X_input, outputs = X, name='HappyModel')
    
    ### END CODE HERE ###
    
    return model

• 创建模型实例

happyModel = HappyModel(X_train[0].shape)

• 配置训练模型

import keras
opt = keras.optimizers.Adam(learning_rate=0.01)
happyModel.compile(optimizer=opt, 
		loss=keras.losses.BinaryCrossentropy(),metrics=['acc'])

• 训练 并 存储返回的训练过程数据用于可视化

history = happyModel.fit(x=X_train, y=Y_train, 
		validation_split=0.25, batch_size=32, epochs=30)

• 绘制训练过程

# 绘制训练 & 验证的准确率值
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()

# 绘制训练 & 验证的损失值
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()

。。。省略
Epoch 29/30
15/15 [==============================] 
- 2s 148ms/step - loss: 0.1504 - acc: 0.9733 - val_loss: 0.1518 - val_acc: 0.9600
Epoch 30/30
15/15 [==============================] 
- 2s 147ms/step - loss: 0.1160 - acc: 0.9711 - val_loss: 0.2242 - val_acc: 0.9333

• 测试模型效果

### START CODE HERE ### (1 line)
from keras import metrics
preds = happyModel.evaluate(X_test, Y_test, batch_size=32, verbose=1, sample_weight=None)
### END CODE HERE ###
print(preds)

print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1]))

输出：

5/5 [==============================] - 0s 20ms/step - loss: 0.2842 - acc: 0.9400
[0.28415805101394653, 0.9399999976158142]
Loss = 0.28415805101394653
Test Accuracy = 0.9399999976158142

3. 用你的图片测试

### START CODE HERE ###
img_path = 'images/1.jpg'
### END CODE HERE ###
img = image.load_img(img_path, target_size=(64, 64))
imshow(img)

x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

print(happyModel.predict(x))

4. 一些有用的Keras函数

• happyModel.summary() 模型的结构，参数等信息

happyModel.summary()

Model: "HappyModel"
_________________________________________________________________
Layer (type)                 Output Shape              Param # 
=================================================================
input_1 (InputLayer)         [(None, 64, 64, 3)]       0         
_________________________________________________________________
zero_padding2d (ZeroPadding2 (None, 70, 70, 3)         0         
_________________________________________________________________
conv0 (Conv2D)               (None, 64, 64, 32)        4736      
_________________________________________________________________
bn0 (BatchNormalization)     (None, 64, 64, 32)        128       
_________________________________________________________________
activation (Activation)      (None, 64, 64, 32)        0         
_________________________________________________________________
max_pool (MaxPooling2D)      (None, 32, 32, 32)        0         
_________________________________________________________________
flatten (Flatten)            (None, 32768)             0         
_________________________________________________________________
fc (Dense)                   (None, 1)                 32769     
=================================================================
Total params: 37,633
Trainable params: 37,569
Non-trainable params: 64
_________________________________________________________________

• plot_model() 把模型结构保存成图片

作业2：残差网络 Residual Networks

使用残差网络能够训练更深的神经网络，普通的深层神经网络是很难训练的。

• 导入包

import numpy as np
from keras import layers
from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D
from keras.models import Model, load_model
from keras.preprocessing import image
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import preprocess_input
import pydot
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
from resnets_utils import *
from keras.initializers import glorot_uniform
import scipy.misc
from matplotlib.pyplot import imshow
%matplotlib inline

import keras.backend as K
K.set_image_data_format('channels_last')
K.set_learning_phase(1)

1. 深层神经网络的问题

深层网络优点：

• 可以表示复杂的函数
• 可以学习很多不同层次的特征（低层次，高层次）

缺点：

• 梯度消失/爆炸，梯度变的非常小或者非常大

随着迭代次数增加，浅层的梯度很快的就降到 0

2. 建立残差网络

通过跳跃的连接，允许梯度直接反向传到浅层

• 跳跃连接使得模块更容易学习恒等函数
• 残差模块不会损害训练效果

残差网络有两种类型的模块，主要取决于输入输出的维度是否一样

2.1 identity恒等模块

下面我们要实现：跳过3个隐藏层的结构，其稍微更强大一些

convolution2d 参考：https://keras.io/api/layers/convolution_layers/convolution2d/

batch_normalization 参考：
https://keras.io/api/layers/normalization_layers/batch_normalization/

add 参考：
https://keras.io/api/layers/merging_layers/add/

# GRADED FUNCTION: identity_block

def identity_block(X, f, filters, stage, block):
    """ Implementation of the identity block as defined in Figure 3 Arguments: X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev) f -- integer, specifying the shape of the middle CONV's window for the main path filters -- python list of integers, defining the number of filters in the CONV layers of the main path stage -- integer, used to name the layers, depending on their position in the network block -- string/character, used to name the layers, depending on their position in the network Returns: X -- output of the identity block, tensor of shape (n_H, n_W, n_C) """
    
    # defining name basis
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'
    
    # Retrieve Filters
    F1, F2, F3 = filters
    
    # Save the input value. You'll need this later to add back to the main path. 
    X_shortcut = X
    
    # First component of main path
    X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)
    X = Activation('relu')(X)
    
    ### START CODE HERE ###
    
    # Second component of main path (≈3 lines)
    X = Conv2D(filters = F2, kernel_size=(f, f), strides = (1, 1), padding='same', name=conv_name_base+'2b', kernel_initializer=glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis=3, name=bn_name_base+'2b')(X)
    X = Activation('relu')(X)
    
    # Third component of main path (≈2 lines)
    X = Conv2D(filters=F3, kernel_size=(1,1), strides=(1,1), padding='valid', name=conv_name_base+'2c', kernel_initializer=glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis=3, name=bn_name_base+'2c')(X)

    # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)
    X = Add()([X_shortcut, X])
    X = Activation('relu')(X)
    
    ### END CODE HERE ###
    
    return X

测试代码：

# import tensorflow.compat.v1 as tf
# tf.disable_v2_behavior()

tf.reset_default_graph()

with tf.Session() as test:
    np.random.seed(1)
    A_prev = tf.placeholder("float", [3, 4, 4, 6])
    X = np.random.randn(3, 4, 4, 6)
    A = identity_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')
    test.run(tf.global_variables_initializer())
    out = test.run([A], feed_dict={ A_prev: X, K.learning_phase(): 0})
    print("out = " + str(out[0][1][1][0]))

输出：

out = [0.19716819 0.         1.3561226  2.1713073  0.         1.3324987 ]

2.2 卷积模块

该模块可以适用于：输入输出维度不匹配的情况

其 跳跃连接上有一个 CONV2D 卷积层，它没有使用非线性激活函数，作用是改变输入的维度，使后面的加法维度匹配

# GRADED FUNCTION: convolutional_block

def convolutional_block(X, f, filters, stage, block, s = 2):
    """ Implementation of the convolutional block as defined in Figure 4 Arguments: X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev) f -- integer, specifying the shape of the middle CONV's window for the main path filters -- python list of integers, defining the number of filters in the CONV layers of the main path stage -- integer, used to name the layers, depending on their position in the network block -- string/character, used to name the layers, depending on their position in the network s -- Integer, specifying the stride to be used Returns: X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C) """
    
    # defining name basis
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'
    
    # Retrieve Filters
    F1, F2, F3 = filters
    
    # Save the input value
    X_shortcut = X


    ##### MAIN PATH #####
    # First component of main path 
    X = Conv2D(F1, (1, 1), strides = (s,s), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)
    X = Activation('relu')(X)
    
    ### START CODE HERE ###

    # Second component of main path (≈3 lines)
    X = Conv2D(F2, (f,f), strides=(1,1),padding='same',name=conv_name_base+'2b',kernel_initializer=glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis=3, name=bn_name_base+'2b')(X)
    X = Activation('relu')(X)

    # Third component of main path (≈2 lines)
    X = Conv2D(F3,(1,1), strides=(1,1),padding='valid',name=conv_name_base+'2c',kernel_initializer=glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis=3, name=bn_name_base+'2c')(X)

    ##### SHORTCUT PATH #### (≈2 lines)
    X_shortcut = Conv2D(F3, (1,1), strides=(s,s),padding='valid',name=conv_name_base+'1',kernel_initializer=glorot_uniform(seed=0))(X_shortcut)
    X_shortcut = BatchNormalization(axis=3, name=bn_name_base+'1')(X_shortcut)

    # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)
    X = Add()([X, X_shortcut])
    X = Activation('relu')(X)
    
    ### END CODE HERE ###
    
    return X

测试：

tf.reset_default_graph()

with tf.Session() as test:
    np.random.seed(1)
    A_prev = tf.placeholder("float", [3, 4, 4, 6])
    X = np.random.randn(3, 4, 4, 6)
    A = convolutional_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')
    test.run(tf.global_variables_initializer())
    out = test.run([A], feed_dict={ A_prev: X, K.learning_phase(): 0})
    print("out = " + str(out[0][1][1][0]))

输出：

out = [0.09018463 1.2348979  0.46822023 0.03671762 0.         0.65516603]

3. 建立你的第一个残差网络（50层）

ID（Identity）恒等模块，ID BLOCK x3 表示恒等模块3次

pooling 参考 https://keras.io/zh/layers/pooling/

# GRADED FUNCTION: ResNet50

def ResNet50(input_shape = (64, 64, 3), classes = 6):
    """ Implementation of the popular ResNet50 the following architecture: CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3 -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER Arguments: input_shape -- shape of the images of the dataset classes -- integer, number of classes Returns: model -- a Model() instance in Keras """
    
    # Define the input as a tensor with shape input_shape
    X_input = Input(input_shape)

    
    # Zero-Padding
    X = ZeroPadding2D((3, 3))(X_input)
    
    # Stage 1
    X = Conv2D(64, (7, 7), strides = (2, 2), name = 'conv1', kernel_initializer = glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis = 3, name = 'bn_conv1')(X)
    X = Activation('relu')(X)
    X = MaxPooling2D((3, 3), strides=(2, 2))(X)

    # Stage 2
    X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 2, block='a', s = 1)
    X = identity_block(X, 3, [64, 64, 256], stage=2, block='b')
    X = identity_block(X, 3, [64, 64, 256], stage=2, block='c')

    ### START CODE HERE ###

    # Stage 3 (≈4 lines)
    X = convolutional_block(X, f=3, filters=[128,128,512], stage=3, block='a', s=2)
    X = identity_block(X, 3, [128,128,512],stage=3, block='b')
    X = identity_block(X, 3, [128,128,512],stage=3, block='c')
    X = identity_block(X, 3, [128,128,512],stage=3, block='d')

    # Stage 4 (≈6 lines)
    X = convolutional_block(X, f=3, filters=[256,256,1024], stage=4, block='a', s=2)
    X = identity_block(X, 3, [256,256,1024],stage=4, block='b')
    X = identity_block(X, 3, [256,256,1024],stage=4, block='c')
    X = identity_block(X, 3, [256,256,1024],stage=4, block='d')
    X = identity_block(X, 3, [256,256,1024],stage=4, block='e')
    X = identity_block(X, 3, [256,256,1024],stage=4, block='f')

    # Stage 5 (≈3 lines)
    X = convolutional_block(X, f=3, filters=[512,512,2048], stage=5, block='a', s=2)
    X = identity_block(X, 3, [512,512,2048], stage=5, block='b')
    X = identity_block(X, 3, [512,512,2048], stage=5, block='c')

    # AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)"
    X = AveragePooling2D(pool_size=(2,2))(X)
    
    ### END CODE HERE ###

    # output layer
    X = Flatten()(X)
    X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X)
    
    
    # Create model
    model = Model(inputs = X_input, outputs = X, name='ResNet50')

    return model

• 建立模型

model = ResNet50(input_shape = (64, 64, 3), classes = 6)

• 配置模型

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

• 数据导入 + one_hot 编码
  

X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()

# Normalize image vectors
X_train = X_train_orig/255.
X_test = X_test_orig/255.

# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6).T
Y_test = convert_to_one_hot(Y_test_orig, 6).T

print ("number of training examples = " + str(X_train.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(Y_train.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(Y_test.shape))

输出：

number of training examples = 1080
number of test examples = 120
X_train shape: (1080, 64, 64, 3)
Y_train shape: (1080, 6)
X_test shape: (120, 64, 64, 3)
Y_test shape: (120, 6)

• 训练（迭代两次测试下）

model.fit(X_train, Y_train, epochs = 2, batch_size = 32)

输出：（损失在下降，准确率在上升）

Epoch 1/2
1080/1080 [==============================] 
- 208s 192ms/step - loss: 2.6086 - acc: 0.3037
Epoch 2/2
1080/1080 [==============================] 
- 193s 178ms/step - loss: 2.2677 - acc: 0.3972

• 测试

preds = model.evaluate(X_test, Y_test)
print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1]))

输出：（准确率 19%）

120/120 [==============================] - 5s 38ms/step
Loss = 12.753657023111979
Test Accuracy = 0.19166666467984517

该模型训练2次效果很差，训练更多次效果才会好（时间比较久）

老师直接给出了训练好的模型

model = load_model('ResNet50.h5') 

preds = model.evaluate(X_test, Y_test)
print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1]))

Loss = 0.5301782568295796
Test Accuracy = 0.8666667

老师给的 ResNets 残差网络 预测准确率为 86.7%
前次作业中 TF 3层网络模型的预测准确率为 72.5%

4. 用自己的照片测试

import imageio

img_path = 'images/my_image.jpg'
img = image.load_img(img_path, target_size=(64, 64))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
print('Input image shape:', x.shape)
my_image = imageio.imread(img_path)
imshow(my_image)
print("class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = ")
print(model.predict(x))

输出：

Input image shape: (1, 64, 64, 3)
class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = 
[[1. 0. 0. 0. 0. 0.]]

• 模型结构总结

model.summary()

Model: "ResNet50"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param # Connected to 
==================================================================================================
input_1 (InputLayer)            [(None, 64, 64, 3)]  0                                            
__________________________________________________________________________________________________
zero_padding2d_1 (ZeroPadding2D (None, 70, 70, 3)    0           input_1[0][0]                    
__________________________________________________________________________________________________
conv1 (Conv2D)                  (None, 32, 32, 64)   9472        zero_padding2d_1[0][0]           
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization)   (None, 32, 32, 64)   256         conv1[0][0]                      
__________________________________________________________________________________________________
activation_4 (Activation)       (None, 32, 32, 64)   0           bn_conv1[0][0]                   
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)  (None, 15, 15, 64)   0           activation_4[0][0]               
__________________________________________________________________________________________________
res2a_branch2a (Conv2D)         (None, 15, 15, 64)   4160        max_pooling2d_1[0][0]            
________________________________________________________________________

省略省略省略省略
省略省略省略省略

add_16 (Add)                    (None, 2, 2, 2048)   0           bn5b_branch2c[0][0]              
                                                                 activation_46[0][0]              
__________________________________________________________________________________________________
activation_49 (Activation)      (None, 2, 2, 2048)   0           add_16[0][0]                     
__________________________________________________________________________________________________
res5c_branch2a (Conv2D)         (None, 2, 2, 512)    1049088     activation_49[0][0]              
__________________________________________________________________________________________________
bn5c_branch2a (BatchNormalizati (None, 2, 2, 512)    2048        res5c_branch2a[0][0]             
__________________________________________________________________________________________________
activation_50 (Activation)      (None, 2, 2, 512)    0           bn5c_branch2a[0][0]              
__________________________________________________________________________________________________
res5c_branch2b (Conv2D)         (None, 2, 2, 512)    2359808     activation_50[0][0]              
__________________________________________________________________________________________________
bn5c_branch2b (BatchNormalizati (None, 2, 2, 512)    2048        res5c_branch2b[0][0]             
__________________________________________________________________________________________________
activation_51 (Activation)      (None, 2, 2, 512)    0           bn5c_branch2b[0][0]              
__________________________________________________________________________________________________
res5c_branch2c (Conv2D)         (None, 2, 2, 2048)   1050624     activation_51[0][0]              
__________________________________________________________________________________________________
bn5c_branch2c (BatchNormalizati (None, 2, 2, 2048)   8192        res5c_branch2c[0][0]             
__________________________________________________________________________________________________
add_17 (Add)                    (None, 2, 2, 2048)   0           bn5c_branch2c[0][0]              
                                                                 activation_49[0][0]              
__________________________________________________________________________________________________
activation_52 (Activation)      (None, 2, 2, 2048)   0           add_17[0][0]                     
__________________________________________________________________________________________________
avg_pool (AveragePooling2D)     (None, 1, 1, 2048)   0           activation_52[0][0]              
__________________________________________________________________________________________________
flatten_1 (Flatten)             (None, 2048)         0           avg_pool[0][0]                   
__________________________________________________________________________________________________
fc6 (Dense)                     (None, 6)            12294       flatten_1[0][0]                  
==================================================================================================
Total params: 23,600,006
Trainable params: 23,546,886
Non-trainable params: 53,120

• 绘制模型结构图

plot_model(model, to_file='model.png')
SVG(model_to_dot(model).create(prog='dot', format='svg'))

图片很长，只截取部分

参考论文

• Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun - Deep Residual Learning for Image Recognition (2015)
• Francois Chollet’s github repository: https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py

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