实现ResNet50-马育民老师

# 官方源码网址
[点击](https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnet_common.py "点击")

# 分析代码
参见
https://www.malaoshi.top/show_1EF5LiLIgrnW.html

# 代码

### conv1
```
import tensorflow as tf
from tensorflow.keras import layers,models

def conv1(input_shape):
    img_input = layers.Input(shape=input_shape)
    
    x = layers.ZeroPadding2D(padding=(3, 3))(img_input)
    x = layers.Conv2D(64, 7, strides=2)(x)
    x = layers.BatchNormalization( epsilon=1.001e-5)(x)
    x = layers.Activation('relu')(x)
    x = layers.ZeroPadding2D(padding=(1, 1))(x)
    
    return img_input,x
```

### conv2_x到conv5_x的第一个block

**注意：** 做加法时，顺序不能调到，否则加载keras ResNet50 预训练模型时会报错，错误如下：
```
 ValueError: Shapes (1, 1, 128, 512) and (512, 256, 1, 1) are incompatible
 ```

```
def block1(x,filters, stride=1):
    shortcut=tf.keras.layers.Conv2D(4*filters,1,strides=stride)(x)
    shortcut = layers.BatchNormalization( epsilon=1.001e-5,)(shortcut)
    
    x = layers.Conv2D(filters, 1, strides=stride)(x)
    x = layers.BatchNormalization( epsilon=1.001e-5)(x)
    x = layers.Activation('relu')(x)
    x = layers.Conv2D(filters, 3, padding='SAME')(x)
    x = layers.BatchNormalization( epsilon=1.001e-5)(x)
    x = layers.Activation('relu')(x)
    x = layers.Conv2D(4 * filters, 1)(x)
    x = layers.BatchNormalization( epsilon=1.001e-5)(x)

# 不可调换顺序，否则不能导入tf权重
    x = layers.Add()([shortcut,x])
    # 使用下面add()也可以
    # x=tf.add(shortcut,x)
    x = layers.Activation('relu')(x)
    
    return x
```

### conv2_x到conv5_x的其他block
```
def block2(x,filters):
    shortcut=x

x = layers.Conv2D(filters, 1)(x)
    x = layers.BatchNormalization( epsilon=1.001e-5)(x)
    x = layers.Activation('relu')(x)
    x = layers.Conv2D(filters, 3, padding='SAME')(x)
    x = layers.BatchNormalization( epsilon=1.001e-5)(x)
    x = layers.Activation('relu')(x)
    x = layers.Conv2D(4 * filters, 1)(x)
    x = layers.BatchNormalization( epsilon=1.001e-5,)(x)
    
    x = layers.Add()([shortcut, x])
    # 使用下面add()也可以
    # x=tf.add(shortcut,x)
    x = layers.Activation('relu')(x)

return x

```

### ResNet50
```
def resnet50(input_shape,weights_path):
    img_input,x=conv1(input_shape=input_shape)

x=tf.keras.layers.MaxPool2D((3,3),2)(x)

x=block1(x,64,1)
    x=block2(x,64)
    x=block2(x,64)

x=block1(x,128,2)
    x=block2(x,128)
    x=block2(x,128)
    x=block2(x,128)

x=block1(x,256,2)
    x=block2(x,256)
    x=block2(x,256)
    x=block2(x,256)
    x=block2(x,256)
    x=block2(x,256)

x=block1(x,512,2)
    x=block2(x,512)
    x=block2(x,512)

model=tf.keras.Model(img_input,x)
    if weights_path:
        model.load_weights(weights_path)
        print("加载权重成功！")
    return model
```

### 测试
```
model=resnet50(input_shape=(224,224,3),weights_path="/Users/mym/.keras/models/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5")
```

原文出处：/show_1EF5VeQUYY0Y.html