#encoding=utf8 def relu(x): ''' input:x(ndarray)输入数据 ''' #********* Begin *********# #********* End *********#
时间: 2024-06-05 22:04:44 浏览: 8
这是一个实现ReLU(Rectified Linear Unit)激活函数的Python函数,它的输入是一个NumPy数组x,返回值也是一个NumPy数组,其中进行了ReLU运算。ReLU函数可以通过以下公式表示:
f(x) = max(0, x)
即当输入x大于0时,输出f(x)等于x;当输入x小于等于0时,输出f(x)等于0。
下面是该函数的实现方式:
#encoding=utf8
import numpy as np
def relu(x):
'''
input:
x(ndarray): 输入数据
output:
ret(ndarray): ReLU输出结果
'''
# 将x中小于等于0的元素赋值为0
ret = np.maximum(x, 0)
return ret
相关问题
构建一个神经网络 输入6*4矩阵,输出3*8矩阵
可以使用下面这段 Python 代码来构建这样的神经网络:
```
import tensorflow as tf
# 定义输入和输出的维度
input_dim = 6 * 4
output_dim = 3 * 8
# 定义神经网络模型
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(input_dim,)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dense(output_dim)
])
# 编译模型
model.compile(loss='mse', optimizer='adam')
# 输出模型概要信息
model.summary()
```
这个神经网络模型有4个全连接层,每个层都使用 ReLU 激活函数。最后一个全连接层的输出维度是 3x8,所以这个模型可以满足你的输入输出需求。
resnet 用keras实现 输入50*50
以下是使用Keras实现ResNet50网络并输入大小为50x50的示例代码:
```python
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Activation, MaxPooling2D, Add, GlobalAveragePooling2D, Dense
from tensorflow.keras.models import Model
def resnet_block(inputs, filters, strides=1):
x = Conv2D(filters, kernel_size=3, strides=strides, padding='same')(inputs)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(filters, kernel_size=3, padding='same')(x)
x = BatchNormalization()(x)
if strides != 1 or inputs.shape[-1] != filters:
shortcut = Conv2D(filters, kernel_size=1, strides=strides, padding='same')(inputs)
shortcut = BatchNormalization()(shortcut)
else:
shortcut = inputs
x = Add()([x, shortcut])
x = Activation('relu')(x)
return x
def ResNet50(input_shape=(50,50, 3), num_classes=1000):
inputs = Input(shape=input_shape)
x = Conv2D(64, kernel_size=7, strides=2, padding='same')(inputs)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D(pool_size=3, strides=2, padding='same')(x)
x = resnet_block(x, filters=64)
x = resnet_block(x, filters=64)
x = resnet_block(x, filters=64)
x = resnet_block(x, filters=128, strides=2)
x = resnet_block(x, filters=128)
x = resnet_block(x, filters=128)
x = resnet_block(x, filters=128)
x = resnet_block(x, filters=256, strides=2)
x = resnet_block(x, filters=256)
x = resnet_block(x, filters=256)
x = resnet_block(x, filters=256)
x = resnet_block(x, filters=256)
x = resnet_block(x, filters=256)
x = resnet_block(x, filters=512, strides=2)
x = resnet_block(x, filters=512)
x = resnet_block(x, filters=512)
x = GlobalAveragePooling2D()(x)
x = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=inputs, outputs=x)
return model
model = ResNet50(input_shape=(50, 50, 3), num_classes=1000)
model.summary()
```
这段代码定义了一个ResNet50网络模型,输入大小为50x50的彩色图像。模型的结构包括卷积层、批归一化层、激活函数层、池化层、残差块、全局平均池化层和全连接层。最后使用`model.summary()`打印出模型的结构信息。
相关推荐
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)