用趣味语言介绍一下 山楂
时间: 2023-02-09 16:26:03 浏览: 178
山楂啊,这可是一种萌萌酸酸的水果哦!它长得小巧玲珑,口感酸甜多汁,吃起来像是在和阳光玩耍。别看它的外表平凡,其实它是一位“果中之王”,富含维生素C、钾、铁等营养元素,对健康非常有益。山楂,不仅仅是水果,更是一种生活态度,让我们用酸甜的心情,去面对人生的坎坷与起伏。
相关问题
用python画出yolov7-tiny的结构框图
抱歉,目前并不存在 YOLOv7,可能是您想表达 YOLOv3 或 YOLOv4 的结构框图。以下是使用 Python 画出 YOLOv4-tiny 的结构框图的示例代码:
```python
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Input, LeakyReLU, ZeroPadding2D, UpSampling2D, Concatenate
def darknet_conv(input_tensor, num_filters, kernel_size, strides=1, batch_norm=True):
x = Conv2D(filters=num_filters, kernel_size=kernel_size, strides=strides, padding='same', use_bias=not batch_norm,
kernel_regularizer=tf.keras.regularizers.l2(0.0005))(input_tensor)
if batch_norm:
x = tf.keras.layers.BatchNormalization()(x)
x = LeakyReLU(alpha=0.1)(x)
return x
def darknet_residual(input_tensor, num_filters):
x = darknet_conv(input_tensor, num_filters // 2, kernel_size=1)
x = darknet_conv(x, num_filters, kernel_size=3)
x = tf.keras.layers.Add()([input_tensor, x])
return x
def darknet_block(input_tensor, num_filters, num_blocks):
x = darknet_conv(input_tensor, num_filters, kernel_size=3, strides=2)
for _ in range(num_blocks):
x = darknet_residual(x, num_filters)
return x
def yolo_head(input_tensor, num_filters):
x = darknet_conv(input_tensor, num_filters * 2, kernel_size=3)
x = darknet_conv(x, num_filters, kernel_size=1)
x = darknet_conv(x, num_filters * 2, kernel_size=3)
x = darknet_conv(x, num_filters, kernel_size=1)
x = darknet_conv(x, num_filters * 2, kernel_size=3)
return x
def yolo_v4_tiny(input_shape, num_classes):
input_tensor = Input(shape=input_shape)
x = darknet_conv(input_tensor, 32, kernel_size=3)
x = darknet_block(x, 64, 1)
x = darknet_block(x, 128, 2)
x = darknet_block(x, 256, 4)
x = darknet_block(x, 512, 4)
x = darknet_block(x, 1024, 2)
x = darknet_conv(x, 512, kernel_size=1)
x = darknet_conv(x, 1024, kernel_size=3)
x = darknet_conv(x, 512, kernel_size=1)
x = darknet_conv(x, 1024, kernel_size=3)
x = darknet_conv(x, 512, kernel_size=1)
x1 = yolo_head(x, num_filters=256)
x1 = Conv2D(filters=num_classes * 5, kernel_size=1)(x1)
x2 = darknet_conv(x, 256, kernel_size=1)
x2 = UpSampling2D()(x2)
x2 = Concatenate()([x2, darknet_conv(x, 256, kernel_size=1)])
x2 = darknet_conv(x2, 256, kernel_size=3)
x2 = yolo_head(x2, num_filters=128)
x2 = Conv2D(filters=num_classes * 5, kernel_size=1)(x2)
model = tf.keras.Model(inputs=[input_tensor], outputs=[x1, x2])
return model
model = yolo_v4_tiny(input_shape=(416, 416, 3), num_classes=80)
tf.keras.utils.plot_model(model, to_file='yolov4-tiny.png', show_shapes=True)
```
该代码使用 TensorFlow 2.x 实现了 YOLOv4-tiny 的结构框图的绘制,将会在当前目录下生成一个名为 `yolov4-tiny.png` 的 PNG 图片文件,展示模型的结构框图。
python利用for循环在for i in range打印出苹果、沙果、海棠、野樱莓、枇杷、欧楂、山楂、梨、温柏、蔷薇果
可以使用列表来存储这些水果名称,然后使用 for 循环遍历列表,依次打印出每个水果名称。代码如下:
```python
fruits = ['苹果', '沙果', '海棠', '野樱莓', '枇杷', '欧楂', '山楂', '梨', '温柏', '蔷薇果']
for fruit in fruits:
print(fruit)
```
输出结果为:
```
苹果
沙果
海棠
野樱莓
枇杷
欧楂
山楂
梨
温柏
蔷薇果
```