学习于：简单粗暴 TensorFlow 2

1. tfhub

网址：
https://hub.tensorflow.google.cn/
https://tfhub.dev/

• 可以搜索，下载模型
  

• 安装包 pip install tensorflow-hub

import tensorflow_hub as hubhub_url = 'https://hub.tensorflow.google.cn/google/magenta/arbitrary-image-stylization-v1-256/2'
hub_model = hub.load(hub_url) # 加载模型
outputs = hub_model(inputs) # 调用模型

2. 例子：神经风格转换

Ng 课也讲过这个例子

import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf# 归一化，resize
def load_image_local(img_path, img_size=(256, 256)):# png 4 通道转 jpg 3通道if 'png' in img_path:img = Image.open(img_path)img = img.convert('RGB')img.save("temp.jpg")img = plt.imread("temp.jpg").astype(np.float32)[np.newaxis, :, :, :]else:# 添加一个 batch_size 轴img = plt.imread(img_path).astype(np.float32)[np.newaxis, :, :, :]if img.max() > 1.0:img = img / 255.img = tf.image.resize(img, img_size, preserve_aspect_ratio=True)return img# 绘制图片
def show_image(img, title, save=False, fig_dpi=300):plt.imshow(img, aspect='equal')plt.axis('off')plt.show()if save:plt.imsave(title + '.jpg', img.numpy())# 图片路径
content_image_path = "pic1.jpg"
style_image_path = "pic2.jpg"# 处理图片
content_image = load_image_local(content_image_path)
style_image = load_image_local(style_image_path)# 展示图片
show_image(content_image[0], "Content Image")
show_image(style_image[0], "Style Image")# 加载模型
hub_url = 'https://hub.tensorflow.google.cn/google/magenta/arbitrary-image-stylization-v1-256/2'
hub_model = hub.load(hub_url)# 调用模型
outputs = hub_model(tf.constant(content_image), tf.constant(style_image))
stylized_image = outputs[0]  # 取出第一个样本预测值 [ :, :, 3]# 展示预测图片
show_image(stylized_image[0], "Stylized Image", True)

内容图片：

风格图片：

转换后的图片：

3. retrain 例子

https://hub.tensorflow.google.cn/google/imagenet/inception_v3/feature_vector/4

• hub.KerasLayer(url) 封装一个layer到模型当中，可以设置是否 finetune

num_classes = 10
model = tf.keras.Sequential([hub.KerasLayer("https://hub.tensorflow.google.cn/google/imagenet/inception_v3/feature_vector/4",trainable=False),  # 可以设为True，微调tf.keras.layers.Dense(num_classes, activation='softmax')
])
model.build([None, 299, 299, 3])  # Batch input shape
model.summary()

模型结构

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
keras_layer (KerasLayer)     (None, 2048)              21802784  
_________________________________________________________________
dense (Dense)                (None, 10)                20490     
=================================================================
Total params: 21,823,274
Trainable params: 20,490
Non-trainable params: 21,802,784
_________________________________________________________________