Python Deep Learning 18-DeepTraum vom generativen Deep Learning

# ---------------
from tensorflow import keras
import matplotlib.pyplot as plt
%matplotlib inline

base_image_path = keras.utils.get_file(
"coast.jpg", 
origin="https://img-datasets.s3.amazonaws.com/coast.jpg")

plt.axis("off")
plt.imshow(keras.utils.load_img(base_image_path))
plt.show()

# 使用Inception V3实现
from keras.applications import inception_v3

# 使用预训练的ImageNet权重来加载模型
model = inception_v3.InceptionV3(weights="imagenet", # 构建不包含全连接层的Inceptino 
 include_top=False)
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
87916544/87910968 [==============================] - 74s 1us/step
87924736/87910968 [==============================] - 74s 1us/step

model.summary()

# 层的名称 + 系数：该层对需要最大化的损失的贡献大小

layer_settings = {"mixed4":1.0, 
"mixed5":1.5,
"mixed6":2.0,
"mixed7":2.5}

outputs_dict = dict(
[
(layer.name, layer.output) # 层的名字 + 该层的输出
for layer in [model.get_layer(name) for name in layer_settings.keys()]
]
)

outputs_dict

{'mixed4': <KerasTensor: shape=(None, None, None, 768) dtype=float32 (created by layer 'mixed4')>,
 'mixed5': <KerasTensor: shape=(None, None, None, 768) dtype=float32 (created by layer 'mixed5')>,
 'mixed6': <KerasTensor: shape=(None, None, None, 768) dtype=float32 (created by layer 'mixed6')>,
 'mixed7': <KerasTensor: shape=(None, None, None, 768) dtype=float32 (created by layer 'mixed7')>}

# 特征提取

feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict)
feature_extractor

<keras.engine.functional.Functional at 0x15b5ff0d0>

def compute_loss(image):
features = feature_extractor(image)# 特征提取
loss = tf.zeros(shape=())# 损失初始化

for name in features.keys():# 遍历层
coeff = layer_settings[name] # 某个层的系数
activation = features[name]# 某个层的激活函数
#为了避免出现边界伪影，损失中仅包含非边界的像素
loss += coeff * tf.reduce_mean(tf.square(activation[:, 2:-2, 2:-2, :])) # 将该层的L2范数添加到loss中；
return loss

import tensorflow as tf

@tf.function
def gradient_ascent_step(image, lr): # lr--->learning_rate学习率
with tf.GradientTape() as tape:
tape.watch(image)
loss = compute_loss(image)# 调用计算损失方法
grads = tape.gradient(loss, image)# 梯度更新
grads = tf.math.l2_normalize(grads)
image += lr * grads
return loss, image

def gradient_ascent_loop(image, iterations, lr, max_loss=None):
for i in range(iterations):
loss, image = gradient_ascent_step(image, lr)
if max_loss is not None and loss > max_loss:
break
print(f"第{i}步的损失值是{loss:.2f}")

return image

import numpy as np

array = np.array([[1,2,3],
[4,5,6]]
)
array

array.shape

array1 = np.expand_dims(array,axis=0)
array1

(1, 2, 3)

array2 = np.expand_dims(array,axis=1)
array2

array([[[1, 2, 3]],

 [[4, 5, 6]]])

array2.shape

(2, 1, 3)

array3 = np.expand_dims(array,axis=-1)
array3

array([[[1],
[2],
[3]],

 [[4],
[5],
[6]]])

array3.shape

(2, 3, 1)

np.clip(
array, 
min(array), 
max(array), 
out=None):

array = np.array([1,2,3,4,5,6])

np.clip(array, 2, 5)# 输出长度和原数组相同

array([2, 2, 3, 4, 5, 5])

array = np.arange(18).reshape((6,3))
array

array([[ 0,1,2],
 [ 3,4,5],
 [ 6,7,8],
 [ 9, 10, 11],
 [12, 13, 14],
 [15, 16, 17]])

array([[ 5,5,5],
 [ 5,5,5],
 [ 6,7,8],
 [ 9, 10, 11],
 [12, 13, 14],
 [15, 15, 15]])

step = 20.#梯度上升的步长
num_octave = 3# 运行梯度上升的尺度个数
octave_scale = 1.4# 两个尺度间的比例大小
iterations = 30# 在每个尺度上运行梯度上升的步数
max_loss = 15.# 损失值若大于15，则中断梯度上升过程

import numpy as np

def preprocess_image(image_path):# 预处理
img = keras.utils.load_img(image_path)# 导入图片
img = keras.utils.img_to_array(img)# 转成数组
img = np.expand_dims(img, axis=0)# 增加数组维度；见上面解释(x,y) ---->(1,x,y)
img = keras.applications.inception_v3.preprocess_input(img) 
return img


def deprocess_image(img):# 图片压缩处理
img = img.reshape((img.shape[1], img.shape[2], 3))
img /= 2.0
img += 0.5
img *= 255.
# np.clip：截断功能，保证数组中的取值在0-255之间
img = np.clip(img, 0, 255).astype("uint8")
return img