Python深度学习18-生成式深度学习之DeepDream

DeepDream是一种艺术性的图像修改技术，主要是基于训练好的卷积神经网络CNN进行图片的生成。

在生成图片时，神经网络是冻结的，也就是网络的权重不再更新，只需要更新输入的图片。常用的预训练卷积网络包括Google的Inception、VGG网络和ResNet网络等。

DeePDream的基本步骤：

• 获取输入图片
• 将图片输入网络，得到所希望可视化的神经元的输出值
• 计算神经元输出值对图片各像素的梯度
• 使用梯度下降不断更新图片

重复第2、3、4步，直到满足所设定的条件

下面是使用Keras实现DeepDream的大致过程：

In [1]:

# ---------------
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()

上面是Keras自带的一张海岸线的图片。下面就是对这张图进行变化。

In [2]:

# 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

In [3]:

model.summary()

In [4]:

# 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

Out[4]:

{'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')>}

In [5]:

# 特征提取

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

Out[5]:

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

In [6]:

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

In [7]:

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

In [8]:

import numpy as np

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

Out[8]:

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

In [9]:

array.shape

Out[9]:

(2, 3)

In [10]:

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

Out[10]:

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

In [11]:

array1.shape

Out[11]:

(1, 2, 3)

In [12]:

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

Out[12]:

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

 [[4, 5, 6]]])

In [13]:

array2.shape

Out[13]:

(2, 1, 3)

In [14]:

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

Out[14]:

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

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

In [15]:

array3.shape

Out[15]:

(2, 3, 1)

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

In [16]:

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

np.clip(array, 2, 5) # 输出长度和原数组相同

Out[16]:

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

In [17]:

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

Out[17]:

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

In [18]:

np.clip(array, 5, 15)

Out[18]:

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

In [19]:

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

In [20]:

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

In [21]:

# 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 = 15.0 # 损失值若大于15，则中断梯度上升过程

original_img = preprocess_image(base_image_path) # 预处理函数
original_shape = original_img.shape[1:3]

print(original_img.shape) # 四维图像
print(original_shape) # 第2和3维度的值
(1, 900, 1200, 3)
(900, 1200)

In [22]:

successive_shapes = [original_shape]

for i in range(1, num_octave):
 shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape])
 successive_shapes.append(shape)
successive_shapes = successive_shapes[::-1] # 翻转

shrunk_original_img = tf.image.resize(original_img, successive_shapes[0])

img = tf.identity(original_img)
for i, shape in enumerate(successive_shapes):
 print(f"Processing octave {i} with shape {shape}")
 # resize
 img = tf.image.resize(img, shape)
 img = gradient_ascent_loop( # 梯度上升函数调用
 img, 
 iteratinotallow=iterations, 
 lr=step, 
 max_loss=max_loss
 )
 # resize
 upscaled_shrunk_original_img = tf.image.resize(shrunk_original_img, shape)
 same_size_original = tf.image.resize(original_img, shape)
 
 lost_detail = same_size_original - upscaled_shrunk_original_img
 img += lost_detail
 shrunk_original_img = tf.image.resize(original_img, shape)

keras.utils.save_img("dream.png", deprocess_image(img.numpy()))

结果为：

Processing octave 0 with shape (459, 612)
第0步的损失值是0.80
第1步的损失值是1.07
第2步的损失值是1.44
第3步的损失值是1.82
......
第26步的损失值是11.44
第27步的损失值是11.72
第28步的损失值是12.03
第29步的损失值是12.49

同时在本地生成了新图片，看下效果：

再看一眼原图：相对比之下，新图有点梦幻的味道！

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