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：
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_dictOut[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 imageIn [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 imgIn [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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