【深度学习】DeepDream的实现

问题描述：

        用keras实现DeepDream。我们将从一个在 ImageNet 上预训练的卷积神经网络开始。本次选择的模型是Inception模型

实现步骤：

下载数据集和设置DeepDream配置

from tensorflow import keras
import matplotlib.pyplot as plt

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))

#instantiating a model
from tensorflow.keras.applications import inception_v3
model = inception_v3.InceptionV3(weights='imagenet',include_top=False)

#配置各层对DeepDream损失的贡献
layer_settings = {
    "mixed4": 1.0,
    "mixed5": 1.5,
    "mixed6": 2.0,
    "mixed7": 2.5,
}
outputs_dict = dict(
    [
        (layer.name, layer.output)
        for layer in [model.get_layer(name) for name in layer_settings.keys()]
    ]
)
feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict)

定义最大化的损失和梯度上升过程

#定义损失函数
import tensorflow as tf
def compute_loss(input_image):
    features = feature_extractor(input_image)
    loss = tf.zeros(shape=())
    for name in features.keys():
        coeff = layer_settings[name]
        activation = features[name]
        loss += coeff * tf.reduce_mean(tf.square(activation[:, 2:-2, 2:-2, :]))
    return loss
#梯度上升过程
@tf.function
def gradient_ascent_step(image, learning_rate):
    with tf.GradientTape() as tape:
        tape.watch(image)
        loss = compute_loss(image)
    grads = tape.gradient(loss, image)
    grads = tf.math.l2_n