Data_Extractor数据恢复软件教程

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"Data_Extractor中文图文教程2011.pdf是关于PC3000原配的数据恢复工具使用手册,由北京军达成科技有限公司提供。该教程详细介绍了如何使用Data_Extractor进行数据恢复,包括软件安装、任务管理、工作模式以及数据恢复策略等内容。" 在数据恢复领域,Data_Extractor是一款重要的工具,它与PC3000配合使用,旨在帮助专业人士解决硬盘数据丢失的问题。本教程首先阐述了Data_Extractor的基本用途,即用于数据提取和恢复,同时列出了硬件和软件的最低运行要求。接着,教程详细介绍了如何开始使用这个工具,包括软件的安装过程,安装光盘中的内容,以及首次启动软件时的界面和操作。 教程的主体部分围绕DataExtractorPCI程序的工作描述展开,讲解了数据管理和任务管理的一般准则。用户可以通过快捷按钮图标进行操作,了解基本概念和定义。在创建数据恢复任务时,用户需要选择保存任务数据的文件夹、指定工作设备,并进行任务初始化。教程还详细说明了附加功能、选择目标设备、对任务进行注释等步骤。 此外,教程涵盖了打开先前创建的任务,展示了数据恢复任务的主窗口,包括主菜单、快捷工具条、信息面板和Log面板。教程详细列出了各种工作模式,如建立数据区域拷贝、使用“资源管理器”模式、创建虚拟译码表、数据拷贝输出等。还介绍了辅助模式,如使用对象示意图、表面检查、GREP搜索、偏移量搜索、原始恢复、搜索丢失的文件和目录、MFT扫描、逻辑扫描、拷贝扇区以及查看和编辑扇区等功能。 在数据恢复章节,教程分析了导致数据破坏的各种原因,包括物理故障和逻辑结构破坏,并提供了初步诊断硬盘问题的方法,如电路板故障、主轴电机故障、磁头装置问题以及系统信息丢失的判断和处理。 Data_Extractor中文图文教程2011.pdf是一个全面的指南,涵盖了数据恢复过程中可能遇到的各种情况和应对策略,对于数据恢复专业人员来说是一份非常实用的参考资料。通过学习此教程,用户可以熟练掌握Data_Extractor的使用,提升数据恢复的效率和成功率。

逐行详细解释以下代码并加注释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_normalize(grads) image += learning_rate * grads return loss, image def gradient_ascent_loop(image, iterations, learning_rate, max_loss=None): for i in range(iterations): loss, image = gradient_ascent_step(image, learning_rate) if max_loss is not None and loss > max_loss: break print(f"... Loss value at step {i}: {loss:.2f}") return image #hyperparameters step = 20. num_octave = 3 octave_scale = 1.4 iterations = 30 max_loss = 15. #图像处理方面 import numpy as np def preprocess_image(image_path): img = keras.utils.load_img(image_path) img = keras.utils.img_to_array(img) img = np.expand_dims(img, axis=0) img = keras.applications.inception_v3.preprocess_input(img) return img def deprocess_image(img): img = img.reshape((img.shape[1], img.shape[2], 3)) img /= 2.0 img += 0.5 img *= 255. img = np.clip(img, 0, 255).astype("uint8") return img #在多个连续 上运行梯度上升 original_img = preprocess_image(base_image_path) original_shape = original_img.shape[1:3] 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] 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}") img = tf.image.resize(img, shape) img = gradient_ascent_loop( img, iterations=iterations, learning_rate=step, max_loss=max_loss ) 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("DeepDream.png", deprocess_image(img.numpy()))

2023-06-07 上传

import torchimport torch.nn as nnimport torch.optim as optimimport numpy as np# 定义视频特征提取模型class VideoFeatureExtractor(nn.Module): def __init__(self): super(VideoFeatureExtractor, self).__init__() self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1) self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1) self.pool = nn.MaxPool2d(kernel_size=2, stride=2) def forward(self, x): x = self.pool(torch.relu(self.conv1(x))) x = self.pool(torch.relu(self.conv2(x))) x = x.view(-1, 32 * 8 * 8) return x# 定义推荐模型class VideoRecommendationModel(nn.Module): def __init__(self, num_videos, embedding_dim): super(VideoRecommendationModel, self).__init__() self.video_embedding = nn.Embedding(num_videos, embedding_dim) self.user_embedding = nn.Embedding(num_users, embedding_dim) self.fc1 = nn.Linear(2 * embedding_dim, 64) self.fc2 = nn.Linear(64, 1) def forward(self, user_ids, video_ids): user_embed = self.user_embedding(user_ids) video_embed = self.video_embedding(video_ids) x = torch.cat([user_embed, video_embed], dim=1) x = torch.relu(self.fc1(x)) x = self.fc2(x) return torch.sigmoid(x)# 加载数据data = np.load('video_data.npy')num_users, num_videos, embedding_dim = data.shapetrain_data = torch.tensor(data[:int(0.8 * num_users)])test_data = torch.tensor(data[int(0.8 * num_users):])# 定义模型和优化器feature_extractor = VideoFeatureExtractor()recommendation_model = VideoRecommendationModel(num_videos, embedding_dim)optimizer = optim.Adam(recommendation_model.parameters())# 训练模型for epoch in range(10): for user_ids, video_ids, ratings in train_data: optimizer.zero_grad() video_features = feature_extractor(video_ids) ratings_pred = recommendation_model(user_ids, video_ids) loss = nn.BCELoss()(ratings_pred, ratings) loss.backward() optimizer.step() # 计算测试集准确率 test_ratings_pred = recommendation_model(test_data[:, 0], test_data[:, 1]) test_loss = nn.BCELoss()(test_ratings_pred, test_data[:, 2]) test_accuracy = ((test_ratings_pred > 0.5).float() == test_data[:, 2]).float().mean() print('Epoch %d: Test Loss %.4f, Test Accuracy %.4f' % (epoch, test_loss.item(), test_accuracy.item()))解释每一行代码

2023-05-22 上传