void shenboAI::Predict(tensorflow::Tensor &image_t, tensorflow::Tensor &image_c, std::vector<float>& results) { LOG(INFO) << "Enter shenboAI::PredictFp"; string input_layer = "input_5"; string input_layer2 = "cinput_6"; string output_layer = "dense_9/Softmax"; std::vector<Tensor> outputs; Status run_status = session_classification->Run({{input_layer, image_t},{input_layer2, image_c}}, {output_layer}, {}, &outputs);

def predict(self, board, color, last_move): if sum(sum(board)) == 0 and color == WHITE: print('error: network.predict') if sum(sum(board)) == 1 and color == BLACK: print('error: network.predict') tensor = board2tensor(board, color, last_move) policy, value_tensor = self._model.predict_on_batch(tensor) value = value_tensor[0][0] return policy, value

接下来，函数通过调用另一个名为board2tensor的函数将棋盘状态转化为神经网络模型可以接受的张量格式，再通过调用predict_on_batch方法对张量进行预测，得到表示下一步落子概率的向量和当前局面的胜率估计值。...

修改以下代码使其能够输出模型预测结果： def open_image(self): file_dialog = QFileDialog() file_paths, _ = file_dialog.getOpenFileNames(self, "选择图片", "", "Image Files (.png .jpg .jpeg)") if file_paths: self.display_images(file_paths) def preprocess_images(self, image_paths): data_transform = transforms.Compose([ transforms.CenterCrop(150), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) self.current_image_paths = [] images = [] for image_path in image_paths: image = Image.open(image_path) image = data_transform(image) image = torch.unsqueeze(image, dim=0) images.append(image) self.current_image_paths.append(image_path) return images def predict_images(self): if not self.current_image_paths: return for i, image_path in enumerate(self.current_image_paths): image = self.preprocess_image(image_path) output = self.model(image) predicted_class = self.class_dict[output.argmax().item()] self.result_labels[i].setText(f"Predicted Class: {predicted_class}") self.progress_bar.setValue((i+1)20) def display_images(self, image_paths): for i, image_path in enumerate(image_paths): image = QImage(image_path) image = image.scaled(300, 300, Qt.KeepAspectRatio) if i == 0: self.image_label_1.setPixmap(QPixmap.fromImage(image)) elif i == 1: self.image_label_2.setPixmap(QPixmap.fromImage(image)) elif i == 2: self.image_label_3.setPixmap(QPixmap.fromImage(image)) elif i == 3: self.image_label_4.setPixmap(QPixmap.fromImage(image)) elif i == 4: self.image_label_5.setPixmap(QPixmap.fromImage(image))

请注意，代码中的 preprocess_images 函数和 predict_images 函数中的 self.preprocess_image(image_path) 需要修改为 self.preprocess_images([image_path])。修改后的代码如下： def open_image(self): file...

#include "prepare_ogm.hpp" namespace senior { namespace guardian { namespace prepare { std::string PrepareOgm::Name() { return "Prepare Ogm Element"; } void PrepareOgm::Initiate() {} void PrepareOgm::Process(data::DataFrame& his, data::DataFrame& cur) { if (cur.source_ogm_points_.is_invalid()) return; if (cur.source_visual_ogm_points_.is_valid()) { cur.source_ogm_points_.insert(cur.source_ogm_points_.end(), cur.source_visual_ogm_points_.begin(), cur.source_visual_ogm_points_.end()); } if (cur.source_higher_ogm_points_.is_valid()) { cur.source_ogm_points_.insert(cur.source_ogm_points_.end(), cur.source_higher_ogm_points_.begin(), cur.source_higher_ogm_points_.end()); } auto& predict_path = cur.monitor_data_.mutable_predict_path(); predict_path.GenerateBoundary(cur); cur.AABox2d_ = predict_path.vehicle_AABox2d_; // if (!his.monitor_data_.is_need_to_take_over()) { // LOG(INFO)<<"1"; cur.AABox2d_.SetWidth(cur.AABox2d_.width() + 1.0); cur.AABox2d_.SetLength(cur.AABox2d_.length() + 1.0); // } std::vector<math::Vec2d> corner_points_; cur.AABox2d_.GetAllCorners(&corner_points_); auto& polygon2d = predict_path.tractor_polygon2d_; math::Vec2d temp; VoxelGrid filter_; common::Time now = common::Time::Now(); for (auto& point : cur.source_ogm_points_) { temp.set_x(point.x()); temp.set_y(-point.y()); if (cur.AABox2d_.IsPointIn(temp)) { cur.AABB_ogm_points_.emplace_back(point); } } cur.guardian_diagnose_["Prepare_PrepareOgm_AABox_filter"] = std::to_string((common::Time::Now() - now).ToSecond() * 1000); now = common::Time::Now(); filter_.VoxelGrid_ApplyFilter( cur.AABB_ogm_points_, cur.ogm_points_, corner_points_, 0.1, 0.1, 0); cur.guardian_diagnose_["Prepare_PrepareOgm_VoxelGrid_ApplyFilter"] = std::to_string((common::Time::Now() - now).ToSecond() * 1000); cur.ogm_points_.set_stamp(cur.source_ogm_points_.stamp()); cur.ogm_points_.set_time(cur.source_ogm_points_.time()); cur.ogm_points_.set_delay_time(cur.source_ogm_points_.delay_time()); cur.ogm_points_.set_valid(); } } // namespace prepare } // namespace guardian } // namespace senior 改变为C语言程序

例如命名空间(namespace)、类(class)、std::string、std::vector、auto关键字、模板等等，这些在C语言中都没有对应的语法和库。如果需要将这段代码改写成C语言程序，需要根据实际需求重新设计程序结构和数据结构，...

D:\python\python3.9.5\pythonProject\venv\Scripts\python.exe C:/Users/马斌/Desktop/model/model/sudu.py 2023-05-25 00:57:58.533488: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. Traceback (most recent call last): File "C:\Users\马斌\Desktop\model\model\sudu.py", line 7, in <module> predictions = model.predict(input_data) NameError: name 'input_data' is not defined Process finished with exit code 1

这个错误提示是因为在代码中没有定义 input_data...另外，你的 TensorFlow 版本似乎已经使用了 oneDNN 库进行性能优化，如果你在其他操作中也想使用 AVX 和 AVX2 指令，需要使用相应的编译器标志重新构建 TensorFlow。

import cv2 import numpy as np import torch as torch from torchvision.models import densenet121 # Load the DenseNet model model = densenet121(pretrained=True) # Read the image image = cv2.imread('C:/Users/23594/Desktop/888.jpg') # Convert the image to grayscale grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Resize the image to the size of the model input resized_image = cv2.resize(grayscale_image, (224, 224)) # Normalize the image normalized_image = resized_image / 255.0 # Convert the image to a tensor image_tensor = torch.from_numpy(normalized_image).float() # Predict the key points of the person predictions = model(image_tensor) # Convert the predictions to a list of points points = [] for i in range(len(predictions[0])): points.append((predictions[0][i][0], predictions[0][i][1])) # Draw the key points on the image cv2.drawKeypoints(image, points, np.array([]), (0, 255, 0), flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) # Display the image cv2.imshow('Image', image) cv2.waitKey(0)import cv2 import numpy as np import torch as torch from torchvision.models import densenet121 # Load the DenseNet model model = densenet121(pretrained=True) # Read the image image = cv2.imread('C:/Users/23594/Desktop/888.jpg') # Convert the image to grayscale grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Resize the image to the size of the model input resized_image = cv2.resize(grayscale_image, (224, 224)) # Normalize the image normalized_image = resized_image / 255.0 # Convert the image to a tensor image_tensor = torch.from_numpy(normalized_image).float() # Predict the key points of the person predictions = model(image_tensor) # Convert the predictions to a list of points points = [] for i in range(len(predictions[0])): points.append((predictions[0][i][0], predictions[0][i][1])) # Draw the key points on the image cv2.drawKeypoints(image, points, np.array([]), (0, 255, 0), flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) # Display the image cv2.imshow('Image', image) cv2.waitKey(0)

它使用了OpenCV和PyTorch库。具体流程包括： 1. 导入必要的库，包括OpenCV、NumPy和PyTorch等。 ... 3. 读取待检测的人物图片，并将其转为灰度图像。 4. 调整图片大小为与模型输入大小相同。...5. 将像素值归一化到0到1...

tfAlexNet-master.zip_AlexNet网络_alexnet_alexnet tensorflow_tensor

5. **评估与预测**: 使用model.evaluate()评估模型在验证集上的性能，model.predict()进行图像分类。在tfAlexNet-master文件中，可能包含了AlexNet模型的实现代码、训练脚本、配置文件以及可能的数据集...

tianchi O2O: xgb_predict_file-数据集

标题 "tianchi O2O: xgb_predict_file-数据集" 暗示这是一个与天池（Tianchi）在线到线下（O2O）业务相关的数据分析项目，其中使用了XGBoost（极端梯度提升）算法进行预测。XGBoost是一种优化的分布式梯度提升库，...

matlab代码梯度下降法-tf-decompose:在TensorFlow中实现的Tensor分解

在TensorFlow中实现的CP和Tucker张量分解。用法 import numpy as np import tensorflow as tf from scipy . io . matlab import loadmat from ktensor import KruskalTensor # Load sensory bread data ...

# 生成一个训练batch def get_next_batch(batch_size=128): obj = gen_id_card() batch_x = np.zeros([batch_size, IMAGE_HEIGHTIMAGE_WIDTH]) batch_y = np.zeros([batch_size, MAX_CAPTCHACHAR_SET_LEN]) for i in range(batch_size): image, text, vec = obj.gen_image() batch_x[i,:] = image.reshape((IMAGE_HEIGHT*IMAGE_WIDTH)) batch_y[i,:] = vec return batch_x, batch_y 怎么调用模型

在使用 TensorFlow 框架时，可以使用 tf.keras.models.load_model() 函数加载模型，然后使用 model.predict() 方法进行预测。在使用 PyTorch 框架时，可以使用 torch.load() 函数加载模型，然后使用 model....

TypeError: DNNNet.predict() missing 1 required positional argument: 'input_data'

这个错误提示表明在Python的深度学习库（如TensorFlow、PyTorch等）中，你在调用DNNNet.predict()函数时缺少了一个必需的位置参数input_data。predict方法通常需要输入数据作为参数，以便模型能够基于这些数据...

用TensorFlow2.0将波士顿房价数据集分为训练集，验证集和测试集（6:2:2），进行Tensorflow2.0的数据加载、线性回归模型的搭建、线性回归模型的交叉验证、模型保持和新数据预测

使用TensorFlow2.0 将波士顿房价数据集分为训练集，验证集和测试集（6:2:2），进行Tensorflow2.0的数据加载、线性回归模型的搭建、线性回归模型的交叉验证、模型保持和新数据预测。

好的，首先我们需要加载波士顿房价数据集。可以使用Scikit-learn库中的load_boston函数来加载数据。接下来，我们将数据集分为训练...prediction = model.predict(new_data) print("预测结果：", prediction[0][0])

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