Python-JupyterGPU机器学习代码

��       使用 Jupyter Notebook 进行 GPU 加速的机器学习代码开发，通常涉及到利用 GPU 运行深度学习模型，特别是基于 TensorFlow 或 PyTorch 这样的深度学习框架。GPU 的并行计算能力可以显著加快模型训练的速度，尤其对于大规模数据集和复杂模型来说效果更为明显。

根据需要训练的数据进行机器学习建模

import time start = time.time()import numpy as np # linear algebraimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)from keras.models import Sequentialfrom keras.layers import Conv2D, MaxPooling2D, Activation, Dropout, Flatten, Densefrom keras.preprocessing.image import ImageDataGenerator, img_to_array, load_imgimport matplotlib.pyplot as pltfrom PIL import Image from glob import globimport osimport os print(os.system('ls /dataset/stress_test_data_2'))train_path = '/dataset/stress_test_data_2/Training/'test_path = '/dataset/stress_test_data_2/Test/'img = load_img(train_path + "Apple Braeburn/0_100.jpg", target_size=(100,100))plt.imshow(img)plt.axis("off")plt.show()images = ['Orange', 'Banana', 'Cauliflower', 'Cactus fruit', 'Eggplant', 'Avocado', 'Blueberry','Lemon', 'Kiwi']import matplotlib.pyplot as pltimport numpy as npfig = plt.figure(figsize =(15,5))for i in range(9):    ax = fig.add_subplot(3,3,i+1,xticks=[],yticks=[])    #fig.patch.set_facecolor('#E53090')    #Above code adds a background color for subplots you can change the hex color code as you wish    plt.title(images[i])    plt.axis("off")    ax.imshow(load_img(train_path + images[i] +"/0_100.jpg", target_size=(100,100)))x = img_to_array(img)print(x.shape)className = glob(train_path + '/*')number_of_class = len(className)print(number_of_class)model = Sequential()model.add(Conv2D(32, (3,3), input_shape= x.shape))model.add(Activation("relu"))model.add(MaxPooling2D())model.add(Conv2D(32, (3,3),))model.add(Activation("relu"))model.add(MaxPooling2D())model.add(Conv2D(64, (3,3),))model.add(Activation("relu"))model.add(MaxPooling2D())model.add(Flatten())model.add(Dense(1024))model.add(Activation("relu"))model.add(Dropout(0.5))model.add(Dense(number_of_class))#outputmodel.add(Activation("softmax"))model.compile(loss = "categorical_crossentropy",             optimizer = "rmsprop",             metrics = ["accuracy"])model.summary()batch_size = 32train_datagen = ImageDataGenerator(rescale = 1./255,                  shear_range = 0.3,                  horizontal_flip=True,                  vertical_flip=False,                  zoom_range = 0.3                  )test_datagen  = ImageDataGenerator(rescale = 1./255)train_generator = train_datagen.flow_from_directory(train_path,     target_size=x.shape[:2],     batch_size = batch_size,     color_mode= "rgb",     class_mode = "categorical")test_generator = test_datagen.flow_from_directory(test_path,     target_size=x.shape[:2],     batch_size = batch_size,     color_mode= "rgb",     class_mode = "categorical")hist = model.fit_generator(generator = train_generator,                    steps_per_epoch = 1600 // batch_size,                   epochs = 50,                   validation_data = test_generator,                   validation_steps = 800 // batch_size)print(hist.history.keys())plt.plot(hist.history["loss"], label = "Train Loss")plt.plot(hist.history["val_loss"], label = "Validation Loss")plt.legend()plt.show()plt.plot(hist.history["accuracy"], label = "Train Accuracy")plt.plot(hist.history["val_accuracy"], label = "Validation Accuracy")plt.legend()plt.show()end = time.time() print("time cost",end - start)

        在 Jupyter Notebook 中进行 GPU 加速的机器学习代码开发，可以带来训练速度的显著提升，尤其适用于大规模数据和复杂模型的场景。同时，结合 Jupyter Notebook 的交互式编程和展示优势，可以更方便地进行实验、调试和结果展示。如果你需要进一步的帮助或有其他问题，请随时告诉我。