conda terminal using the below command: spyder To ensure everything was installed correctly, import all the modules, it will add everything and if anything went wrong, you will get module not found Now save your file, restart your terminal and start keras, your backend will be changed. >>> import keras as k using theano backend. Keras 11 Deep learning is an evolving subfield neural networks. A simple sequential model is as follows: from keras.models import Sequential from keras.layers import Dense, Activation model = Sequential() model.add(Dense(512, activation='relu'

它允许构建任意的神经网络图。 Sequential 顺序模型如下所示： from keras.models import Sequential model = Sequential() 可以简单地使用 .add() 来堆叠模型： KERAS: 基于 PYTHON 的深度学习库 2 from keras.layers import Dense model.add(Dense(units=64, activation='relu' 顺序模型是多个网络层的线性堆叠。 你可以通过将层的列表传递给 Sequential 的构造函数，来创建一个 Sequential 模型： from keras.models import Sequential from keras.layers import Dense, Activation model = Sequential([ Dense(32, input_shape=(784,)), Activation('relu') metrics=['accuracy']) # 均方误差回归问题 model.compile(optimizer='rmsprop', loss='mse') # 自定义评估标准函数 import keras.backend as K def mean_pred(y_true, y_pred): return K.mean(y_pred) model.compile(optimizer='rmsprop'

transformers>=4. 37.0 版本。以下是一个非常简单的代码片段示例，展示如何运行 Qwen1.5-Chat 模型，其中包含 Qwen1. 5-7B-Chat 的实例： from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto # Now you modelscope import AutoModelForCausalLM, AutoTokenizer 借助 TextStreamer ，chat 的流式模式变得非常简单。下面我们将展示一个如何使用它的示例： ... # Reuse the code before `model.generate()` in the last code snippet from transformers import TextStreamer about large language models."} ], }' 或者您可以按照下面所示的方式，使用 openai Python 包中的 Python 客户端： from openai import OpenAI # Set OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base

meaning that you can simple add your metrics to the Decanter Prometheus servlet. You just have to import io.prometheus* packages and simple use the regular Prometheus code: Apache Karaf Decanter 2.x - Your code here. inprogressRequests.dec(); } } Don’t forget to import io.prometheus* packages in your bundle MANIFEST.MF: Import-Package: io.prometheus.client;version="[0.8,1)" That’s the only thing karaf.decanter.sample.collector; import org.osgi.framework.SynchronousBundleListener; import org.osgi.service.event.EventAdmin; import org.osgi.service.event.Event; import java.util.HashMap; public class

karaf.decanter.sample.collector; import org.osgi.framework.SynchronousBundleListener; import org.osgi.service.event.EventAdmin; import org.osgi.service.event.Event; import java.util.HashMap; public class collector; import org.osgi.framework.BundleActivator; import org.osgi.framework.BundleContext; import org.osgi.framework.ServiceReference; import org.osgi.framework.ServiceRegistration; import org.osgi.service service.event.EventAdmin; import org.osgi.util.tracker.ServiceTracker; public class Activator implements BundleActivator { private BundleCollector collector; public void start(final BundleContext bundleContext)

#@save import collections import hashlib import math import os import random import re import shutil import sys import tarfile import time import zipfile from collections import defaultdict defaultdict import pandas as pd import requests from IPython import display from matplotlib import pyplot as plt from matplotlib_inline import backend_inline d2l = sys.modules[__name__] 本书中的大部分代码都是基于PyT #@save import numpy as np import torch (continues on next page) 目录 5 (continued from previous page) import torchvision from PIL import Image from torch import nn from torch.nn import functional

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.3 Import the example configuration (optional) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 13 How to import third-party libraries for Python runtime 91 13.1 Import requests third-party libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 13.2 Import Pytorch third-party . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 14 How to import third-party libraries for Node runtime 99 14.1 Import Lodash third-party libraries . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.3 Import the example configuration (optional) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 How to import third-party libraries for Python runtime 101 14.1 Import requests third-party libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 14.2 Import Pytorch third-party . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 15 How to import third-party libraries for Node runtime 109 15.1 Import Lodash third-party libraries . . . . . . . . . . . . . . . . . . . .

displaying the results. import numpy as np import cv2 from matplotlib import pyplot as plt from keras.preprocessing.image import ImageDataGenerator from urllib.request import urlopen IMG_SIZE = 224 function takes the name of the dataset and loads the training and the validation splits as follows. import tensorflow_datasets as tfds def make_dataset(name): loadfn = lambda x: tfds.load(name, split=x) resize them to 264x264 size. This is a required step because our model expects fixed-sized images. import tensorflow as tf # Target image size IMG_SIZE = 264 def dsitem_to_tuple(item): return (item['image']

to see if there are any problems: # Get the installation directory of PyFlink python3 -c "import pyflink;import os;print(os.path.dirname(os.path.abspath(pyflink.__ ˓→file__)))" # It will output a path messages in the log file as following: # Get the installation directory of PyFlink python3 -c "import pyflink;import os;print(os.path.dirname(os.path.abspath(pyflink.__ ˓→file__)))" (continues on next page) latest version Create a TableEnvironment [1]: # Create a batch TableEnvironment from pyflink.table import EnvironmentSettings, TableEnvironment env_settings = EnvironmentSettings.in_batch_mode() table_env