网站建设常用的英文,电商首页模板网站,关于我们做网站,有了域名 建设自己的网站介绍#xff1a; Keras Tuner是一种用于优化Keras模型超参数的开源Python库。它允许您通过自动化搜索算法来寻找最佳的超参数组合#xff0c;以提高模型的性能。Keras Tuner提供了一系列内置的超参数搜索算法#xff0c;如随机搜索、网格搜索、贝叶斯优化等。它还支持自定义…介绍  Keras Tuner是一种用于优化Keras模型超参数的开源Python库。它允许您通过自动化搜索算法来寻找最佳的超参数组合以提高模型的性能。Keras Tuner提供了一系列内置的超参数搜索算法如随机搜索、网格搜索、贝叶斯优化等。它还支持自定义搜索空间和搜索算法。通过使用Keras Tuner您可以更轻松地优化模型的性能节省调参的时间和精力。 数据  from tensorflow.keras.datasets import fashion_mnist (x_train, y_train), (x_test, y_test) fashion_mnist.load_data() Label Description 0 T-shirt/top 1 Trouser 2 Pullover 3 Dress 4 Coat 5 Sandal 6 Shirt 7 Sneaker 8 Bag 9 Ankle boot import matplotlib.pyplot as plt %matplotlib inlineprint(y_test[0]) plt.imshow(x_test[0], cmapgray) #each having 1 channel (grayscale, it would have been 3 in the case of color, 1 each for Red, Green and Blue) 建模 x_train x_train.reshape(-1, 28, 28, 1) x_test x_test.reshape(-1, 28, 28, 1) from tensorflow import keras from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Activation import os os.environ[KMP_DUPLICATE_LIB_OK]TRUEmodel keras.models.Sequential()model.add(Conv2D(32, (3, 3), input_shapex_train.shape[1:])) model.add(Activation(relu)) model.add(MaxPooling2D(pool_size(2, 2)))model.add(Conv2D(32, (3, 3))) model.add(Activation(relu)) model.add(MaxPooling2D(pool_size(2, 2)))model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectorsmodel.add(Dense(10)) model.add(Activation(softmax))model.compile(optimizeradam,losssparse_categorical_crossentropy,metrics[accuracy])model.fit(x_train, y_train, batch_size64, epochs1, validation_data (x_test, y_test)) Keras Tuner from kerastuner.tuners import RandomSearch from kerastuner.engine.hyperparameters import HyperParametersdef build_model(hp): # random search passes this hyperparameter() object model keras.models.Sequential()model.add(Conv2D(hp.Int(input_units,min_value32,max_value256,step32), (3, 3), input_shapex_train.shape[1:]))model.add(Activation(relu))model.add(MaxPooling2D(pool_size(2, 2)))for i in range(hp.Int(n_layers, 1, 4)): # adding variation of layers.model.add(Conv2D(hp.Int(fconv_{i}_units,min_value32,max_value256,step32), (3, 3)))model.add(Activation(relu))model.add(Flatten()) model.add(Dense(10))model.add(Activation(softmax))model.compile(optimizeradam,losssparse_categorical_crossentropy,metrics[accuracy])return modeltuner RandomSearch(build_model,objectiveval_accuracy,max_trials1, # how many model variations to test?executions_per_trial1, # how many trials per variation? (same model could perform differently)directoryLesson56,project_nameOptimise)tuner.search(xx_train,yy_train,verbose1, # just slapping this here bc jupyter notebook. The console out was getting messy.epochs1,batch_size64,#callbacks[tensorboard], # if you have callbacks like tensorboard, they go here.validation_data(x_test, y_test)) tuner.results_summary()