(train_images, train_labels), (test_images, test_labels) = keras.datasets.cifar100.load_data(label_mode='fine') # 定义分类字典 class_dict = { 0: [0, 53, 57, 83], 1: [1, 3, 4, 6, 7, 14, 15, 18, 19, 21, 24, 26, 27, 29, 30, 31, 32, 34, 36, 38, 42, 43, 44, 45, 50, 55, 63, 64, 65, 66, 67, 72, 73, 74, 75, 77, 78, 79, 80, 88, 91, 93, 95, 97, 99], 2: [2, 11, 35, 46, 98], 3: [5, 9, 10, 16, 20, 22, 25, 28, 39, 40, 41, 61, 84, 86, 87, 94], 4: [12, 17, 37, 68, 76], 5: [23, 33, 49, 60, 71], 6: [47, 51, 52, 54, 56, 59, 62, 70, 82, 92, 96], 7: [8, 13, 48, 58, 69, 81, 85, 89, 90] } train_labels = np.zeros((train_labels.shape[0], 8)) test_labels = np.zeros((test_labels.shape[0], 8)) for i in range(8): train_labels[:, i] = np.any([train_labels[:, class_dict[i][j]] == 1 for j in range(len(class_dict[i]))], axis=0) test_labels[:, i] = np.any([test_labels[:, class_dict[i][j]] == 1 for j in range(len(class_dict[i]))], axis=0)

(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()修改该代码更换为从我的路径中读取，地址为E:\date\mnist.npz

history = model.fit(train_images, train_labels, epochs=5, validation_data=(test_images, test_labels)) # 保存模型 model.save(os.path.join(output_path, 'model.h5')) # 输出最终识别率 test_loss, test_...

注释每一行代码import tensorflow as tf mnist = tf.keras.datasets.mnist #加载mnist数据集 (train_images,train_labels),(test_images,test_labels) = mnist.load_data() train_images, test_images = train_images/255.0, test_images/255.0 #归一化到0-1 print("Train image shape:",train_images.shape,"Train label shape:",train_labels.shape) #打印图像大小和标签数 print("Test image shape:",test_images.shape,"Test label shape:",test_labels.shape) print("image data:",train_images[1]) #具体看一幅image的数据 print("label data:",train_labels[1]) import matplotlib.pyplot as plt def plot_image(image): plt.imshow(image.reshape(28,28),cmap='binary') plt.show() plot_image(train_images[1]) #搭建模型 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=[28, 28]), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax') ]) #编译 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) #训练模型 model.fit(train_images,train_labels, epochs=5, batch_size=64) model.evaluate(test_images,test_labels)

(train_images, train_labels), (test_images, test_labels) = mnist.load_data() train_images, test_images = train_images/255.0, test_images/255.0 # 对图像数据进行归一化处理 print("Train image shape:", ...

import tensorflow as tf mnist = tf.keras.datasets.mnist #加载mnist数据集 (train_images,train_labels),(test_images,test_labels) = mnist.load_data() train_images, test_images = train_images/255.0, test_images/255.0 #归一化到0-1 print("Train image shape:",train_images.shape,"Train label shape:",train_labels.shape) #打印图像大小和标签数 print("Test image shape:",test_images.shape,"Test label shape:",test_labels.shape) print("image data:",train_images[1]) #具体看一幅image的数据 print("label data:",train_labels[1]) import matplotlib.pyplot as plt def plot_image(image): plt.imshow(image.reshape(28,28),cmap='binary') plt.show() plot_image(train_images[1]) #搭建模型 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=[28, 28]), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax') ]) #编译 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) #训练模型 model.fit(train_images,train_labels, epochs=5, batch_size=64) model.evaluate(test_images,test_labels)注释代码，并说明实现的功能，生成实验总结

(train_images,train_labels),(test_images,test_labels) = mnist.load_data() # 将图像数据归一化到 0-1 train_images, test_images = train_images/255.0, test_images/255.0 # 打印图像大小和标签数 print(...

import tensorflow as tf from tensorflow import keras import numpy as np # 加载 IMDB 数据集 imdb = keras.datasets.imdb (train_data, train_labels), (test, test_labels) = imdb.load_data(num_words=00) # 将整数序列填充为相同的长度 train_data = keras.preprocessing.sequence.pad_sequences(train_data, value=0, padding='post', maxlen=256) test_data = keras.preprocessing.sequence.pad_sequences(test_data, value=0, padding='post', maxlen=256) # 构建模型 model = keras.Sequential() model.add(keras.layers.Embed(10000, 16)) model.add(keras.layers.GlobalAveragePooling1D()) model.add(keras.layers(16, activation=tf.nn.relu)) model.add(keras.layers.Dense(1, activation=tf.sigmoid)) # 编译模型 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # 训练模型 history = model.fit(train_data, train_labels, epochs=40, batch_size=512, validation_data=(test_data, test_labels), verbose=1) # 在测试数据上评估模型 results = model.evaluate(test_data, test_labels) print('Test loss:', results[0]) print('Test accuracy:', results[1])test_data = keras.preprocessing.sequence.pad_sequences(test_data, value=0, padding='post', maxlen=256) NameError: name 'test_data' is not defined

(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000) # 将整数序列填充为相同的长度 maxlen = 256 train_data = keras.preprocessing.sequence.pad_sequences(train_data, ...

import numpy as np import tensorflow as tf from tensorflow import keras import matplotlib.pyplot as plt ## Let us define a plt function for simplicity def plt_loss(x,training_metric,testing_metric,ax,colors = ['b']): ax.plot(x,training_metric,'b',label = 'Train') ax.plot(x,testing_metric,'k',label = 'Test') ax.set_xlabel('Epochs') ax.set_ylabel('Accuarcy')# ax.set_ylabel('Categorical Crossentropy Loss') plt.legend() plt.grid() plt.show() tf.keras.utils.set_random_seed(1) ## We import the Minist Dataset using Keras.datasets (train_data, train_labels), (test_data, test_labels) = keras.datasets.mnist.load_data() ## We first vectorize the image (2828) into a vector （784） train_data = train_data.reshape(train_data.shape[0],train_data.shape[1]train_data.shape[2]) # 60000784 test_data = test_data.reshape(test_data.shape[0],test_data.shape[1]test_data.shape[2]) # 10000*784 ## We next change label number to a 10 dimensional vector, e.g., 1->[0,1,0,0,0,0,0,0,0,0] train_labels = keras.utils.to_categorical(train_labels,10) test_labels = keras.utils.to_categorical(test_labels,10) ## start to build a MLP model N_batch_size = 5000 N_epochs = 100 lr = 0.01 # ## we build a three layer model, 784 -> 64 -> 10 MLP_3 = keras.models.Sequential([ keras.layers.Dense(64, input_shape=(784,),activation='relu'), keras.layers.Dense(10,activation='softmax') ]) MLP_3.compile( optimizer=keras.optimizers.Adam(lr), loss= 'categorical_crossentropy', metrics = ['accuracy'] ) History = MLP_3.fit(train_data,train_labels, batch_size = N_batch_size, epochs = N_epochs,validation_data=(test_data,test_labels), shuffle=False) train_acc = History.history['accuracy'] test_acc = History.history['val_accuracy']模仿此段代码，写一个双隐层感知器（输入层784，第一隐层128，第二隐层64，输出层10）

(train_data, train_labels), (test_data, test_labels) = keras.datasets.mnist.load_data() ## We first vectorize the image (28*28) into a vector （784） train_data = train_data.reshape(train_data.shape...

import numpy as np import tensorflow as tf from tensorflow import keras import matplotlib.pyplot as plt Let us define a plt function for simplicity def plt_loss(x,training_metric,testing_metric,ax,colors = ['b']): ax.plot(x,training_metric,'b',label = 'Train') ax.plot(x,testing_metric,'k',label = 'Test') ax.set_xlabel('Epochs') ax.set_ylabel('Accuracy') plt.legend() plt.grid() plt.show() tf.keras.utils.set_random_seed(1) We import the Minist Dataset using Keras.datasets (train_data, train_labels), (test_data, test_labels) = keras.datasets.mnist.load_data() We first vectorize the image (28*28) into a vector （784） train_data = train_data.reshape(train_data.shape[0],train_data.shape[1]train_data.shape[2]) # 60000784 test_data = test_data.reshape(test_data.shape[0],test_data.shape[1]test_data.shape[2]) # 10000784 We next change label number to a 10 dimensional vector, e.g., 1-> train_labels = keras.utils.to_categorical(train_labels,10) test_labels = keras.utils.to_categorical(test_labels,10) start to build a MLP model N_batch_size = 5000 N_epochs = 100 lr = 0.01 we build a three layer model, 784 -> 64 -> 10 MLP_3 = keras.models.Sequential([ keras.layers.Dense(128, input_shape=(784,),activation='relu'), keras.layers.Dense(64, activation='relu'), keras.layers.Dense(10,activation='softmax') ]) MLP_3.compile( optimizer=keras.optimizers.Adam(lr), loss= 'categorical_crossentropy', metrics = ['accuracy'] ) History = MLP_3.fit(train_data,train_labels, batch_size = N_batch_size, epochs = N_epochs,validation_data=(test_data,test_labels), shuffle=False) train_acc = History.history['accuracy'] test_acc = History.history对于该模型，使用不同数量的训练数据（5000，10000，15000，…，60000,公差=5000的等差数列），绘制训练集和测试集准确率（纵轴）关于训练数据大小(横轴)的曲线

(train_data, train_labels), (test_data, test_labels) = keras.datasets.mnist.load_data() # Vectorize the image (28*28) into a vector （784） train_data = train_data.reshape(train_data.shape[0],train_...

import tensorflow as tf from tensorflow import keras import numpy as np # 加载 IMDB 数据集 imdb = keras.datasets.imdb (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000) # 将整数序列填充为相同的长度 maxlen = 256 train_data = keras.preprocessing.sequence.pad_sequences(train_data, value=0, padding='post', maxlen=maxlen) test_data = keras.preprocessing.sequence.pad_sequences(test_data, value=0, padding='post', maxlen=maxlen) # 构建模型 model = keras.Sequential() model.add(keras.layers.Embedding(10000, 16)) model.add(keras.layers.GlobalAveragePooling1D()) model.add(keras.layers.Dense(16, activation=tf.nn.relu)) model.add(keras.layers.Dense(1, activation=tf.sigmoid)) # 编译模型 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # 训练模型 history = model.fit(train_data, train_labels, epochs=40, batch_size=512, validation_data=(test_data, test_labels), verbose=1) # 在测试数据上评估模型 results = model.evaluate(test_data, test_labels) print('Test loss:', results[0]) print('Test accuracy:', results[1])优化代码

(train_data, train_labels), (test_data, test_labels) = keras.datasets.imdb.load_data(num_words=10000) # 将整数序列填充为相同的长度 maxlen = 256 train_data = keras.preprocessing.sequence.pad_sequences...

AttributeError: module 'keras.api._v1.keras.datasets.mnist' has no attribute 'train'

(train_images, train_labels), (test_images, test_labels) = mnist.load_data() 请确保你的 Keras 版本与示例代码兼容，并检查你的导入语句是否正确。如果问题仍然存在，请提供更多的代码和错误信息，以便我...

对这个MNIST数据集的深度神经网络进行调整和优化，给出代码# 提取特征和标签 train_features, train_labels = train_data.iloc[:, 1:], train_data.iloc[:, 0] test_features, test_labels = test_data.iloc[:, 1:], test_data.iloc[:, 0] # 对标签进行二进制编码 lb = LabelBinarizer() train_labels = lb.fit_transform(train_labels) test_labels = lb.fit_transform(test_labels) # 将特征值缩放到0-1之间并且reshape为(28,28,1) train_features = train_features.values.reshape(-1, 28, 28, 1) / 255. test_features = test_features.values.reshape(-1, 28, 28, 1) / 255. #定义模型：深度神经网络 DNN = tf.keras.models.Sequential([ tf.keras.layers.Dense(512, input_shape=(784,), activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(512, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax') ])

(train_features, train_labels), (test_features, test_labels) = mnist.load_data() # 对数据进行预处理 lb = LabelBinarizer() train_labels = lb.fit_transform(train_labels) test_labels = lb.fit_transform...

mnist.load_data()

(train_images, train_labels), (test_images, test_labels) = mnist.load_data() 调用 mnist.load_data() 将会返回两个元组，第一个元组包含了训练图像和对应的标签，第二个元组包含了测试图像和对应的标签。...

import keras from keras.datasets import cifar10 from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D from pathlib import Path import tensorflow as tf # Load data set (x_train, y_train), (x_test, y_test) = cifar10.load_data() # Normalize data set to 0-to-1 range x_train = x_train.astype("float32") x_test = x_test.astype("float32") x_train = x_train / 255 x_test = x_test / 255 # Convert class vectors to binary class matrices # Our labels are single values from 0 to 9 # Instead, we want each label to be an array with on element set to 1 y_train = tf.keras.utils.to_categorical(y_train, 10) y_test = tf.keras.utils.to_categorical(y_test, 10)代码解释

然后，通过调用cifar10.load_data()函数加载CIFAR10数据集，并将训练集和测试集分别赋值给(x_train, y_train)和(x_test, y_test)。接下来，对数据集进行规范化处理，将像素值从0到255的范围转换为0到1之间的...

mnist.load_data()使用方法

(train_images, train_labels), (test_images, test_labels) = mnist.load_data() 3. 查看数据集信息 print('训练集图像数量：', len(train_images)) print('测试集图像数量：', len(test_images)) print...

mnist = tf.keras.datasets.mnist.怎么改成中文手写数据集

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() 如果您想使用中文手写数据集，需要先准备好一个中文手写数据集，并将其转换成适当的格式。一种常见的格式是将图像数据保存在一个...

import tensorflow as tf from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropoutfrom tensorflow.keras import Model# 在GPU上运算时，因为cuDNN库本身也有自己的随机数生成器，所以即使tf设置了seed，也不会每次得到相同的结果tf.random.set_seed(100)mnist = tf.keras.datasets.mnist(X_train, y_train), (X_test, y_test) = mnist.load_data()X_train, X_test = X_train/255.0, X_test/255.0# 将特征数据集从(N,32,32)转变成(N,32,32,1),因为Conv2D需要(NHWC)四阶张量结构X_train = X_train[..., tf.newaxis] X_test = X_test[..., tf.newaxis]batch_size = 64# 手动生成mini_batch数据集train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train)).shuffle(10000).batch(batch_size)test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test)).batch(batch_size)class Deep_CNN_Model(Model): def init(self): super(Deep_CNN_Model, self).init() self.conv1 = Conv2D(32, 5, activation='relu') self.pool1 = MaxPool2D() self.conv2 = Conv2D(64, 5, activation='relu') self.pool2 = MaxPool2D() self.flatten = Flatten() self.d1 = Dense(128, activation='relu') self.dropout = Dropout(0.2) self.d2 = Dense(10, activation='softmax') def call(self, X): # 无需在此处增加training参数状态。只需要在调用Model.call时，传递training参数即可 X = self.conv1(X) X = self.pool1(X) X = self.conv2(X) X = self.pool2(X) X = self.flatten(X) X = self.d1(X) X = self.dropout(X) # 无需在此处设置training状态。只需要在调用Model.call时，传递training参数即可 return self.d2(X)model = Deep_CNN_Model()loss_object = tf.keras.losses.SparseCategoricalCrossentropy()optimizer = tf.keras.optimizers.Adam()train_loss = tf.keras.metrics.Mean(name='train_loss')train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')test_loss = tf.keras.metrics.Mean(name='test_loss')test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')# TODO：定义单批次的训练和预测操作@tf.functiondef train_step(images, labels): ...... @tf.functiondef test_step(images, labels): ...... # TODO：执行完整的训练过程EPOCHS = 10for epoch in range(EPOCHS)补全代码

(X_train, y_train), (X_test, y_test) = mnist.load_data() X_train, X_test = X_train/255.0, X_test/255.0 X_train = X_train[..., tf.newaxis] X_test = X_test[..., tf.newaxis] # 定义batch_size并手动生成...

import matplotlib.pyplot as plt import tensorflow as tf from tensorflow import keras import numpy as np #加载IMDB数据 imdb = keras.datasets.imdb (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=100) print("训练记录数量：{}，标签数量：{}".format(len(train_data),len(train_labels))) print(train_data[0]) #数据标准化 train_data = keras.preprocessing.sequence.pad_sequences(train_data,value=0,padding='post',maxlen=256) text_data = keras.preprocessing.sequence.pad_sequences(train_data,value=0,padding='post',maxlen=256) print(train_data[0]) #构建模型 vocab_size = 10000 model = tf.keras.Sequential([tf.keras.layers.Embedding(vocab_size, 64), tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)), tf.keras.layers.Dense(64,activation='relu'), tf.keras.layers.Dense(1) ]) model.summary() #配置并训练模型 model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy']) x_val = train_data[:10000] partial_x_train = train_data[10000:] y_val = train_labels[:10000] partial_y_train = train_labels[10000:] history = model.fit(partial_x_train,partial_y_train,epochs=1,batch_size=512,validation_data=(x_val,y_val),verbose=1) #测试性能 results = model.evaluate(test_data, test_labels, verbose=2) print(results) #训练过程可视化 history_dict = history.history print(history_dict.keys()) def plot_graphs(history, string): plt.plot(history.history[string]) plt.plot(history.history['val_'+string]) plt.xlabel("Epochs") plt.ylabel(string) plt.legend([string,'val_'+string]) plt.show() plot_graphs(history,"accuracy") plot_graphs(history,"loss")

需要注意的是，这段代码中 text_data 的赋值是错误的，应该是 text_data = keras.preprocessing.sequence.pad_sequences(test_data,value=0,padding='post',maxlen=256)。同时，代码中只训练了一次，因为 ...

(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()

(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()中的(train_images, train_labels)具体指的是什么？

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(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()中的(train_images, train_labels)具体指的是什么？

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