model.fit(x_train, one_hot_labels, epochs=10) ``` 在这个示例中,`x_train` 是输入数据,`one_hot_labels` 是独热编码后的标签数据。通过这样的方式,我们就可以应用独热编码的标签进行模型训练了。 通过以上步骤,我们成功地使用 tf.keras.utils.to_categorical 函数对原始标签数据进行了独热编码,并将其应...
train_labels = tf.keras.utils.to_categorical(train_labels, LABEL_DIMENSIONS) test_labels = tf.keras.utils.to_categorical(test_labels, LABEL_DIMENSIONS) # Cast the labels to floats, needed later train_labels = train_labels.astype(np.float32) test_labels = test_labels.astype(np.float32) 构建...
train_X, train_y= mnist.load_data(r"F:\大学\大三\选修\机器学习\机械学习\实验\实验十四\mnist.npz")[0] train_X= train_X.reshape(-1, 28, 28, 1) train_X= train_X.astype('float32') train_X/= 255train_y= to_categorical(train_y, 10)fromkeras.modelsimportSequentialfromkeras.layers...
例如:与 categorical_crossentropy 一起使用。 例子: a = tf.keras.utils.to_categorical([0, 1, 2, 3], num_classes=4) a = tf.constant(a, shape=[4, 4]) print(a) tf.Tensor( [[1. 0. 0. 0.] [0. 1. 0. 0.] [0. 0. 1. 0.] [0. 0. 0. 1.]], shape=(4, 4), dtyp...
对于二者的区别而言简单地说，k-means 的结果是每个数据点被 assign 到其中某一个 cluster ，而 GMM ...
train_data的shape是[210000,24,24,1]。把这210000张的目标结果先用list保存,如果是二分类,此时list保存的是0,1两种取值。先通过np.array 转换成[210000,1]的形状,然后通过keras.utils.to_categorical转换成onehot编码,并reshape最后得到train_labels的形状是[210000,2]...
all_labels = train_labels + test_labels print ('(2) doc to var...') from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.utils import to_categorical import numpy as np tokenizer = Tokenizer() ...
<!-- li标签定义列表项目 --> 列表标题一
(10, activation='softmax') ]) # 模型编译 model.compile(loss='sparse_categorical_crossentropy', optimizer=SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True), metrics=['acc']) # 模型训练 history = model.fit(training_images, training_labels, batch_size=32, epochs=3) # 保存模型 ...
29# Output layers: separate outputs for the weather and the ground labels 30weather_output = tf.keras.layers.Dense(4, 31activation='softmax', 32name='weather')(fc_2) 33ground_output = tf.keras.layers.Dense(13, 34activation='sigmoid', ...