如果你在使用的是TensorFlow 1.x版本,那么你可能需要安装并单独使用Keras库,并使用from keras.utils import to_categorical这样的语句来导入to_categorical函数。但在TensorFlow 2.x中,推荐直接从tensorflow.keras中导入。 确保你的TensorFlow库已经正确安装,并且版本是2.x。你可以通过运行pip show tensorflow来检查TensorFl...
from keras.utils import to_categorical 从TensorFlow 2.x开始，Keras被直接集成在TensorFlow中作为高级API...
y_train = np_utils.to_categorical(y_train, 10) y_test = np_utils.to_categorical(y_test, 10) 构建模型 使用Keras构建模型非常简单,我们将使用一个简单的卷积神经网络(Convolutional Neural Network, CNN)结构。 from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D, Flatten...
from keras.layers import Dense from keras.utils import to_categorical from keras.layers.convolutional import Conv2D # to add convolutional layers from keras.layers.convolutional import MaxPooling2D # to add pooling layers from keras.layers import Flatten # to flatten data for fully connected layers ...
from keras.utils import to_categorical Y_train = to_categorical(Y_train) 计算准确性: correct_prediction = tf.equal(tf.argmax(Z3,axis=1), tf.argmax(Y,1) ) # tf.argmax找出每一列最大值的索引 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) # tf.cast转化数据类型 ...
from keras.utils import to_categorical Y_train = to_categorical(Y_train) 计算准确性: correct_prediction = tf.equal(tf.argmax(Z3,axis=1), tf.argmax(Y,1) ) # tf.argmax找出每一列最大值的索引 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) # tf.cast转化数据类型 ...
大家好，我是默语，擅长全栈开发、运维和人工智能技术。在本篇博文中，我们将深入探讨一个常见的Python...
import numpy as np # import data from keras.datasets import mnist import tensorflow as tf # load data (X_train, y_train), (X_test, y_test) = mnist.load_data() np.random.seed(0) train_indices = np.random.choice(60000, 50000, replace=False) ...
from tensorflow.keras.datasets import cifar10 from tensorflow.keras.utils import to_categorical # ...
#标签向量化,使用one-hot编码#这次使用keras自带的方法,也可以用上一篇文章的函数构造 from keras.utils.np_utils import to_categorical one_hot_train_label = to_categorical(train_labels) one_hot_test_label = to_categorical(test_labels) #训练模型 ...