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...
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...
对于Keras的to_categorical，正确的导入语句是：from keras.utils import to_categorical 从TensorFlow 2.x...
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, Dense, Dropout model = Sequential() # 第一层卷...
from keras.layers.convolutional import Conv2D, MaxPooling2D from keras.utils import to_categorical # import data from keras.datasets import mnist # load data (X_train, y_train), (X_test, y_test) = mnist.load_data() print(X_train.shape, X_test.shape) ...
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 ...
from tensorflow.keras.utils import to_categorical from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Flatten, Dense, Dropout params = { 'dropout': 0.25, 'batch-size': 128, 'epochs': 50, 'layer-1-size': 128, ...
from keras.layers.convolutional import Conv2D, MaxPooling2D from keras.utils import to_categorical # import data from keras.datasets import mnist # load data (X_train, y_train), (X_test, y_test) = mnist.load_data() print(X_train.shape, X_test.shape) ...
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 from keras import models from keras.layers import Dense, Activation model = models.Sequential() ...