astype('float32') x_Train4D_normalize = x_Train4D / 255 x_Test4D_normalize = x_Test4D / 255 y_Train = np_utils.to_categorical...(y_Train) y_Test = np_utils.to_categorical(y_Test) CNN建模 建立模型 from keras.models import Sequential from
= X_test.reshape(10000,784).astype('float32') X_train = X_train/255 X_test = X_test/255 y_train = np_utils.to_categorical...(y_train) y_test = np_utils.to_categorical(y_test) MLP建模 建立模型 这次我们在中间加入的为1000个神经元，我们只需要简单修改下代码即可。
x_img_test_normalize = x_img_test.astype('float32') / 255.0 from keras.utils import np_utils y_label_train_OneHot = np_utils.to_categorical...(y_label_train) y_label_test_OneHot = np_utils.to_categorical(y_label_test) CNN建模 模型结构 建立模型 from keras.models
new_test) new_test = new_test.reshape(new_test.shape[0], 1, new_test.shape[1]) trainLabel = np_utils.to_categorical...(trainLabel) val_label = np_utils.to_categorical(val_label) # 单向LSTM model = Sequential(
reshape(10, 120, 120, 3) Y = [0, 0, 1, 1, 2, 2, 3, 3, 2, 0] X = X.astype('float32') Y = np_utils.to_categorical...X = np.array(np.arange(86400)).reshape(2, 120, 120, 3) Y = [0, 1] X = X.astype('float32') Y = np_utils.to_categorical...X = np.array(np.arange(86400)).reshape(2, 120, 120, 3) Y = [0, 1] X = X.astype('float32') Y = np_utils.to_categorical...np.arange(432000)).reshape(10, 120, 120, 3) Y = [0, 0, 1, 1, 2, 2, 3, 3, 2, 0] X = X.astype('float32') Y = np_utils.to_categorical...data X = np.array(np.arange(86400)).reshape(2, 120, 120, 3) Y = [0, 1] X = X.astype('float32') Y = np_utils.to_categorical
x_test.reshape(x_test.shape[0], 28, 28, 1).astype('float32') / 255 # 将类别数据转换为 one-hot 编码 Y_train = np_utils.to_categorical...(y_train, 10) Y_test = np_utils.to_categorical(y_test, 10) ④构建模型 这里采用了Sequential模型，它是一系列层的线性堆叠。...x_test.reshape(x_test.shape[0], 28, 28, 1).astype('float32') / 255 # 将类别数据转换为 one-hot 编码 Y_train = np_utils.to_categorical...(y_train, 10) Y_test = np_utils.to_categorical(y_test, 10) # 构建模型 model = Sequential() # 定义一个序贯模型 #
Y_train = np_utils.to_categorical(trainY, nb_classes) Y_test = np_utils.to_categorical(testY, nb_classes
4D_normalize = train_image_4D / 255 test_image_4D_normalize = test_image_4D / 255 train_label_onehotencoding = np_utils.to_categorical...(train_label) test_label_onehotencoding = np_utils.to_categorical(test_label) 数据预处理之后开始建立模型 from keras.models
float32') 18 19 X_train = X_train / 255 20 X_test = X_test / 255 21 22 # 对输出进行one hot编码 23 y_train = np_utils.to_categorical...(y_train) 24 y_test = np_utils.to_categorical(y_test) 25 num_classes = y_test.shape[1] 26 27 # MLP模型...(y_train) 28 y_test = np_utils.to_categorical(y_test) 29 num_classes = y_test.shape[1] 30 31 # define...(y_train) 24 y_test = np_utils.to_categorical(y_test) 25 num_classes = y_test.shape[1] 26 # define the...(y_train) 27 y_test = np_utils.to_categorical(y_test) 28 num_classes = y_test.shape[1] 29 ###raw 30 #
. # normalize y_train = np_utils.to_categorical(y_train, num_classes=10) y_test = np_utils.to_categorical
= X_test.reshape(10000,784).astype('float32') X_train = X_train/255 X_test = X_test/255 y_train = np_utils.to_categorical...(y_train) y_test = np_utils.to_categorical(y_test) MLP建模 模型结构 输入层为784（28×28）个神经元 ，隐层256个，输出层为10。
float32') # 格式化数据到0~1 x_train = x_train/255 x_validation = x_validation/255 # 进行one-hot编码 y_train = np_utils.to_categorical...(y_train) y_validation = np_utils.to_categorical(y_validation) num_classes = y_validation.shape[1] print...float32') # 格式化数据到0~1 x_train = x_train/255 x_validation = x_validation/255 # 进行one-hot编码 y_train = np_utils.to_categorical...(y_train) y_validation = np_utils.to_categorical(y_validation) # 定义模型 def create_model(): model = Sequential
float32') #归一化 X_train /= 255 X_test /= 255 #将类别训练目标向量转换为二值类别矩阵，即one-hot处理，传入单值，返回制定长度的向量表示形式 Y_train = np_utils.to_categorical...(y_train, NB_CLASSES) Y_test = np_utils.to_categorical(y_test, NB_CLASSES) 至此，数据的预处理部分结束，下面正式进行MLP的模型搭建和训练过程...(y_train, NB_CLASSES) Y_test = np_utils.to_categorical(y_test, NB_CLASSES) '''网络结构搭建部分''' #定义模型为keras...(y_train, NB_CLASSES) Y_test = np_utils.to_categorical(y_test, NB_CLASSES) '''网络结构搭建部分''' #定义模型为keras...(y_train, NB_CLASSES) Y_test = np_utils.to_categorical(y_test, NB_CLASSES) '''网络结构搭建部分''' ##定义模型为keras
我们可以使用Keras中内置的np_utils.to_categorical()函数完成此操作。...# 独热编码 y_train = np_utils.to_categorical(y_train) y_test = np_utils.to_categorical(y_test) num_classes...# 规范化输入从 0-255 到 0-1 X_train = X_train / 255 X_test = X_test / 255 # 独热编码输出 y_train = np_utils.to_categorical...(y_train) y_test = np_utils.to_categorical(y_test) num_classes = y_test.shape[1] 接下来我们定义我们的神经网络模型 卷积神经网络比标准的多层感知器复杂...(y_train) y_test = np_utils.to_categorical(y_test) num_classes = y_test.shape[1] 这一次，我们定义一个大的CNN架构，其中包含额外的卷积
np_utils.to_categorical(y_train, nb_classes=10) 调用up_utils将类标转换成10个长度的值，如果数字是3，则会在对应的地方标记为1，其他地方标记为0，...X_test.shape[0], -1) / 255 # normalize # 将类向量转化为类矩阵 数字 5 转换为 0 0 0 0 0 1 0 0 0 0 矩阵 y_train = np_utils.to_categorical...(y_train, num_classes=10) y_test = np_utils.to_categorical(y_test, num_classes=10) 第三步，创建神经网络层。...(y_train, num_classes=10) y_test = np_utils.to_categorical(y_test, num_classes=10) #----------------...(y_train, num_classes=10) y_test = np_utils.to_categorical(y_test, num_classes=10) #----------------
ImageDataGenerator # 下面是官网的cifar10例子 (x_train, y_train), (x_test, y_test) = cifar10.load_data() y_train = np_utils.to_categorical...(y_train, num_classes) y_test = np_utils.to_categorical(y_test, num_classes) datagen = ImageDataGenerator
X_test.reshape(X_test.shape[0], num_rows, num_cols, num_channels).astype(np.float32) / 255 y_train = np_utils.to_categorical...(y_train) y_test = np_utils.to_categorical(y_test) 设计培训模型。
pre-processing X_train = X_train.reshape(-1, 1, 28, 28)/255 X_test = X_test.reshape(-1, 1, 28, 28)/255 Y_train = np_utils.to_categorical...(Y_train, num_classes=10) Y_test = np_utils.to_categorical(Y_test, num_classes=10) # build CNN model
x_test = x_test.astype('float32') # convert class vectors to binary class matrices y_train = np_utils.to_categorical...(y_train, 10) y_test = np_utils.to_categorical(y_test, 10) x_train = x_train x_test = x_test
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