Question

Jarvis类（模型）： def init （自己）：模型。初始化（个体） self.model = Sequential（）

    # Convulational layers\w MaxPooling
    self.model.add(Conv2D(64, (5, 5), activation="relu"))
    self.model.add(MaxPooling2D((2, 2)))
    self.model.add(Conv2D(64, (5, 5), activation="relu"))
    self.model.add(MaxPooling2D((2, 2)))

    # Flattening layers
    self.model.add(Flatten())

    # Dense layers
    self.model.add(Dense(1000))
    self.model.add(Dense(10, activation="softmax"))

    # Compiling model
    self.model.compile(optimizer="adam",
                       loss="categorical_crossentropy",
                       metrics=["accuracy"])

    self.model.fit(x=train_x, y=train_y,
                   epochs=8, batch_size=100)

我正在像这样加载数据

（train_x，train_y），（test_x，test_y）= tfds.load（“ glue”，split =“ train”，data_dir = os.path.dirname（ file ））

Answer 1

我建议您使用scikit Learn加载数据，因为这样会更好！

首先将数据加载为csv或excel文件：

import pandas as pd
data = pd.read_csv('Example$Path$')

然后从scikitlearn导入train_test_split：

from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)

#X and y over here are the columns of the data. X is the training coluns and y is the column you are trying to predict

希望这对您有所帮助！

无法从张量流数据集中加载数据

1 个答案: