无法从张量流数据集中加载数据

Question

class Jarvis(Model):
    def __init__(self):
        Model.__init__(self)
        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，因为这样要好得多！

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 columns and y is the column you are trying to predict