如何针对我的 keras 训练模型运行测试数据？

Question

训练我的模型的代码是：from keras.models import Sequentialfrom keras.layers import Denseimport numpyimport pandas as pdX = pd.read_csv(    "data/train.csv", header=0, usecols=['Type', 'Age', 'Breed1', 'Breed2', 'Gender', 'Color1', 'Color2', 'Color3', 'MaturitySize', 'FurLength',    'Vaccinated',   'Dewormed', 'Sterilized',   'Health',   'Quantity', 'Fee', 'VideoAmt', 'PhotoAmt'])Y = pd.read_csv(    "data/train.csv", header=0, usecols=['AdoptionSpeed'])X = pd.get_dummies(X, columns=["Type", "Breed1",                               "Breed2", 'Color1', 'Color2', 'Color3', 'Gender', 'MaturitySize', 'FurLength'])print(X)Y = Y['AdoptionSpeed'].apply(lambda v: v / 4)input_units = X.shape[1]model = Sequential()model.add(Dense(input_units, input_dim=input_units, activation='relu'))model.add(Dense(input_units, activation='relu'))model.add(Dense(1, activation='sigmoid'))model.compile(loss='binary_crossentropy',              optimizer='adam', metrics=['accuracy'])model.fit(X, Y, epochs=250, batch_size=1000)scores = model.evaluate(X, Y)我有一个名为test.csv. 我如何针对模型测试该集合以查看我的模型的有效性？它似乎对训练数据有 97% 的准确率，但我担心它可能会过度拟合。