我尝试构建一个 keras 模型来对 kaggle 上的叶子分类数据集中的叶子进行分类。在我编译和训练模型之后，我用它来预测测试图像中叶子的名称，但我得到的只是一个整数数组。我怎样才能准确地解释这些数字以获得叶子的名称。
model = Sequential()
model.add(Dense(128, kernel_initializer="uniform", input_dim= 192, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(99, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model_history = model.fit(x=X_train,y=Y_train, epochs=500, batch_size= 32, validation_data=(X_val, Y_val), verbose=1)
predictions = model.predict(test_arr, batch_size=32, verbose=1)
computed_predictions = np.argmax(predictions, axis=1)
computed_predictions
array([51, 50, 1, 19, 14, 3, 3, 28, 84, 8, 43, 74, 75, 10, 52, 46, 45,
73, 13, 71, 61, 68, 57, 77, 1, 70, 28, 15, 35, 70, 53, 74, 47, 50,
4, 36, 14, 69, 36, 93, 8, 32, 8, 9, 71, 70, 38, 23, 26, 18, 17,
5, 55, 94, 14, 86, 62, 33, 51, 54, 88, 56, 21, 59, 65, 11, 48, 5,
13, 4, 54, 57, 29, 7, 31, 98, 92, 84, 25, 10, 61, 43, 85, 24, 1,
2, 23, 83, 40, 22, 48, 90, 25, 21, 37, 56, 41, 95, 7, 49, 98, 77,
3, 12, 31, 84, 53, 96, 64, 72, 93, 93, 67, 30, 8, 88, 60, 87, 6,
57, 34, 34, 60, 17, 75, 27, 51, 73, 39, 23, 38, 2, 41, 61, 24, 97,
29, 28, 68, 81, 42, 51, 86, 62, 60, 52, 95, 81, 42, 96, 95, 20, 59,
35, 86, 1, 26, 38, 43, 75, 20, 60, 46, 79, 22, 79, 69, 87, 65, 97,
75, 21, 29, 21, 11, 10, 58, 94, 27, 22, 15, 45, 89, 54, 43, 5, 23,
94, 40, 49, 89, 72, 36, 11, 81, 95, 18, 91, 29, 64, 80, 6, 78, 45,
28, 9, 78, 90, 44, 89, 92, 13, 2, 59, 0, 96, 70, 32, 29, 78, 91,
55, 44, 38, 5, 60, 49, 58, 93, 67, 92, 88, 90, 79, 25, 37, 18, 0,
76, 27, 70, 71, 44, 70, 32, 90, 30, 82, 34, 30, 82, 96, 48, 65, 57,
64, 26, 53, 69, 73, 9, 3, 83, 26, 30, 63, 17, 22, 36, 63, 12, 78,
36, 14, 27, 25, 67, 38, 20, 54, 76, 69, 67, 97, 80, 44, 92, 69, 23,
21, 11, 51, 33, 77, 16, 11, 97, 1, 52, 39, 24, 52, 42, 17, 2, 73,
96, 83, 88, 9, 63, 50, 16, 37, 87, 95, 3, 35, 83, 60, 59, 58, 0,
79, 62, 38, 93, 68, 69, 46, 19, 46, 94, 18, 0, 33, 89, 40, 62, 48,
42, 6, 31, 91, 73, 81, 12, 85, 26, 6, 79, 2, 22, 35, 43, 6, 80,
78, 82, 5, 61, 37, 43, 33, 69, 56, 71, 45, 59, 42, 66, 86, 98, 83,
90, 64, 82, 11, 79, 56, 56, 49, 48, 20, 74, 15, 33, 49, 89, 44, 7,
35, 14, 55, 23, 34, 44, 32, 30, 36, 9, 72, 31, 61, 50, 82, 34, 28,
22, 92, 72, 11, 19, 4, 87, 51, 80, 39, 84, 32, 66, 36, 41, 31, 80,
4, 26, 68, 96, 20, 36, 34, 39, 56, 73, 76, 84, 7, 67, 37, 8, 95,
85, 62, 10, 65, 41, 2, 83, 86, 41, 52, 3, 49, 47, 76, 52, 11, 26,
88, 71, 45, 39, 66, 87, 75, 74, 7, 64, 65, 78, 63, 56, 21, 61, 88,
62, 91, 59, 12, 74, 15, 85, 8, 66, 57, 83, 82, 72, 58, 96, 7, 67,
66, 57, 66, 92, 35, 18, 9, 54, 91, 65, 19, 15, 10, 24, 71, 69, 48,
39, 98, 16, 19, 45, 74, 6, 69, 42, 34, 71, 47, 85, 28, 85, 47, 25,
27, 58, 68, 84, 97, 63, 97, 76, 81, 87, 77, 14, 0, 28, 41, 14, 12,
33, 86, 46, 4, 4, 47, 30, 19, 58, 13, 77, 98, 5, 49, 72, 53, 32,
77, 40, 68, 26, 92, 16, 81, 37, 14, 93, 80, 53, 46, 25, 50, 17, 37,
93, 0, 20, 54, 10, 91, 40, 81, 53, 18, 27, 1, 12, 54, 73, 15],
dtype=int64)