如何设计神经网络以从数组预测数组

我正在尝试设计一个神经网络，以从包含高斯噪声的数据集数组中预测平滑基础函数的数组。我创建了一个包含10000个数组的训练和数据集。现在，我正在尝试预测实际函数的数组值，但它似乎失败了，准确性也不高。有人可以指导我如何进一步改进我的模型以获得更好的准确性并能够预测良好的数据。我使用的代码如下：

用于生成测试和培训数据：

noisy_data = []
pure_data =[]
time = np.arange(1,100)
for i in tqdm(range(10000)):
    array = []
    noise = np.random.normal(0,1/10,99)
    for j in range(1,100):
        array.append( np.log(j))
    array = np.array(array)
    pure_data.append(array)
    noisy_data.append(array+noise)
    

pure_data=np.array(pure_data)
noisy_data=np.array(noisy_data)
    
print(noisy_data.shape)
print(pure_data.shape)

training_size=6000


x_train = noisy_data[:training_size]
y_train = pure_data[:training_size]
x_test = noisy_data[training_size:]
y_test = pure_data[training_size:]
print(x_train.shape)

我的模特：

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(99,)))
model.add(tf.keras.layers.Dense(768, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(768, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(99, activation=tf.nn.softmax))

model.compile(optimizer = 'adam',
         loss = 'categorical_crossentropy',
         metrics = ['accuracy'])

model.fit(x_train, y_train, epochs = 20)

准确性不佳的结果：

Epoch 1/20
125/125 [==============================] - 2s 16ms/step - loss: 947533.1875 - accuracy: 0.0000e+00
Epoch 2/20
125/125 [==============================] - 2s 15ms/step - loss: 9756863.0000 - accuracy: 0.0000e+00
Epoch 3/20
125/125 [==============================] - 2s 16ms/step - loss: 30837548.0000 - accuracy: 0.0000e+00
Epoch 4/20
125/125 [==============================] - 2s 15ms/step - loss: 63707028.0000 - accuracy: 0.0000e+00
Epoch 5/20
125/125 [==============================] - 2s 16ms/step - loss: 107545128.0000 - accuracy: 0.0000e+00
Epoch 6/20
125/125 [==============================] - 1s 12ms/step - loss: 161612192.0000 - accuracy: 0.0000e+00
Epoch 7/20
125/125 [==============================] - 1s 12ms/step - loss: 225245360.0000 - accuracy: 0.0000e+00
Epoch 8/20
125/125 [==============================] - 1s 12ms/step - loss: 297850816.0000 - accuracy: 0.0000e+00
Epoch 9/20
125/125 [==============================] - 1s 12ms/step - loss: 378894176.0000 - accuracy: 0.0000e+00
Epoch 10/20
125/125 [==============================] - 1s 12ms/step - loss: 467893216.0000 - accuracy: 0.0000e+00
Epoch 11/20
125/125 [==============================] - 2s 17ms/step - loss: 564412672.0000 - accuracy: 0.0000e+00
Epoch 12/20
125/125 [==============================] - 2s 15ms/step - loss: 668056384.0000 - accuracy: 0.0000e+00
Epoch 13/20
125/125 [==============================] - 2s 13ms/step - loss: 778468480.0000 - accuracy: 0.0000e+00
Epoch 14/20
125/125 [==============================] - 2s 18ms/step - loss: 895323840.0000 - accuracy: 0.0000e+00
Epoch 15/20
125/125 [==============================] - 2s 13ms/step - loss: 1018332672.0000 - accuracy: 0.0000e+00
Epoch 16/20
125/125 [==============================] - 1s 11ms/step - loss: 1147227136.0000 - accuracy: 0.0000e+00
Epoch 17/20
125/125 [==============================] - 2s 12ms/step - loss: 1281768448.0000 - accuracy: 0.0000e+00
Epoch 18/20
125/125 [==============================] - 2s 14ms/step - loss: 1421732608.0000 - accuracy: 0.0000e+00
Epoch 19/20
125/125 [==============================] - 1s 11ms/step - loss: 1566927744.0000 - accuracy: 0.0000e+00
Epoch 20/20
125/125 [==============================] - 1s 10ms/step - loss: 1717172480.0000 - accuracy: 0.0000e+00

以及我使用的预测代码：

model.predict([noisy_data[0]])

这会抛出错误：

WARNING:tensorflow:Model was constructed with shape (None, 99) for input Tensor("flatten_5_input:0", shape=(None, 99), dtype=float32), but it was called on an input with incompatible shape (None, 1).


ValueError: Input 0 of layer dense_15 is incompatible with the layer: expected axis -1 of input shape to have value 99 but received input with shape [None, 1]