python - Why is my margin of error graph so spikey tensorflow

Question

So whenever I run my TensorFlow model the margin of error (loss / val_loss) graph is extremely back and fourth and I was wondering how I could stop this /reduce it here is a picture Graph

here's the code if anyone wants to run it should work fine as long as you have the pips

import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
import matplotlib.pyplot as plt
import datetime
import tensorboard

from keras.models import Sequential
from keras.layers import Dense

train_df = pd.read_csv('https://www.dropbox.com/s/ednsabkdzs8motw/ROK%20INPUT%20DATA%20-%20Sheet1.csv?dl=1')
eval_df = pd.read_csv('https://www.dropbox.com/s/irnqwc1v67wmbfk/ROK%20EVAL%20DATA%20-%20Sheet1.csv?dl=1')


train_df['Troops'] = train_df['Troops'].astype(float)
train_df['Enemy Troops'] = train_df['Enemy Troops'].astype(float)
train_df['Damage'] = train_df['Damage'].astype(float)
eval_df['Troops'] = eval_df['Troops'].astype(float)
eval_df['Enemy Troops'] = eval_df['Enemy Troops'].astype(float)
eval_df['Damage'] = eval_df['Damage'].astype(float)


damage = train_df.pop('Damage')
dataset = tf.data.Dataset.from_tensor_slices((train_df.values, damage.values))

test_labels = eval_df.pop('Damage')
test_features = eval_df.copy()


model = keras.Sequential(
    [
        tf.keras.layers.InputLayer(input_shape = (8,)),
        tf.keras.layers.Dense(8, activation='relu'),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(1),
    ]
)


model.compile(optimizer='adam', loss='mean_squared_error')
model.summary()



history = model.fit(train_df, damage, validation_split=0.2, epochs=5000)

def plot_loss(history):
  plt.plot(history.history['loss'], label='loss')
  plt.plot(history.history['val_loss'], label='val_loss')
  plt.ylim([0, 2000])
  plt.xlabel('Epoch')
  plt.ylabel('Error [MPG]')
  plt.legend()
  plt.grid(True)
plot_loss(history)
plt.show()

Answer 1

等待大神答复