import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, datasets, losses
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimHei']  # 用來(lái)正常顯示中文標(biāo)簽
plt.rcParams['axes.unicode_minus'] = False  # 用來(lái)正常顯示負(fù)號(hào)

import matplotlib
print(matplotlib.matplotlib_fname())

# 在線下載汽車效能數(shù)據(jù)集
dataset_path = keras.utils.get_file("auto-mpg.data", 
"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data")
# 字段有效能（公里數(shù)每加侖） 氣缸數(shù) 排量 馬力 重量 加速度 型號(hào)年份 產(chǎn)地
column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',
 'Acceleration', 'Model Year', 'Origin']
raw_dataset = pd.read_csv(dataset_path, names=column_names,
 na_values = "?", comment='\t',
 sep=" ", skipinitialspace=True)

dataset = raw_dataset.copy()
# 查看部分?jǐn)?shù)據(jù)
dataset.head(8)

begin = dataset.isna().sum()  # 統(tǒng)計(jì)空白數(shù)據(jù)
dataset = dataset.dropna()  # 刪除空白數(shù)據(jù)項(xiàng)
end = dataset.isna().sum()  # 再次統(tǒng)計(jì)空白數(shù)據(jù)
print(f'========統(tǒng)計(jì)前:========\n{begin}\n========統(tǒng)計(jì)后:========\n{end}')
# 輸出結(jié)果:
========統(tǒng)計(jì)前:========
MPG             0
Cylinders       0
Displacement    0
Horsepower      6
Weight          0
Acceleration    0
Model Year      0
Origin          0
dtype: int64
========統(tǒng)計(jì)后:========
MPG             0
Cylinders       0
Displacement    0
Horsepower      0
Weight          0
Acceleration    0
Model Year      0
Origin          0
dtype: int64

# 先彈出（刪除并返回）Origin這一列
origin = dataset.pop('Origin')
# 根據(jù)origin列的數(shù)據(jù)來(lái)寫入新的3個(gè)列
dataset['USA'] = (origin == 1) * 1.0
dataset['Europe'] = (origin == 2) * 1.0
dataset['Japan'] = (origin == 3) * 1.0
dataset.tail()

# 切分為訓(xùn)練集和測(cè)試集
train_dataset = dataset.sample(frac=0.8, random_state=0)
test_dataset = dataset.drop(train_dataset.index)

# 查看訓(xùn)練集的輸入x的統(tǒng)計(jì)數(shù)據(jù)
train_stats = train_dataset.describe()
train_stats = train_stats.transpose()  # 轉(zhuǎn)置
train_stats

# 移動(dòng)MPG油耗效能這一列為真實(shí)標(biāo)簽Y
train_labels = train_dataset.pop('MPG')
test_labels = test_dataset.pop('MPG')

# 標(biāo)準(zhǔn)化數(shù)據(jù)
def norm(x):
    # 減去每個(gè)字段的均值并除以標(biāo)準(zhǔn)差
    return (x - train_stats['mean']) / train_stats['std']
 
normed_train_data = norm(train_dataset)  # 標(biāo)準(zhǔn)化訓(xùn)練集
normed_test_data = norm(test_dataset)  # 標(biāo)準(zhǔn)化測(cè)試集
print(f'訓(xùn)練集大小:{normed_train_data.shape}  {train_labels.shape}')
print(f"測(cè)試集大小:{normed_test_data.shape}  {test_labels.shape}")
normed_train_data  # 查看數(shù)據(jù)處理后的訓(xùn)練集
# 利用切分的訓(xùn)練集構(gòu)建數(shù)據(jù)集對(duì)象
train_db = tf.data.Dataset.from_tensor_slices((normed_train_data.values, train_labels.values))  # 構(gòu)建Dataset對(duì)象
# 為防止數(shù)據(jù)標(biāo)準(zhǔn)化后出現(xiàn)梯度彌散，切分訓(xùn)練集，分為幾個(gè)batch，加速計(jì)算
train_db = train_db.shuffle(100).batch(32)  # 隨機(jī)打散，批量化
 
# 輸出結(jié)果:
訓(xùn)練集大小:(314, 9)  (314,)
測(cè)試集大小:(78, 9)  (78,)

# 創(chuàng)建同y軸的上下三聯(lián)圖，需要plt.subplots()
fig, axes = plt.subplots(nrows=1,ncols=3,sharey=True,figsize=(20,6)) 
fig.suptitle('各個(gè)字段對(duì)MPG的影響', fontsize=16)
#subplot1
axes[0].scatter(train_dataset['Cylinders'].to_numpy(), train_labels.to_numpy(), color='b',s=20)
axes[0].set_xlabel('Cylinders', fontsize=13)
axes[0].set_ylabel('MPG', fontsize=14)
#subplot2
axes[1].scatter(train_dataset['Displacement'].to_numpy(), train_labels.to_numpy(), color='b',s=20)
axes[1].set_xlabel('Displacement', fontsize=13)
#subplot3
axes[2].scatter(train_dataset['Weight'].to_numpy(), train_labels.to_numpy(), color='b',s=20)
axes[2].set_xlabel('Weight', fontsize=13)
 
#展示圖片
plt.show()

class Network(keras.Model):
    # 回歸網(wǎng)絡(luò)模型
    def __init__(self):
        super(Network, self).__init__()
        # 創(chuàng)建3個(gè)全連接層
        self.fc1 = layers.Dense(64, activation='relu', name='Layer1')
        self.fc2 = layers.Dense(64, activation='relu', name='Layer2')
        self.fc3 = layers.Dense(1, name='OutputLayer')
        
    def call(self, inputs, training=None, mask=None):
        # 依次通過(guò)3個(gè)全連接層
        x = self.fc1(inputs)
        x = self.fc2(x)
        x = self.fc3(x)
        return x

# 創(chuàng)建網(wǎng)絡(luò)類實(shí)例
model = Network()
# 通過(guò)build函數(shù)完成內(nèi)部張量的創(chuàng)建（其中4為任意設(shè)置的batch量，9為輸入特征長(zhǎng)度）
model.build(input_shape=(4, 9))
# 打印網(wǎng)絡(luò)信息
model.summary()
# 創(chuàng)建優(yōu)化器，指定學(xué)習(xí)率為0.001
optimizers = tf.keras.optimizers.RMSprop(0.001)

# 接下來(lái)實(shí)現(xiàn)網(wǎng)絡(luò)訓(xùn)練部分。通過(guò) Epoch 和 Step 組成的雙層循環(huán)訓(xùn)練網(wǎng)絡(luò)，共訓(xùn)練 200 個(gè) Epoch
Epoch =  np.arange(0, 200)
train_MAE = np.zeros(200)
test_MAE = np.zeros(200)
 
for epoch in range(200):
    for step, (x, y) in enumerate(train_db):  # 遍歷依次訓(xùn)練集
        # 梯度記錄器
        with tf.GradientTape() as tape:
            out = model(x)  # 通過(guò)網(wǎng)絡(luò)獲得輸出
            loss = tf.reduce_mean(losses.MSE(y, out))
            mae_loss = tf.reduce_mean(losses.MAE(y, out)) # 計(jì)算 MAE
            # 計(jì)算梯度并更新
            grads = tape.gradient(loss, model.trainable_variables)
            Optimizers.apply_gradients(zip(grads, model.trainable_variables))
    train_MAE[epoch] = float(mae_loss)
    out = model(tf.constant(normed_test_data.values))
    test_MAE[epoch] = tf.reduce_mean(losses.MAE(test_labels, out))

plt.xlabel('Epoch')
plt.ylabel('MAE')
plt.plot(Epoch, train_MAE, label="Train")
plt.plot(Epoch, test_MAE, label="Test")
plt.title('汽車油耗實(shí)戰(zhàn)')
plt.legend(['Train', 'Test'])  # 設(shè)置折線名稱
plt.show()