從零基礎入門Tensorflow2.0 ----一、3.2 實戰深度神經網絡（批歸一化）

every blog every motto: The shortest answer is doing.

0. 前言

續上節，在神經網絡中添加批歸一化。

1. 代碼部分

1. 導入模塊

import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras

print(tf.__version__)
print(sys.version_info)
for module in mpl,np,pd,sklearn,tf,keras:
    print(module.__name__,module.__version__)

2. 讀取數據

fashion_mnist = keras.datasets.fashion_mnist
# print(fashion_mnist)
(x_train_all,y_train_all),(x_test,y_test) = fashion_mnist.load_data()
x_valid,x_train = x_train_all[:5000],x_train_all[5000:]
y_valid,y_train = y_train_all[:5000],y_train_all[5000:]
# 打印格式
print(x_valid.shape,y_valid.shape)
print(x_train.shape,y_train.shape)
print(x_test.shape,y_test.shape)

3. 數據歸一化

# 數據歸一化
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
# x_train:[None,28,28] -> [None,784]
x_train_scaled = scaler.fit_transform(x_train.astype(np.float32).reshape(-1,1)).reshape(-1,28,28)
x_valid_scaled = scaler.transform(x_valid.astype(np.float32).reshape(-1,1)).reshape(-1,28,28)
x_test_scaled = scaler.transform(x_test.astype(np.float32).reshape(-1,1)).reshape(-1,28,28)

4. 構建模型（添加批歸一化）

注： 有兩種方法：分別是激活函數在前和激活函數在後。

# tf.keras.models.Sequential()
# 構建模型

# 深度神經網絡
model = keras.models.Sequential()
# 輸入數據展平
model.add(keras.layers.Flatten(input_shape=[28,28]))
# 隱藏層 20層
for _ in range(20):
    model.add(keras.layers.Dense(100,activation="relu"))
    # 批歸一化
    model.add(keras.layers.BatchNormalization())
    
    # 另一種方法：激活函數在批歸一化的後面
    """
    model.add(keras.layers.Dense(100))
    model.add(keras.layers.BatchNormalization())
    model.add(keras.layers.Activation('relu'))
    """
# 輸出層
model.add(keras.layers.Dense(10,activation="softmax"))



# 
model.compile(loss='sparse_categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])
model.summary()

5. 開始訓練

# 回調函數 Tensorboard(文件夾）\earylystopping\ModelCheckpoint（文件名）
logdir = os.path.join("dnn-bn-callbacks")
print(logdir)
if not os.path.exists(logdir):
    os.mkdir(logdir)
# 文件名
output_model_file = os.path.join(logdir,"fashion_mnist_model.h5")

callbacks = [
    keras.callbacks.TensorBoard(logdir),
    keras.callbacks.ModelCheckpoint(output_model_file,save_best_only=True),
    keras.callbacks.EarlyStopping(patience=5,min_delta=1e-3),
]
# 開始訓練
history = model.fit(x_train_scaled,y_train,epochs=10,validation_data=(x_valid_scaled,y_valid),callbacks=callbacks)

6. 學習曲線

# 畫圖
def plot_learning_curves(history):
    pd.DataFrame(history.history).plot(figsize=(8,5))
    plt.grid(True)
    plt.gca().set_ylim(0,1)
    plt.show()
plot_learning_curves(history)

# 損失函數，剛開始下降慢的原因
# 1. 參數衆多，訓練不充分
# 2. 梯度消失 -》 鏈式法則中
# 解決： 批歸一化緩解梯度消失

7. 測試集上

model.evaluate(x_test_scaled,y_test)

8. 問題

批歸一化能夠緩解梯度消失問題 。