2_用keras训练一个神经网络及不同优化和初始化对性能的影响分析
文章目录
- 用keras 训练一个神经网络
- 目标
- 数据集
- Train/Test split
- Data Preprocess
- Preprocess the target data
- 用keras搭建Feedforward Neural Network
- 使用keras高级api构建模型
- 查看训练结果
- 使用Tensorboard记录训练
- 实验一：不同优化器对算法的影响
- 实验二 改变优化算法：
- 实验三：模型前向推理和泛化性能分析
- 实验四：numpy array与tensorflow tensorflow对比
- 实验五：初始化对模型的影响
用keras 训练一个神经网络
目标
- 使用tensorflow和keras来训练模型
数据集
- Digits: 10 分类手写数字
- http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits
%matplotlib inline
#在本notebook中显示图片
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
digits = load_digits()
#有1797张8乘8大小的图片
digits.images.shape
(1797, 8, 8)
sample_index = 45
plt.figure(figsize=(3, 3))
plt.imshow(digits.images[sample_index], cmap=plt.cm.gray_r,
interpolation='nearest')
plt.title("image label: %d" % digits.target[sample_index]);
Train/Test split
留一些数据来验证算法的泛化性能
from sklearn.model_selection import train_test_split
data = np.asarray(digits.data,dtype=np.float32)
target = np.asarray(digits.target,dtype=np.int32)
X_train,X_test,y_train,y_test = train_test_split(data,target,test_size=0.15,random_state=20)
Data Preprocess
能过对数据预处理，使所有输入数据处在同一尺度
from sklearn import preprocessing
#mean=0 standard deviation=1
scaler = preprocessing.StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
print(scaler.mean_)
print(scaler.scale_)
[0.00000000e+00 3.16306483e-01 5.28880157e+00 1.18474132e+01
1.18375900e+01 5.70268500e+00 1.30844794e+00 1.20497708e-01
5.89390963e-03 2.06221349e+00 1.04184676e+01 1.19377865e+01
1.02501637e+01 8.10150622e+00 1.81728880e+00 9.88867060e-02
3.27439424e-03 2.64047151e+00 9.87557302e+00 6.98166339e+00
7.18860511e+00 7.77406680e+00 1.84413883e+00 5.04256713e-02
1.30975769e-03 2.45972495e+00 9.07138179e+00 8.84217420e+00
9.97904388e+00 7.55926654e+00 2.35625409e+00 1.96463654e-03
0.00000000e+00 2.32023576e+00 7.63326785e+00 9.03274394e+00
1.02776686e+01 8.73280943e+00 2.86247544e+00 0.00000000e+00
9.16830386e-03 1.59135560e+00 6.82711198e+00 7.23379175e+00
7.62671906e+00 8.17943680e+00 3.40667976e+00 2.68500327e-02
6.54878847e-03 7.00065488e-01 7.54878847e+00 9.56712508e+00
9.35101506e+00 8.72757040e+00 3.77799607e+00 1.97118533e-01
0.00000000e+00 2.86836935e-01 5.63457760e+00 1.20445318e+01
1.17832351e+01 6.81008513e+00 2.14472823e+00 3.83104126e-01]
[1. 0.92630336 4.79560205 4.2722852 4.27552413 5.62847896
3.24483584 0.9730583 0.09893657 3.26408982 5.40583492 3.9711706
4.82017417 6.05190754 3.57774757 0.75571091 0.06762714 3.59979068
5.69678963 5.82283094 6.17607195 6.17211406 3.33296379 0.42291689
0.03616687 3.13244768 6.19045142 5.87475516 6.15151042 5.8499233
3.72096472 0.04428066 1. 3.48054792 6.33937763 6.29957041
5.92919758 5.87241474 3.52455323 1. 0.15326963 2.97038492
6.54781209 6.44261037 6.25789493 5.72445399 4.31597845 0.31746699
0.20779585 1.73046774 5.65361855 5.21149745 5.37096999 6.00854386
4.95227656 0.9060906 1. 0.93199177 5.13435047 4.39614727
4.94708425 5.94346449 4.1585327 1.89523995]
显示一张经过预处理后的图来看
sample_index = 45
plt.figure(figsize=(3, 3))
plt.imshow(X_train[sample_index].reshape(8, 8),
cmap=plt.cm.gray_r, interpolation='nearest')
plt.title("transformed sample\n(standardization)");
scaler可以反向操作，恢复到原来
plt.figure(figsize=(3, 3))
plt.imshow(scaler.inverse_transform(X_train[sample_index]).reshape(8, 8),
cmap=plt.cm.gray_r, interpolation='nearest')
plt.title("original sample");
print(X_train.shape,y_train.shape)
(1527, 64) (1527,)
print(X_test.shape,y_test.shape)
(270, 64) (270,)
Preprocess the target data
还需要对标签进行处理，将它编码成one-hot的开式
y_train[:3]
array([4, 0, 7], dtype=int32)
#keras提供了转换的api
from tensorflow.keras.utils import to_categorical
Y_train=to_categorical(y_train)
Y_train[:3]
array([[0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 1., 0., 0.]], dtype=float32)
用keras搭建Feedforward Neural Network
- 用
Keras构建并训练一个神经网络- https://www.tensorflow.org/guide/keras/overview
- 使用不同的优化算法、初始化方法、激活函数、网络层数来做实验（Experiments with different optimizers,initializations,activations and size of layers
Experiment with different optimizers, activations, size of layers, initializations
使用keras高级api构建模型
- 通过堆叠layer构建网络
- 定义损失函数和优化算法
- feed model with data 来训练网络
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Activation
from tensorflow.keras.optimizers import SGD
input_dim = X_train.shape[1]
hidden_dim=100
output_dim=10
model = Sequential([
Dense(hidden_dim,input_dim=input_dim,activation="tanh"),
Dense(output_dim,activation="softmax")
])
model.compile(optimizer=SGD(lr=0.1),loss="categorical_crossentropy",metrics=["accuracy"])
history=model.fit(X_train,Y_train,validation_split=0.2,epochs=15,batch_size=32)
Train on 1221 samples, validate on 306 samples
Epoch 1/15
1221/1221 [==============================] - 1s 572us/sample - loss: 0.9697 - accuracy: 0.7543 - val_loss: 0.4316 - val_accuracy: 0.9150
Epoch 2/15
1221/1221 [==============================] - 0s 61us/sample - loss: 0.3328 - accuracy: 0.9287 - val_loss: 0.2767 - val_accuracy: 0.9444
Epoch 3/15
1221/1221 [==============================] - 0s 94us/sample - loss: 0.2264 - accuracy: 0.9541 - val_loss: 0.1988 - val_accuracy: 0.9641
Epoch 4/15
1221/1221 [==============================] - 0s 98us/sample - loss: 0.1771 - accuracy: 0.9664 - val_loss: 0.1761 - val_accuracy: 0.9641
Epoch 5/15
1221/1221 [==============================] - 0s 101us/sample - loss: 0.1452 - accuracy: 0.9746 - val_loss: 0.1588 - val_accuracy: 0.9641
Epoch 6/15
1221/1221 [==============================] - 0s 98us/sample - loss: 0.1250 - accuracy: 0.9779 - val_loss: 0.1482 - val_accuracy: 0.9641
Epoch 7/15
1221/1221 [==============================] - 0s 101us/sample - loss: 0.1098 - accuracy: 0.9828 - val_loss: 0.1366 - val_accuracy: 0.9673
Epoch 8/15
1221/1221 [==============================] - 0s 117us/sample - loss: 0.0979 - accuracy: 0.9885 - val_loss: 0.1312 - val_accuracy: 0.9673
Epoch 9/15
1221/1221 [==============================] - 0s 132us/sample - loss: 0.0882 - accuracy: 0.9877 - val_loss: 0.1221 - val_accuracy: 0.9673
Epoch 10/15
1221/1221 [==============================] - 0s 142us/sample - loss: 0.0802 - accuracy: 0.9910 - val_loss: 0.1196 - val_accuracy: 0.9641
Epoch 11/15
1221/1221 [==============================] - 0s 144us/sample - loss: 0.0732 - accuracy: 0.9926 - val_loss: 0.1162 - val_accuracy: 0.9641
Epoch 12/15
1221/1221 [==============================] - 0s 145us/sample - loss: 0.0679 - accuracy: 0.9934 - val_loss: 0.1133 - val_accuracy: 0.9673
Epoch 13/15
1221/1221 [==============================] - 0s 143us/sample - loss: 0.0627 - accuracy: 0.9934 - val_loss: 0.1098 - val_accuracy: 0.9673
Epoch 14/15
1221/1221 [==============================] - 0s 142us/sample - loss: 0.0583 - accuracy: 0.9951 - val_loss: 0.1118 - val_accuracy: 0.9673
Epoch 15/15
1221/1221 [==============================] - 0s 145us/sample - loss: 0.0545 - accuracy: 0.9959 - val_loss: 0.1099 - val_accuracy: 0.9706
查看训练结果
history.history
{'loss': [0.9696895429387041,
0.33279134834720697,
0.22635988889904318,
0.17707091860204666,
0.1452269182585479,
0.12495933416971508,
0.10980282952119638,
0.09786553974709582,
0.08823802012776275,
0.08023971164817209,
0.07324654804840135,
0.06792114496560589,
0.06273095264274209,
0.05828069021084775,
0.054510549638331865],
'accuracy': [0.75429976,
0.92874694,
0.95413595,
0.96642095,
0.974611,
0.977887,
0.98280096,
0.988534,
0.987715,
0.990991,
0.992629,
0.993448,
0.993448,
0.995086,
0.995905],
'val_loss': [0.4316010675788705,
0.2766764386027467,
0.19876952089515387,
0.17614836484388588,
0.15884678348217135,
0.148228091498216,
0.13663518302191316,
0.13120633066674464,
0.12209573826369118,
0.11959585177353005,
0.11619883415356182,
0.11327453997302679,
0.10981486129020554,
0.11178031816981197,
0.10993434048069069],
'val_accuracy': [0.9150327,
0.9444444,
0.96405226,
0.96405226,
0.96405226,
0.96405226,
0.96732026,
0.96732026,
0.96732026,
0.96405226,
0.96405226,
0.96732026,
0.96732026,
0.96732026,
0.9705882]}
history.epoch
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
将训练结果转换成panda数据类型并画出来
import pandas as pd
history_df = pd.DataFrame(history.history)
history_df["epoch"]=history.epoch
history_df
| loss | accuracy | val_loss | val_accuracy | epoch | |
|---|---|---|---|---|---|
| 0 | 0.969690 | 0.754300 | 0.431601 | 0.915033 | 0 |
| 1 | 0.332791 | 0.928747 | 0.276676 | 0.944444 | 1 |
| 2 | 0.226360 | 0.954136 | 0.198770 | 0.964052 | 2 |
| 3 | 0.177071 | 0.966421 | 0.176148 | 0.964052 | 3 |
| 4 | 0.145227 | 0.974611 | 0.158847 | 0.964052 | 4 |
| 5 | 0.124959 | 0.977887 | 0.148228 | 0.964052 | 5 |
| 6 | 0.109803 | 0.982801 | 0.136635 | 0.967320 | 6 |
| 7 | 0.097866 | 0.988534 | 0.131206 | 0.967320 | 7 |
| 8 | 0.088238 | 0.987715 | 0.122096 | 0.967320 | 8 |
| 9 | 0.080240 | 0.990991 | 0.119596 | 0.964052 | 9 |
| 10 | 0.073247 | 0.992629 | 0.116199 | 0.964052 | 10 |
| 11 | 0.067921 | 0.993448 | 0.113275 | 0.967320 | 11 |
| 12 | 0.062731 | 0.993448 | 0.109815 | 0.967320 | 12 |
| 13 | 0.058281 | 0.995086 | 0.111780 | 0.967320 | 13 |
| 14 | 0.054511 | 0.995905 | 0.109934 | 0.970588 | 14 |
fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(12, 6))
history_df.plot(x="epoch", y=["loss", "val_loss"], ax=ax0)
history_df.plot(x="epoch", y=["accuracy", "val_accuracy"], ax=ax1);
使用Tensorboard记录训练
tensorboard是内置的tensorflow的官方工具
%load_ext tensorboard
!rm -rf tensorboard_logs
import datetime
from tensorflow.keras.callbacks import TensorBoard
model = Sequential()
model.add(Dense(hidden_dim, input_dim=input_dim, activation="tanh"))
model.add(Dense(output_dim, activation="softmax"))
model.compile(optimizer=SGD(lr=0.1),
loss='categorical_crossentropy', metrics=['accuracy'])
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
log_dir = "tensorboard_logs/" + timestamp
tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1)
model.fit(x=X_train, y=Y_train, validation_split=0.2, epochs=15,batch_size=32,
callbacks=[tensorboard_callback]);
Train on 1221 samples, validate on 306 samples
Epoch 1/15
1221/1221 [==============================] - 0s 239us/sample - loss: 0.9385 - accuracy: 0.7592 - val_loss: 0.4219 - val_accuracy: 0.9150
Epoch 2/15
1221/1221 [==============================] - 0s 78us/sample - loss: 0.3136 - accuracy: 0.9451 - val_loss: 0.2747 - val_accuracy: 0.9444
Epoch 3/15
1221/1221 [==============================] - 0s 123us/sample - loss: 0.2153 - accuracy: 0.9574 - val_loss: 0.2143 - val_accuracy: 0.9608
Epoch 4/15
1221/1221 [==============================] - 0s 128us/sample - loss: 0.1682 - accuracy: 0.9681 - val_loss: 0.1871 - val_accuracy: 0.9575
Epoch 5/15
1221/1221 [==============================] - 0s 127us/sample - loss: 0.1406 - accuracy: 0.9730 - val_loss: 0.1646 - val_accuracy: 0.9608
Epoch 6/15
1221/1221 [==============================] - 0s 127us/sample - loss: 0.1203 - accuracy: 0.9803 - val_loss: 0.1515 - val_accuracy: 0.9608
Epoch 7/15
1221/1221 [==============================] - 0s 146us/sample - loss: 0.1064 - accuracy: 0.9812 - val_loss: 0.1422 - val_accuracy: 0.9641
Epoch 8/15
1221/1221 [==============================] - 0s 146us/sample - loss: 0.0937 - accuracy: 0.9844 - val_loss: 0.1364 - val_accuracy: 0.9673
Epoch 9/15
1221/1221 [==============================] - 0s 145us/sample - loss: 0.0848 - accuracy: 0.9885 - val_loss: 0.1295 - val_accuracy: 0.9673
Epoch 10/15
1221/1221 [==============================] - 0s 146us/sample - loss: 0.0768 - accuracy: 0.9894 - val_loss: 0.1262 - val_accuracy: 0.9673
Epoch 11/15
1221/1221 [==============================] - 0s 151us/sample - loss: 0.0702 - accuracy: 0.9918 - val_loss: 0.1241 - val_accuracy: 0.9641
Epoch 12/15
1221/1221 [==============================] - 0s 147us/sample - loss: 0.0647 - accuracy: 0.9934 - val_loss: 0.1205 - val_accuracy: 0.9673
Epoch 13/15
1221/1221 [==============================] - 0s 164us/sample - loss: 0.0596 - accuracy: 0.9943 - val_loss: 0.1193 - val_accuracy: 0.9706
Epoch 14/15
1221/1221 [==============================] - 0s 223us/sample - loss: 0.0553 - accuracy: 0.9943 - val_loss: 0.1172 - val_accuracy: 0.9706
Epoch 15/15
1221/1221 [==============================] - 0s 237us/sample - loss: 0.0517 - accuracy: 0.9959 - val_loss: 0.1166 - val_accuracy: 0.9706
#调用tensorboard
%tensorboard --logdir tensorboard_logs
实验一：不同优化器对算法的影响
- 调小学习率10或100倍
- 调大学习率（记录模型训练发散）
- 使SGD加上 Nesterov momentum of 0.9
SGD? 加问号可以显示帮助文档，相当于(help(SGD))
SGD 在输入要查询的api后再按下shift+tab可以查看帮助文档
keras的api文档https://www.tensorflow.org/api_docs/python/tf/keras
或直接上keras官网查看
SGD?
#改小学习率 0.001。分析：会收敛很慢
model = Sequential()
model.add(Dense(hidden_dim, input_dim=input_dim,
activation="tanh"))
model.add(Dense(output_dim, activation="softmax"))
model.add(Activation("softmax"))
optimizer = SGD(lr=0.001)
model.compile(optimizer=optimizer, loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(X_train, Y_train, validation_split=0.2,
epochs=15, batch_size=32)
fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(12, 6))
history_df = pd.DataFrame(history.history)
history_df["epoch"] = history.epoch
history_df.plot(x="epoch", y=["loss", "val_loss"], ax=ax0)
history_df.plot(x="epoch", y=["accuracy", "val_accuracy"], ax=ax1);
Train on 1221 samples, validate on 306 samples
Epoch 1/15
1221/1221 [==============================] - 0s 227us/sample - loss: 2.2873 - accuracy: 0.1687 - val_loss: 2.2880 - val_accuracy: 0.1340
Epoch 2/15
1221/1221 [==============================] - 0s 56us/sample - loss: 2.2859 - accuracy: 0.1720 - val_loss: 2.2867 - val_accuracy: 0.1340
Epoch 3/15
1221/1221 [==============================] - 0s 77us/sample - loss: 2.2845 - accuracy: 0.1794 - val_loss: 2.2854 - val_accuracy: 0.1373
Epoch 4/15
1221/1221 [==============================] - 0s 89us/sample - loss: 2.2831 - accuracy: 0.1835 - val_loss: 2.2840 - val_accuracy: 0.1438
Epoch 5/15
1221/1221 [==============================] - 0s 99us/sample - loss: 2.2817 - accuracy: 0.1884 - val_loss: 2.2827 - val_accuracy: 0.1471
Epoch 6/15
1221/1221 [==============================] - 0s 107us/sample - loss: 2.2802 - accuracy: 0.1957 - val_loss: 2.2813 - val_accuracy: 0.1503
Epoch 7/15
1221/1221 [==============================] - 0s 123us/sample - loss: 2.2787 - accuracy: 0.1982 - val_loss: 2.2799 - val_accuracy: 0.1569
Epoch 8/15
1221/1221 [==============================] - 0s 121us/sample - loss: 2.2772 - accuracy: 0.2015 - val_loss: 2.2785 - val_accuracy: 0.1601
Epoch 9/15
1221/1221 [==============================] - 0s 119us/sample - loss: 2.2757 - accuracy: 0.2056 - val_loss: 2.2770 - val_accuracy: 0.1699
Epoch 10/15
1221/1221 [==============================] - 0s 121us/sample - loss: 2.2742 - accuracy: 0.2113 - val_loss: 2.2756 - val_accuracy: 0.1667
Epoch 11/15
1221/1221 [==============================] - 0s 121us/sample - loss: 2.2726 - accuracy: 0.2138 - val_loss: 2.2741 - val_accuracy: 0.1699
Epoch 12/15
1221/1221 [==============================] - 0s 121us/sample - loss: 2.2710 - accuracy: 0.2228 - val_loss: 2.2725 - val_accuracy: 0.1765
Epoch 13/15
1221/1221 [==============================] - 0s 121us/sample - loss: 2.2693 - accuracy: 0.2260 - val_loss: 2.2710 - val_accuracy: 0.1797
Epoch 14/15
1221/1221 [==============================] - 0s 119us/sample - loss: 2.2677 - accuracy: 0.2301 - val_loss: 2.2694 - val_accuracy: 0.1863
Epoch 15/15
1221/1221 [==============================] - 0s 118us/sample - loss: 2.2660 - accuracy: 0.2367 - val_loss: 2.2678 - val_accuracy: 0.1863
#改大学习率 10。分析：模型在局部最优处反复,训练损失值会比较大
model = Sequential()
model.add(Dense(hidden_dim, input_dim=input_dim,
activation="tanh"))
model.add(Dense(output_dim, activation="softmax"))
model.add(Activation("softmax"))
optimizer = SGD(lr=10)
model.compile(optimizer=optimizer, loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(X_train, Y_train, validation_split=0.2,
epochs=15, batch_size=32)
fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(12, 6))
history_df = pd.DataFrame(history.history)
history_df["epoch"] = history.epoch
history_df.plot(x="epoch", y=["loss", "val_loss"], ax=ax0)
history_df.plot(x="epoch", y=["accuracy", "val_accuracy"], ax=ax1);
Train on 1221 samples, validate on 306 samples
Epoch 1/15
1221/1221 [==============================] - 0s 228us/sample - loss: 1.7026 - accuracy: 0.7617 - val_loss: 1.9520 - val_accuracy: 0.5065
Epoch 2/15
1221/1221 [==============================] - 0s 59us/sample - loss: 1.5669 - accuracy: 0.8943 - val_loss: 1.5387 - val_accuracy: 0.9281
Epoch 3/15
1221/1221 [==============================] - 0s 70us/sample - loss: 1.5139 - accuracy: 0.9500 - val_loss: 1.5286 - val_accuracy: 0.9379
Epoch 4/15
1221/1221 [==============================] - 0s 79us/sample - loss: 1.4991 - accuracy: 0.9640 - val_loss: 1.6736 - val_accuracy: 0.7810
Epoch 5/15
1221/1221 [==============================] - 0s 82us/sample - loss: 1.5457 - accuracy: 0.9181 - val_loss: 1.4956 - val_accuracy: 0.9673
Epoch 6/15
1221/1221 [==============================] - 0s 120us/sample - loss: 1.4872 - accuracy: 0.9746 - val_loss: 1.5001 - val_accuracy: 0.9673
Epoch 7/15
1221/1221 [==============================] - 0s 120us/sample - loss: 1.4800 - accuracy: 0.9820 - val_loss: 1.4933 - val_accuracy: 0.9673
Epoch 8/15
1221/1221 [==============================] - 0s 120us/sample - loss: 1.4756 - accuracy: 0.9853 - val_loss: 1.5073 - val_accuracy: 0.9542
Epoch 9/15
1221/1221 [==============================] - 0s 119us/sample - loss: 1.4777 - accuracy: 0.9828 - val_loss: 1.4925 - val_accuracy: 0.9673
Epoch 10/15
1221/1221 [==============================] - 0s 121us/sample - loss: 1.4796 - accuracy: 0.9828 - val_loss: 1.4875 - val_accuracy: 0.9739
Epoch 11/15
1221/1221 [==============================] - 0s 118us/sample - loss: 1.4728 - accuracy: 0.9894 - val_loss: 1.4857 - val_accuracy: 0.9804
Epoch 12/15
1221/1221 [==============================] - 0s 120us/sample - loss: 1.4737 - accuracy: 0.9877 - val_loss: 1.4903 - val_accuracy: 0.9706
Epoch 13/15
1221/1221 [==============================] - 0s 124us/sample - loss: 1.4712 - accuracy: 0.9902 - val_loss: 1.5030 - val_accuracy: 0.9575
Epoch 14/15
1221/1221 [==============================] - 0s 120us/sample - loss: 1.4726 - accuracy: 0.9894 - val_loss: 1.4885 - val_accuracy: 0.9739
Epoch 15/15
1221/1221 [==============================] - 0s 120us/sample - loss: 1.4694 - accuracy: 0.9918 - val_loss: 1.4892 - val_accuracy: 0.9739
#使用momentum 分析：缓解学习率小，训练慢的问题
model = Sequential()
model.add(Dense(hidden_dim, input_dim=input_dim,
activation="tanh"))
model.add(Dense(output_dim, activation="softmax"))
model.add(Activation("softmax"))
optimizer = SGD(lr=0.1, momentum=0.9, nesterov=True)
model.compile(optimizer=optimizer, loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(X_train, Y_train, validation_split=0.2,
epochs=15, batch_size=32)
fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(12, 6))
history_df = pd.DataFrame(history.history)
history_df["epoch"] = history.epoch
history_df.plot(x="epoch", y=["loss", "val_loss"], ax=ax0)
history_df.plot(x="epoch", y=["accuracy", "val_accuracy"], ax=ax1);
Train on 1221 samples, validate on 306 samples
Epoch 1/15
1221/1221 [==============================] - 0s 235us/sample - loss: 1.9252 - accuracy: 0.5946 - val_loss: 1.6523 - val_accuracy: 0.8137
Epoch 2/15
1221/1221 [==============================] - 0s 56us/sample - loss: 1.5720 - accuracy: 0.9222 - val_loss: 1.5479 - val_accuracy: 0.9346
Epoch 3/15
1221/1221 [==============================] - 0s 56us/sample - loss: 1.5193 - accuracy: 0.9648 - val_loss: 1.5266 - val_accuracy: 0.9477
Epoch 4/15
1221/1221 [==============================] - 0s 71us/sample - loss: 1.5028 - accuracy: 0.9779 - val_loss: 1.5084 - val_accuracy: 0.9608
Epoch 5/15
1221/1221 [==============================] - 0s 76us/sample - loss: 1.4935 - accuracy: 0.9812 - val_loss: 1.5066 - val_accuracy: 0.9641
Epoch 6/15
1221/1221 [==============================] - 0s 100us/sample - loss: 1.4876 - accuracy: 0.9861 - val_loss: 1.5061 - val_accuracy: 0.9608
Epoch 7/15
1221/1221 [==============================] - 0s 117us/sample - loss: 1.4862 - accuracy: 0.9853 - val_loss: 1.5017 - val_accuracy: 0.9673
Epoch 8/15
1221/1221 [==============================] - 0s 124us/sample - loss: 1.4817 - accuracy: 0.9877 - val_loss: 1.4997 - val_accuracy: 0.9673
Epoch 9/15
1221/1221 [==============================] - 0s 122us/sample - loss: 1.4788 - accuracy: 0.9894 - val_loss: 1.4991 - val_accuracy: 0.9673
Epoch 10/15
1221/1221 [==============================] - 0s 142us/sample - loss: 1.4766 - accuracy: 0.9918 - val_loss: 1.5002 - val_accuracy: 0.9673
Epoch 11/15
1221/1221 [==============================] - 0s 149us/sample - loss: 1.4752 - accuracy: 0.9918 - val_loss: 1.4983 - val_accuracy: 0.9706
Epoch 12/15
1221/1221 [==============================] - 0s 153us/sample - loss: 1.4742 - accuracy: 0.9918 - val_loss: 1.4981 - val_accuracy: 0.9641
Epoch 13/15
1221/1221 [==============================] - 0s 151us/sample - loss: 1.4734 - accuracy: 0.9918 - val_loss: 1.4974 - val_accuracy: 0.9641
Epoch 14/15
1221/1221 [==============================] - 0s 151us/sample - loss: 1.4729 - accuracy: 0.9918 - val_loss: 1.4968 - val_accuracy: 0.9673
Epoch 15/15
1221/1221 [==============================] - 0s 149us/sample - loss: 1.4726 - accuracy: 0.9918 - val_loss: 1.4968 - val_accuracy: 0.9641
实验二 改变优化算法：
- 使用Adam替代SGD,并使用Adam的默认参数
- 填加另一层隐藏层，并改所有激活函数为RELU
from tensorflow.keras.optimizers import Adam
model = Sequential()
model.add(Dense(hidden_dim, input_dim=input_dim,
activation="relu"))
model.add(Dense(hidden_dim, activation="relu"))
model.add(Dense(output_dim, activation="softmax"))
optimizer = Adam()
model.compile(optimizer=optimizer, loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(X_train, Y_train, validation_split=0.2,
epochs=15, batch_size=32)
fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(12, 6))
history_df = pd.DataFrame(history.history)
history_df["epoch"] = history.epoch
history_df.plot(x="epoch", y=["loss", "val_loss"], ax=ax0)
history_df.plot(x="epoch", y=["accuracy", "val_accuracy"], ax=ax1);
Train on 1221 samples, validate on 306 samples
Epoch 1/15
1221/1221 [==============================] - 0s 267us/sample - loss: 1.5497 - accuracy: 0.6093 - val_loss: 0.8136 - val_accuracy: 0.8529
Epoch 2/15
1221/1221 [==============================] - 0s 58us/sample - loss: 0.4807 - accuracy: 0.9156 - val_loss: 0.3043 - val_accuracy: 0.9444
Epoch 3/15
1221/1221 [==============================] - 0s 68us/sample - loss: 0.2116 - accuracy: 0.9615 - val_loss: 0.1806 - val_accuracy: 0.9542
Epoch 4/15
1221/1221 [==============================] - 0s 105us/sample - loss: 0.1334 - accuracy: 0.9746 - val_loss: 0.1390 - val_accuracy: 0.9673
Epoch 5/15
1221/1221 [==============================] - 0s 124us/sample - loss: 0.0882 - accuracy: 0.9877 - val_loss: 0.1226 - val_accuracy: 0.9641
Epoch 6/15
1221/1221 [==============================] - 0s 124us/sample - loss: 0.0671 - accuracy: 0.9885 - val_loss: 0.1034 - val_accuracy: 0.9706
Epoch 7/15
1221/1221 [==============================] - 0s 128us/sample - loss: 0.0512 - accuracy: 0.9934 - val_loss: 0.1006 - val_accuracy: 0.9739
Epoch 8/15
1221/1221 [==============================] - 0s 127us/sample - loss: 0.0383 - accuracy: 0.9959 - val_loss: 0.0881 - val_accuracy: 0.9739
Epoch 9/15
1221/1221 [==============================] - 0s 127us/sample - loss: 0.0304 - accuracy: 0.9975 - val_loss: 0.0846 - val_accuracy: 0.9739
Epoch 10/15
1221/1221 [==============================] - 0s 124us/sample - loss: 0.0250 - accuracy: 0.9967 - val_loss: 0.0789 - val_accuracy: 0.9771
Epoch 11/15
1221/1221 [==============================] - 0s 124us/sample - loss: 0.0204 - accuracy: 0.9984 - val_loss: 0.0794 - val_accuracy: 0.9739
Epoch 12/15
1221/1221 [==============================] - 0s 125us/sample - loss: 0.0172 - accuracy: 0.9984 - val_loss: 0.0813 - val_accuracy: 0.9706
Epoch 13/15
1221/1221 [==============================] - 0s 125us/sample - loss: 0.0137 - accuracy: 1.0000 - val_loss: 0.0683 - val_accuracy: 0.9804
Epoch 14/15
1221/1221 [==============================] - 0s 124us/sample - loss: 0.0115 - accuracy: 1.0000 - val_loss: 0.0681 - val_accuracy: 0.9837
Epoch 15/15
1221/1221 [==============================] - 0s 121us/sample - loss: 0.0091 - accuracy: 1.0000 - val_loss: 0.0696 - val_accuracy: 0.9804
分析：Adam的默认学习率是0.001，该优化算法速度往往会比SGD快，并且默参数可以很好的调节更新梯度，在很多的优化中都有很好的效果，所以使用默认参数往往就足够。
实验三：模型前向推理和泛化性能分析
- 对测试集进行前向理量，获得测试结果
- 对测试结果进行分析
y_predicted = model.predict_classes(X_test, verbose=0)
# Let's display the first inputs image, the predicted labels and the true labels
fig, axes = plt.subplots(ncols=5, nrows=3, figsize=(12, 9))
for i, ax in enumerate(axes.ravel()):
ax.imshow(scaler.inverse_transform(X_test[i]).reshape(8, 8),
cmap=plt.cm.gray_r, interpolation='nearest')
ax.set_title("predicted label: %d\n true label: %d"
% (y_predicted[i], y_test[i]))
print("test acc: %0.4f" % np.mean(y_predicted == y_test))
test acc: 0.9704
实验四：numpy array与tensorflow tensorflow对比
上一个实验中model.predict_classes(…) 返回的是numpy array
predicted_labels_numpy = model.predict_classes(X_test)
predicted_labels_numpy
array([0, 7, 9, 5, 8, 1, 3, 3, 7, 0, 9, 4, 7, 4, 0, 1, 1, 8, 1, 3, 7, 8,
4, 6, 1, 0, 1, 0, 5, 4, 7, 1, 6, 7, 8, 4, 3, 7, 4, 0, 5, 9, 0, 4,
8, 7, 4, 3, 6, 3, 9, 2, 2, 5, 7, 3, 3, 8, 3, 8, 6, 6, 8, 6, 8, 5,
0, 5, 3, 5, 0, 7, 3, 2, 9, 9, 3, 0, 2, 8, 5, 9, 2, 4, 5, 1, 7, 7,
2, 3, 0, 4, 6, 1, 9, 7, 1, 9, 8, 3, 4, 6, 7, 8, 1, 8, 4, 0, 1, 3,
6, 9, 5, 5, 1, 6, 0, 6, 2, 8, 9, 4, 1, 3, 4, 0, 6, 7, 7, 9, 8, 7,
8, 2, 4, 2, 5, 4, 3, 8, 8, 9, 8, 0, 0, 6, 2, 6, 9, 0, 9, 0, 0, 8,
7, 5, 3, 4, 0, 5, 6, 2, 6, 0, 4, 8, 7, 9, 2, 4, 3, 6, 4, 4, 5, 2,
8, 0, 7, 7, 3, 2, 2, 9, 0, 7, 2, 1, 6, 7, 9, 1, 5, 1, 6, 4, 6, 1,
3, 6, 1, 0, 8, 6, 5, 8, 8, 9, 1, 5, 1, 2, 6, 7, 5, 0, 1, 2, 4, 7,
0, 7, 6, 4, 7, 6, 5, 1, 2, 5, 5, 4, 6, 1, 7, 6, 1, 8, 9, 6, 2, 8,
5, 8, 3, 3, 9, 0, 3, 7, 9, 9, 1, 7, 0, 0, 5, 7, 3, 6, 3, 8, 6, 3,
6, 9, 8, 3, 7, 4])
type(predicted_labels_numpy), predicted_labels_numpy.shape
(numpy.ndarray, (270,))
也可以获得tensorflow tensor的输出
predictions_tf = model(X_test)
predictions_tf[:5]
type(predictions_tf), predictions_tf.shape
(tensorflow.python.framework.ops.EagerTensor, TensorShape([270, 10]))
使用tensorflow检查所有结查和是否为1
import tensorflow as tf
tf.reduce_sum(predictions_tf, axis=1)[:5]
还可以使用tensorflow api来获取最大概率值所在位置的标签
predicted_labels_tf = tf.argmax(predictions_tf, axis=1)
predicted_labels_tf[:5]
计算精度
accuracy_tf = tf.reduce_mean(tf.cast(predicted_labels_tf == y_test, tf.float64))
accuracy_tf
喜欢用numpy也可以将tensor转换成numpy
accuracy_tf.numpy()
0.9703703703703703
predicted_labels_tf[:5]
predicted_labels_tf.numpy()[:5]
array([0, 7, 9, 5, 8])
(predicted_labels_tf.numpy() == y_test).mean()
0.9703703703703703
实验五：初始化对模型的影响
研究初始化对模型性能的影响，有好的初始化和不好的初始化。
Keras对Dense layer的默认初始化方法是“glorot_uniform”：
- 所有参数随机初始化在[-scale,+scale]区间。
- scale= 1 n i n + n o u t \frac{1}{\sqrt{n_{in} + n_{out}}} nin+nout1
这种初始化方法对"tanh"和"relu"作为激活函数用标准SGD来训练有很好的效果。
为了验证初始化的影响，我们将两个dense层设置为"tanh"激活函数，并用gaussian分布用不同的标准差来产生不同的scale。
from tensorflow.keras import initializers
normal_init = initializers.TruncatedNormal(stddev=0.01)
model = Sequential()
model.add(Dense(hidden_dim, input_dim=input_dim, activation="tanh",
kernel_initializer=normal_init))
model.add(Dense(hidden_dim, activation="tanh",
kernel_initializer=normal_init))
model.add(Dense(output_dim, activation="softmax",
kernel_initializer=normal_init))
model.compile(optimizer=SGD(lr=0.1),
loss='categorical_crossentropy', metrics=['accuracy'])
model.layers#有三层
[,
,
]
看一下模型在训练更新参数前的初始化值
model.layers[0].weights
[,
]
w = model.layers[0].weights[0].numpy()
w
array([[ 0.00600229, -0.00476325, 0.00307717, ..., 0.01070479,
-0.00400034, -0.00340873],
[-0.00233381, 0.00023561, 0.00894215, ..., -0.01630376,
0.00206616, 0.00639522],
[-0.0010351 , -0.00708832, 0.00056756, ..., 0.01145899,
-0.01235237, 0.00167793],
...,
[ 0.00522072, -0.00148367, 0.0110029 , ..., 0.00078427,
0.00486906, -0.00247767],
[-0.0015619 , 0.00171984, -0.00316272, ..., -0.01050979,
-0.00815977, 0.00415378],
[-0.00681211, -0.00738525, -0.00051109, ..., 0.00828084,
0.00679922, 0.00588555]], dtype=float32)
w.std()
0.0086482065
b = model.layers[0].weights[1].numpy()
b
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
dtype=float32)
history = model.fit(X_train, Y_train, epochs=15, batch_size=32)
plt.figure(figsize=(12, 4))
plt.plot(history.history['loss'], label="Truncated Normal init")
plt.legend();
Train on 1527 samples
Epoch 1/15
1527/1527 [==============================] - 0s 179us/sample - loss: 2.3029 - accuracy: 0.1022
Epoch 2/15
1527/1527 [==============================] - 0s 48us/sample - loss: 2.2998 - accuracy: 0.1356
Epoch 3/15
1527/1527 [==============================] - 0s 169us/sample - loss: 2.2866 - accuracy: 0.2462
Epoch 4/15
1527/1527 [==============================] - 0s 58us/sample - loss: 2.0631 - accuracy: 0.4289
Epoch 5/15
1527/1527 [==============================] - 0s 54us/sample - loss: 1.3023 - accuracy: 0.6045
Epoch 6/15
1527/1527 [==============================] - 0s 71us/sample - loss: 0.8094 - accuracy: 0.7649
Epoch 7/15
1527/1527 [==============================] - 0s 85us/sample - loss: 0.6261 - accuracy: 0.8094
Epoch 8/15
1527/1527 [==============================] - 0s 92us/sample - loss: 0.5034 - accuracy: 0.8566
Epoch 9/15
1527/1527 [==============================] - 0s 104us/sample - loss: 0.3963 - accuracy: 0.8893
Epoch 10/15
1527/1527 [==============================] - 0s 103us/sample - loss: 0.3246 - accuracy: 0.9096
Epoch 11/15
1527/1527 [==============================] - 0s 100us/sample - loss: 0.2788 - accuracy: 0.9227
Epoch 12/15
1527/1527 [==============================] - 0s 102us/sample - loss: 0.2421 - accuracy: 0.9306
Epoch 13/15
1527/1527 [==============================] - 0s 104us/sample - loss: 0.2144 - accuracy: 0.9417
Epoch 14/15
1527/1527 [==============================] - 0s 102us/sample - loss: 0.1908 - accuracy: 0.9470
Epoch 15/15
1527/1527 [==============================] - 0s 100us/sample - loss: 0.1735 - accuracy: 0.9515
训练好后，模型参数发生变化
model.layers[0].weights
[,
]
还有其它实验做
- 更改初始化方法，查看SGD是否还能很好收敛：
- 很小的scale
stddev=1e-4 - 很大的scale
stddev=1或10 - 把所有值初始化成零 （constant initialization)
- 很小的scale
- 对以上内容的结果做分析
- 试验其它更好的优化算法比如SGD with momentum,或者Adam等能否解决不合适初始化
from tensorflow.keras import optimizers
large_scale_init = initializers.TruncatedNormal(stddev=1)
small_scale_init = initializers.TruncatedNormal(stddev=1e-3)
optimizer_list = [
('SGD', optimizers.SGD(lr=0.1)),
('RMSprop', optimizers.RMSprop()),
('Adadelta', optimizers.Adadelta(learning_rate=0.1)),
('Adagrad',optimizers.Adagrad(learning_rate=0.1)),
('Adamax',optimizers.Adamax()),
('Adam', optimizers.Adam()),
('Nadam', optimizers.Nadam()),
('SGD + Nesterov momentum', optimizers.SGD(
lr=0.1, momentum=0.9, nesterov=True)),
]
init_list = [
('glorot uniform init', 'glorot_uniform', '-'),
('glorot unormal init', 'glorot_normal', '-'),
('he uniform init', 'he_uniform', '-.'),
('he unormal init', 'he_normal', '-.'),
('lecun uniform init', 'lecun_uniform', ':'),
('lecun unormal init', 'lecun_normal', ':'),
('small init scale', small_scale_init, '-'),
('large init scale', large_scale_init, '-'),
('zero init', 'zero', '--'),
]
for optimizer_name, optimizer in optimizer_list:
print("Fitting with:", optimizer_name)
plt.figure(figsize=(12, 6))
for init_name, init, linestyle in init_list:
model = Sequential()
model.add(Dense(hidden_dim, input_dim=input_dim, activation="tanh",
kernel_initializer=init))
model.add(Dense(hidden_dim, activation="tanh",
kernel_initializer=init))
model.add(Dense(output_dim, activation="softmax",
kernel_initializer=init))
model.compile(optimizer=optimizer,
loss='categorical_crossentropy')
history = model.fit(X_train, Y_train,
epochs=10, batch_size=32, verbose=0)
plt.plot(history.history['loss'], linestyle=linestyle,
label=init_name)
plt.xlabel('# epochs')
plt.ylabel('Training loss')
plt.ylim(0, 6)
plt.legend(loc='best');
plt.title('Impact of initialization on convergence with %s'
% optimizer_name)
Fitting with: SGD
Fitting with: RMSprop
Fitting with: Adadelta
Fitting with: Adagrad
Fitting with: Adamax
Fitting with: Adam
Fitting with: Nadam
Fitting with: SGD + Nesterov momentum
分析：
-
初始化是全零时，不论输入是什么，输出都是零，所以所求梯度也是零，无论什么优化算法，都是不会有改变，损失值是个常量。
-
权值中有null时，优化值不是在局部最小也不是局部最大，而是是鞍点处。
-
对于神经网络，当权重的随机初始化的范围太小，SGD很难避免低梯度区域。加动量可以提供帮助，但是特别是对于深层网络而言，可能需要花费很多时间来逃避该区域。用较大的随机值初始化权重将使输出（softmax）非常尖锐：网络对其预测值非常“自信”，即使它们是完全随机的，这导致很高初始损失值
-
Glorot uniform(he uniform,lecun uniform)初始化使用的scale取决于权重矩阵的尺寸，可以获得激活值的范数，使模型的学习成为可能。
-
Adam 对每个参数会计算各自的更新值，所以初始化不好也问题不大，但好的初始化会带来帮助。
所以好的模型要有以下几点： -
确保有合适的初始化值
-
检查每一层的参数来避免bad layer
-
使用Adam而不是SGD
ch_size=32, verbose=0)
plt.plot(history.history[‘loss’], linestyle=linestyle,
label=init_name)plt.xlabel(’# epochs’)
plt.ylabel(‘Training loss’)
plt.ylim(0, 6)
plt.legend(loc=‘best’);
plt.title(‘Impact of initialization on convergence with %s’
% optimizer_name)
Fitting with: SGD
Fitting with: RMSprop
Fitting with: Adadelta
Fitting with: Adagrad
Fitting with: Adamax
Fitting with: Adam
Fitting with: Nadam
Fitting with: SGD + Nesterov momentum
分析：
- 初始化是全零时，不论输入是什么，输出都是零，所以所求梯度也是零，无论什么优化算法，都是不会有改变，损失值是个常量。
- 权值中有null时，优化值不是在局部最小也不是局部最大，而是是鞍点处。
- 对于神经网络，当权重的随机初始化的范围太小，SGD很难避免低梯度区域。加动量可以提供帮助，但是特别是对于深层网络而言，可能需要花费很多时间来逃避该区域。用较大的随机值初始化权重将使输出（softmax）非常尖锐：网络对其预测值非常“自信”，即使它们是完全随机的，这导致很高初始损失值
- Glorot uniform(he uniform,lecun uniform)初始化使用的scale取决于权重矩阵的尺寸，可以获得激活值的范数，使模型的学习成为可能。
- Adam 对每个参数会计算各自的更新值，所以初始化不好也问题不大，但好的初始化会带来帮助。
所以好的模型要有以下几点：
- 确保有合适的初始化值
- 检查每一层的参数来避免bad layer
- 使用Adam而不是SGD