【人工智能項目】LSTM實現數據預測分類實驗

本次主要對csv文件中采集到的數據來區分樹的品種實驗，通過不同列的數據，送入lstm模型中，得到預測結果。

導包

# 導包 import numpy as np import pandas as pd import glob import os import tensorflow as tf from sklearn.model_selection import train_test_split from sklearn.utils import shuffle import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline train_path = "./" print(os.listdir(train_path)) ['.ipynb_checkpoints', 'code.ipynb', 'data.csv', 'lstm.h5', 'plant_totaldatat.xlsx']

讀取數據

# 讀取文件 data = pd.read_csv("data.csv") data Filename172.538173.141173.744174.348174.951175.554176.157176.76177.363...1165.8461166.3731166.91167.4271167.9541168.4811169.0081169.5351170.061Label

0	芭蕉0001.ROH	414.421417	445.234558	482.571625	378.288757	483.976776	476.850617	423.253845	445.033813	477.653564	...	487.088196	513.986938	532.956604	545.502625	504.853424	568.687744	547.811096	584.947449	564.773376	1
1	芭蕉0002.ROH	469.523712	450.353333	447.543030	457.880981	467.616699	456.375458	483.575287	447.543030	415.224365	...	560.357178	511.477722	473.337708	613.151001	513.384766	495.418793	618.771606	618.570923	495.619507	1
2	芭蕉0003.ROH	508.265930	502.946411	522.317505	471.932556	512.682129	503.950104	498.429840	487.891144	465.910461	...	597.694275	552.126953	540.885681	661.327881	553.030273	540.183106	650.889526	659.521240	550.219971	1
3	芭蕉0004.ROH	490.801819	514.789917	529.945557	463.501617	527.536682	525.027466	489.898499	514.288025	503.247528	...	567.784424	576.416138	573.906921	625.596680	548.915161	621.280823	632.221008	652.595825	599.199768	1
4	芭蕉0005.ROH	431.383697	433.290680	436.703217	408.901154	459.386505	461.694977	453.264008	435.900269	438.810974	...	535.867249	499.232788	503.849731	569.691467	518.704285	512.381043	577.921692	573.605835	520.812012	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2422	樟樹10096.ROH	376.682861	396.656189	391.637787	371.363342	434.796234	390.634094	378.489502	410.406677	394.147003	...	478.155395	441.520904	413.317383	508.265930	447.844116	424.558624	497.927978	522.618652	449.951874	7
2423	樟樹10097.ROH	312.647797	359.419495	336.836578	315.056641	381.299835	351.891876	333.925903	377.586182	353.397400	...	434.495117	420.242798	342.758331	476.047668	397.258423	369.054871	446.639709	460.390167	384.913086	7
2424	樟樹10098.ROH	383.809052	372.166290	419.941681	371.363342	412.112946	411.912201	399.566894	382.905731	405.287903	...	438.208740	460.089081	427.469330	478.556885	463.300873	468.620392	485.181183	525.328613	500.035736	7
2425	樟樹10099.ROH	327.100861	333.725159	347.676392	332.621124	376.181030	364.538300	361.727966	377.786926	347.274902	...	417.934326	411.410370	377.184723	433.190338	413.919586	395.752899	432.989593	445.636017	425.562317	7
2426	樟樹10100.ROH	380.697601	424.859741	441.119446	388.526367	448.446320	433.089966	416.428803	431.383697	450.654449	...	500.838684	497.526520	458.182068	537.673889	493.210663	465.709717	551.625122	561.059753	480.263153	7

2427 rows × 1757 columns

# 查看前5行數據 data.head() Filename172.538173.141173.744174.348174.951175.554176.157176.76177.363...1165.8461166.3731166.91167.4271167.9541168.4811169.0081169.5351170.061Label

0	芭蕉0001.ROH	414.421417	445.234558	482.571625	378.288757	483.976776	476.850617	423.253845	445.033813	477.653564	...	487.088196	513.986938	532.956604	545.502625	504.853424	568.687744	547.811096	584.947449	564.773376	1
1	芭蕉0002.ROH	469.523712	450.353333	447.543030	457.880981	467.616699	456.375458	483.575287	447.543030	415.224365	...	560.357178	511.477722	473.337708	613.151001	513.384766	495.418793	618.771606	618.570923	495.619507	1
2	芭蕉0003.ROH	508.265930	502.946411	522.317505	471.932556	512.682129	503.950104	498.429840	487.891144	465.910461	...	597.694275	552.126953	540.885681	661.327881	553.030273	540.183106	650.889526	659.521240	550.219971	1
3	芭蕉0004.ROH	490.801819	514.789917	529.945557	463.501617	527.536682	525.027466	489.898499	514.288025	503.247528	...	567.784424	576.416138	573.906921	625.596680	548.915161	621.280823	632.221008	652.595825	599.199768	1
4	芭蕉0005.ROH	431.383697	433.290680	436.703217	408.901154	459.386505	461.694977	453.264008	435.900269	438.810974	...	535.867249	499.232788	503.849731	569.691467	518.704285	512.381043	577.921692	573.605835	520.812012	1

5 rows × 1757 columns

數據分析

data.index RangeIndex(start=0, stop=2427, step=1) print(data.info()) <class 'pandas.core.frame.DataFrame'> RangeIndex: 2427 entries, 0 to 2426 Columns: 1757 entries, Filename to Label dtypes: float64(1755), int64(1), object(1) memory usage: 32.5+ MB None # 去除缺失數據 data.dropna(axis=0, how='any', inplace=True) data Filename172.538173.141173.744174.348174.951175.554176.157176.76177.363...1165.8461166.3731166.91167.4271167.9541168.4811169.0081169.5351170.061Label

0	芭蕉0001.ROH	414.421417	445.234558	482.571625	378.288757	483.976776	476.850617	423.253845	445.033813	477.653564	...	487.088196	513.986938	532.956604	545.502625	504.853424	568.687744	547.811096	584.947449	564.773376	1
1	芭蕉0002.ROH	469.523712	450.353333	447.543030	457.880981	467.616699	456.375458	483.575287	447.543030	415.224365	...	560.357178	511.477722	473.337708	613.151001	513.384766	495.418793	618.771606	618.570923	495.619507	1
2	芭蕉0003.ROH	508.265930	502.946411	522.317505	471.932556	512.682129	503.950104	498.429840	487.891144	465.910461	...	597.694275	552.126953	540.885681	661.327881	553.030273	540.183106	650.889526	659.521240	550.219971	1
3	芭蕉0004.ROH	490.801819	514.789917	529.945557	463.501617	527.536682	525.027466	489.898499	514.288025	503.247528	...	567.784424	576.416138	573.906921	625.596680	548.915161	621.280823	632.221008	652.595825	599.199768	1
4	芭蕉0005.ROH	431.383697	433.290680	436.703217	408.901154	459.386505	461.694977	453.264008	435.900269	438.810974	...	535.867249	499.232788	503.849731	569.691467	518.704285	512.381043	577.921692	573.605835	520.812012	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2422	樟樹10096.ROH	376.682861	396.656189	391.637787	371.363342	434.796234	390.634094	378.489502	410.406677	394.147003	...	478.155395	441.520904	413.317383	508.265930	447.844116	424.558624	497.927978	522.618652	449.951874	7
2423	樟樹10097.ROH	312.647797	359.419495	336.836578	315.056641	381.299835	351.891876	333.925903	377.586182	353.397400	...	434.495117	420.242798	342.758331	476.047668	397.258423	369.054871	446.639709	460.390167	384.913086	7
2424	樟樹10098.ROH	383.809052	372.166290	419.941681	371.363342	412.112946	411.912201	399.566894	382.905731	405.287903	...	438.208740	460.089081	427.469330	478.556885	463.300873	468.620392	485.181183	525.328613	500.035736	7
2425	樟樹10099.ROH	327.100861	333.725159	347.676392	332.621124	376.181030	364.538300	361.727966	377.786926	347.274902	...	417.934326	411.410370	377.184723	433.190338	413.919586	395.752899	432.989593	445.636017	425.562317	7
2426	樟樹10100.ROH	380.697601	424.859741	441.119446	388.526367	448.446320	433.089966	416.428803	431.383697	450.654449	...	500.838684	497.526520	458.182068	537.673889	493.210663	465.709717	551.625122	561.059753	480.263153	7

2427 rows × 1757 columns

# 刪除第一列數據 data = data.drop(['Filename'], axis=1) data 172.538173.141173.744174.348174.951175.554176.157176.76177.363177.966...1165.8461166.3731166.91167.4271167.9541168.4811169.0081169.5351170.061Label

0	414.421417	445.234558	482.571625	378.288757	483.976776	476.850617	423.253845	445.033813	477.653564	595.285400	...	487.088196	513.986938	532.956604	545.502625	504.853424	568.687744	547.811096	584.947449	564.773376	1
1	469.523712	450.353333	447.543030	457.880981	467.616699	456.375458	483.575287	447.543030	415.224365	601.006409	...	560.357178	511.477722	473.337708	613.151001	513.384766	495.418793	618.771606	618.570923	495.619507	1
2	508.265930	502.946411	522.317505	471.932556	512.682129	503.950104	498.429840	487.891144	465.910461	655.907959	...	597.694275	552.126953	540.885681	661.327881	553.030273	540.183106	650.889526	659.521240	550.219971	1
3	490.801819	514.789917	529.945557	463.501617	527.536682	525.027466	489.898499	514.288025	503.247528	661.628967	...	567.784424	576.416138	573.906921	625.596680	548.915161	621.280823	632.221008	652.595825	599.199768	1
4	431.383697	433.290680	436.703217	408.901154	459.386505	461.694977	453.264008	435.900269	438.810974	592.675842	...	535.867249	499.232788	503.849731	569.691467	518.704285	512.381043	577.921692	573.605835	520.812012	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2422	376.682861	396.656189	391.637787	371.363342	434.796234	390.634094	378.489502	410.406677	394.147003	510.775147	...	478.155395	441.520904	413.317383	508.265930	447.844116	424.558624	497.927978	522.618652	449.951874	7
2423	312.647797	359.419495	336.836578	315.056641	381.299835	351.891876	333.925903	377.586182	353.397400	424.960113	...	434.495117	420.242798	342.758331	476.047668	397.258423	369.054871	446.639709	460.390167	384.913086	7
2424	383.809052	372.166290	419.941681	371.363342	412.112946	411.912201	399.566894	382.905731	405.287903	531.149963	...	438.208740	460.089081	427.469330	478.556885	463.300873	468.620392	485.181183	525.328613	500.035736	7
2425	327.100861	333.725159	347.676392	332.621124	376.181030	364.538300	361.727966	377.786926	347.274902	443.227173	...	417.934326	411.410370	377.184723	433.190338	413.919586	395.752899	432.989593	445.636017	425.562317	7
2426	380.697601	424.859741	441.119446	388.526367	448.446320	433.089966	416.428803	431.383697	450.654449	533.458435	...	500.838684	497.526520	458.182068	537.673889	493.210663	465.709717	551.625122	561.059753	480.263153	7

2427 rows × 1756 columns

# 樣本分布 # 以圖方式表示 sns.countplot(data["Label"]) plt.xlabel("Label") plt.title("Number of messages") Text(0.5, 1.0, 'Number of messages')

# 重新排序 df = data.sample(frac=1).reset_index(drop=True) df 172.538173.141173.744174.348174.951175.554176.157176.76177.363177.966...1165.8461166.3731166.91167.4271167.9541168.4811169.0081169.5351170.061Label

0	429.978546	448.345978	447.342285	430.380005	473.839569	442.323853	457.178406	452.862549	429.175598	582.739380	...	494.415100	501.942718	467.616699	526.934509	482.170136	509.470367	526.432617	595.385803	530.246643	6
1	281.834656	293.979248	335.431427	288.057526	310.238953	317.365112	305.822723	321.179108	327.502319	398.462830	...	329.911163	375.578827	351.691132	373.270355	349.984863	390.132263	369.255615	398.061371	381.199463	7
2	440.316498	426.164520	453.665497	430.480377	450.052216	461.594605	456.174713	440.517212	444.130493	607.429993	...	515.291748	490.400360	489.998871	569.992554	481.567932	517.198731	559.353516	549.617737	548.614075	3
3	285.247192	309.737091	289.362305	302.008728	340.750977	327.000488	323.688324	345.769379	316.963623	418.034698	...	338.442474	373.169983	362.530914	384.511627	340.750977	376.983978	375.578827	407.997833	391.236298	1
4	458.081696	475.345093	447.743744	437.606537	476.950989	457.278748	469.523712	437.305420	436.803589	594.683228	...	486.887451	487.991516	502.544952	553.532104	483.374573	506.158203	554.033997	552.327698	540.785339	4
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2422	441.822022	464.706024	461.996063	427.368958	481.266815	490.299988	465.207855	473.939911	446.037476	610.240295	...	523.421570	524.927124	519.406860	577.720947	499.232788	537.473144	581.434570	584.646362	551.625122	2
2423	290.767456	264.270203	298.395477	291.670776	303.915741	331.416687	325.394592	316.762909	328.204895	404.384583	...	352.795166	357.010651	372.969238	393.544769	368.553040	391.236298	399.767639	412.915894	403.180145	4
2424	303.915741	286.652344	305.722382	293.176300	329.810791	303.815369	317.967316	328.907471	294.179993	419.239105	...	344.866058	354.802551	357.512512	400.972046	342.557587	383.006103	390.634094	388.024506	372.668152	1
2425	428.473022	434.394745	487.991516	408.098206	464.003449	489.396667	456.174713	431.684814	458.583557	590.066223	...	528.038513	561.059753	473.237335	578.624268	537.673889	496.221741	553.732849	616.162048	509.169250	2
2426	454.468445	441.621277	450.754822	424.357910	459.286133	460.289825	469.222595	459.787964	443.729004	592.274353	...	494.916931	484.779724	499.533875	555.840576	474.441772	526.231873	546.707092	568.988892	528.239258	4

2427 rows × 1756 columns

# 空值檢查 df[df.isnull().values==True] 172.538173.141173.744174.348174.951175.554176.157176.76177.363177.966...1165.8461166.3731166.91167.4271167.9541168.4811169.0081169.5351170.061Label

0 rows × 1756 columns

# 得到x和y x = df.iloc[:,:-1] y = df.iloc[:,-1]

劃分數據集

# 劃分數據集 from sklearn.model_selection import train_test_split x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2) # 數據歸一化處理 from sklearn.preprocessing import StandardScalerscaler = StandardScaler() scaler.fit(x_train)X_train = scaler.transform(x_train) X_test = scaler.transform(x_test) print(X_train.shape) print(y_train.shape) print(X_test.shape) print(y_test.shape) (1941, 1755) (1941,) (486, 1755) (486,) # 對數據處理 from keras.utils import np_utilsX_train = X_train.reshape((-1,1,1755)) Y_train = np_utils.to_categorical(y_train) X_test = X_test.reshape((-1,1,1755)) Y_test = np_utils.to_categorical(y_test) print(X_train.shape) print(Y_train.shape) print(X_test.shape) print(Y_test.shape) (1941, 1, 1755) (1941, 8) (486, 1, 1755) (486, 8)

模型

from keras import Sequential from keras.layers import LSTM,Activation,Dense,Dropout,Input,Embedding,BatchNormalization,Add,concatenate,Flattenmodel = Sequential()model.add(LSTM(units=50,return_sequences=True,input_shape=(1,1755))) model.add(Dropout(0.2))model.add(LSTM(units=50,return_sequences=True)) model.add(Dropout(0.2))model.add(LSTM(units=50,return_sequences=True)) model.add(Dropout(0.2))# model.add(LSTM(units=50,return_sequences=True)) # model.add(Dropout(0.2))model.add(LSTM(units=50)) model.add(Dropout(0.2))# model.add(Dense(units=256)) # model.add(Dropout(0.2)) model.add(Dense(units=128)) model.add(Dropout(0.2)) model.add(Dense(units=64)) model.add(Dropout(0.2)) model.add(Dense(units=16)) model.add(Dropout(0.2))model.add(Dense(units=8,activation="softmax")) # Implement Learning rate decay from keras.callbacks import EarlyStopping,ReduceLROnPlateau,ModelCheckpoint,LearningRateSchedulercheckpoint = ModelCheckpoint("lstm.h5",monitor="val_loss",mode="min",save_best_only = True,verbose=1)earlystop = EarlyStopping(monitor = 'val_loss', min_delta = 0, patience = 5,verbose = 1,restore_best_weights = True)reduce_lr = ReduceLROnPlateau(monitor = 'val_loss',factor = 0.2,patience = 3,verbose = 1)#min_delta = 0.00001)callbacks = [earlystop, checkpoint, reduce_lr] model.compile(optimizer="adam", loss='categorical_crossentropy', metrics=['accuracy']) history_fit = model.fit(x=X_train, y=Y_train, batch_size=8, epochs=30, verbose=1, validation_data=(X_test, Y_test),callbacks=callbacks) Train on 1941 samples, validate on 486 samples Epoch 1/30 1941/1941 [==============================] - 6s 3ms/step - loss: 1.0300 - accuracy: 0.6188 - val_loss: 0.5473 - val_accuracy: 0.8313Epoch 00001: val_loss improved from inf to 0.54729, saving model to lstm.h5 Epoch 2/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.6064 - accuracy: 0.7836 - val_loss: 0.3829 - val_accuracy: 0.8374Epoch 00002: val_loss improved from 0.54729 to 0.38287, saving model to lstm.h5 Epoch 3/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.4797 - accuracy: 0.8089 - val_loss: 0.3595 - val_accuracy: 0.8272Epoch 00003: val_loss improved from 0.38287 to 0.35947, saving model to lstm.h5 Epoch 4/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.4672 - accuracy: 0.8083 - val_loss: 0.2970 - val_accuracy: 0.8354Epoch 00004: val_loss improved from 0.35947 to 0.29702, saving model to lstm.h5 Epoch 5/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.3946 - accuracy: 0.8557 - val_loss: 0.2658 - val_accuracy: 0.9033Epoch 00005: val_loss improved from 0.29702 to 0.26579, saving model to lstm.h5 Epoch 6/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.3519 - accuracy: 0.8712 - val_loss: 0.2217 - val_accuracy: 0.8909Epoch 00006: val_loss improved from 0.26579 to 0.22171, saving model to lstm.h5 Epoch 7/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.3287 - accuracy: 0.8743 - val_loss: 0.2439 - val_accuracy: 0.8683Epoch 00007: val_loss did not improve from 0.22171 Epoch 8/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.3400 - accuracy: 0.8635 - val_loss: 0.2036 - val_accuracy: 0.9259Epoch 00008: val_loss improved from 0.22171 to 0.20360, saving model to lstm.h5 Epoch 9/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.3541 - accuracy: 0.8666 - val_loss: 0.2087 - val_accuracy: 0.9321Epoch 00009: val_loss did not improve from 0.20360 Epoch 10/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.3227 - accuracy: 0.8691 - val_loss: 0.2141 - val_accuracy: 0.9362Epoch 00010: val_loss did not improve from 0.20360 Epoch 11/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.2842 - accuracy: 0.8851 - val_loss: 0.1821 - val_accuracy: 0.9506Epoch 00011: val_loss improved from 0.20360 to 0.18205, saving model to lstm.h5 Epoch 12/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.3343 - accuracy: 0.8712 - val_loss: 0.2297 - val_accuracy: 0.8951Epoch 00012: val_loss did not improve from 0.18205 Epoch 13/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.3082 - accuracy: 0.8800 - val_loss: 0.2213 - val_accuracy: 0.9321Epoch 00013: val_loss did not improve from 0.18205 Epoch 14/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.2550 - accuracy: 0.9052 - val_loss: 0.1765 - val_accuracy: 0.9444Epoch 00014: val_loss improved from 0.18205 to 0.17651, saving model to lstm.h5 Epoch 15/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.3290 - accuracy: 0.8856 - val_loss: 0.2044 - val_accuracy: 0.9383Epoch 00015: val_loss did not improve from 0.17651 Epoch 16/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.2812 - accuracy: 0.9031 - val_loss: 0.1578 - val_accuracy: 0.9465Epoch 00016: val_loss improved from 0.17651 to 0.15778, saving model to lstm.h5 Epoch 17/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.2332 - accuracy: 0.9145 - val_loss: 0.1287 - val_accuracy: 0.9547Epoch 00017: val_loss improved from 0.15778 to 0.12870, saving model to lstm.h5 Epoch 18/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.2597 - accuracy: 0.9114 - val_loss: 0.1607 - val_accuracy: 0.9280Epoch 00018: val_loss did not improve from 0.12870 Epoch 19/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.2570 - accuracy: 0.9052 - val_loss: 0.1230 - val_accuracy: 0.9671Epoch 00019: val_loss improved from 0.12870 to 0.12305, saving model to lstm.h5 Epoch 20/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.2401 - accuracy: 0.9129 - val_loss: 0.1639 - val_accuracy: 0.9588Epoch 00020: val_loss did not improve from 0.12305 Epoch 21/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.2233 - accuracy: 0.9155 - val_loss: 0.1172 - val_accuracy: 0.9671Epoch 00021: val_loss improved from 0.12305 to 0.11718, saving model to lstm.h5 Epoch 22/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.2524 - accuracy: 0.9088 - val_loss: 0.1627 - val_accuracy: 0.9588Epoch 00022: val_loss did not improve from 0.11718 Epoch 23/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.2185 - accuracy: 0.9176 - val_loss: 0.1313 - val_accuracy: 0.9342Epoch 00023: val_loss did not improve from 0.11718 Epoch 24/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.2344 - accuracy: 0.9160 - val_loss: 0.1223 - val_accuracy: 0.9527Epoch 00024: val_loss did not improve from 0.11718Epoch 00024: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026. Epoch 25/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.1890 - accuracy: 0.9274 - val_loss: 0.0862 - val_accuracy: 0.9691Epoch 00025: val_loss improved from 0.11718 to 0.08617, saving model to lstm.h5 Epoch 26/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.1475 - accuracy: 0.9361 - val_loss: 0.0794 - val_accuracy: 0.9733Epoch 00026: val_loss improved from 0.08617 to 0.07940, saving model to lstm.h5 Epoch 27/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.1507 - accuracy: 0.9392 - val_loss: 0.0673 - val_accuracy: 0.9774Epoch 00027: val_loss improved from 0.07940 to 0.06732, saving model to lstm.h5 Epoch 28/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.1498 - accuracy: 0.9444 - val_loss: 0.0764 - val_accuracy: 0.9733Epoch 00028: val_loss did not improve from 0.06732 Epoch 29/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.1513 - accuracy: 0.9423 - val_loss: 0.0733 - val_accuracy: 0.9774Epoch 00029: val_loss did not improve from 0.06732 Epoch 30/30 1941/1941 [==============================] - 4s 2ms/step - loss: 0.1338 - accuracy: 0.9418 - val_loss: 0.0815 - val_accuracy: 0.9753Epoch 00030: val_loss did not improve from 0.06732Epoch 00030: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05. # 畫曲線 def plot_performance(history=None,figure_directory=None,ylim_pad=[0,0]):xlabel="Epoch"legends=["Training","Validation"]plt.figure(figsize=(20,5))y1=history.history["accuracy"]y2=history.history["val_accuracy"]min_y=min(min(y1),min(y2))-ylim_pad[0]max_y=max(max(y1),max(y2))+ylim_pad[0]plt.subplot(121)plt.plot(y1)plt.plot(y2)plt.title("Model Accuracy\n",fontsize=17)plt.xlabel(xlabel,fontsize=15)plt.ylabel("Accuracy",fontsize=15)plt.ylim(min_y,max_y)plt.legend(legends,loc="upper left")plt.grid()y1=history.history["loss"]y2=history.history["val_loss"]min_y=min(min(y1),min(y2))-ylim_pad[1]max_y=max(max(y1),max(y2))+ylim_pad[1]plt.subplot(122)plt.plot(y1)plt.plot(y2)plt.title("Model Loss:\n",fontsize=17)plt.xlabel(xlabel,fontsize=15)plt.ylabel("Loss",fontsize=15)plt.ylim(min_y,max_y)plt.legend(legends,loc="upper left")plt.grid()plt.show() # 可視化 plot_performance(history=history_fit)

# 預測 predict_y = model.predict_classes(X_test) predict_y array([1, 6, 4, 3, 4, 1, 4, 6, 6, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 5, 4, 5,7, 1, 4, 5, 3, 4, 1, 6, 4, 4, 5, 4, 1, 1, 7, 4, 1, 4, 6, 4, 4, 5,4, 7, 7, 6, 1, 1, 5, 6, 2, 1, 4, 4, 1, 4, 4, 4, 6, 5, 2, 6, 3, 1,2, 4, 2, 4, 1, 1, 1, 1, 1, 1, 6, 4, 1, 3, 5, 2, 4, 6, 3, 4, 4, 3,6, 5, 7, 1, 1, 2, 7, 4, 1, 6, 6, 2, 6, 1, 3, 4, 1, 1, 1, 4, 2, 1,3, 6, 2, 4, 4, 4, 3, 4, 1, 1, 6, 7, 6, 7, 2, 5, 1, 3, 4, 1, 3, 3,5, 4, 4, 7, 6, 2, 6, 4, 6, 6, 3, 5, 3, 5, 6, 3, 4, 1, 3, 6, 1, 4,6, 4, 6, 2, 2, 1, 7, 4, 6, 3, 6, 6, 5, 4, 4, 4, 4, 2, 4, 6, 1, 3,1, 6, 6, 4, 1, 1, 4, 1, 4, 4, 2, 3, 1, 6, 4, 4, 3, 6, 5, 3, 4, 6,1, 1, 3, 5, 4, 1, 6, 3, 4, 3, 1, 2, 1, 4, 6, 5, 3, 5, 4, 4, 4, 4,7, 3, 1, 4, 2, 4, 6, 7, 4, 1, 4, 3, 1, 4, 1, 5, 2, 5, 3, 4, 1, 2,4, 5, 1, 4, 4, 6, 3, 1, 4, 4, 5, 5, 6, 4, 3, 3, 1, 4, 5, 1, 1, 2,3, 1, 1, 6, 7, 6, 4, 6, 1, 3, 4, 1, 4, 2, 7, 4, 5, 1, 4, 2, 1, 7,3, 6, 4, 4, 1, 7, 1, 5, 4, 4, 1, 4, 4, 1, 1, 4, 1, 1, 3, 6, 3, 3,6, 5, 4, 3, 1, 2, 6, 6, 6, 4, 2, 2, 3, 1, 5, 1, 4, 1, 7, 3, 1, 1,3, 5, 6, 2, 4, 1, 1, 6, 1, 6, 6, 6, 7, 1, 5, 4, 2, 7, 1, 6, 3, 1,4, 5, 2, 1, 4, 5, 6, 3, 1, 5, 1, 6, 3, 1, 3, 6, 6, 5, 1, 6, 4, 1,7, 3, 4, 3, 7, 3, 6, 1, 5, 3, 4, 2, 4, 5, 4, 1, 1, 4, 6, 3, 6, 5,4, 6, 1, 6, 3, 1, 4, 4, 3, 1, 5, 6, 6, 3, 5, 3, 5, 2, 1, 3, 2, 4,1, 4, 1, 3, 7, 6, 3, 4, 4, 1, 4, 2, 1, 4, 4, 2, 1, 3, 1, 3, 4, 7,4, 4, 1, 1, 1, 1, 4, 4, 1, 4, 5, 6, 5, 3, 3, 1, 4, 3, 2, 2, 6, 4,4, 3, 2, 2, 1, 6, 3, 1, 3, 1, 6, 7, 4, 4, 4, 1, 1, 4, 3, 1, 4, 5,4, 3], dtype=int64) from sklearn.metrics import accuracy_score,f1_score,confusion_matrix,classification_reportprint(classification_report(y_test,predict_y)) precision recall f1-score support1 1.00 1.00 1.00 1172 0.97 1.00 0.99 363 0.99 0.88 0.93 754 0.92 0.99 0.95 1175 1.00 1.00 1.00 436 1.00 0.99 0.99 737 1.00 0.96 0.98 25accuracy 0.98 486macro avg 0.98 0.97 0.98 486 weighted avg 0.98 0.98 0.98 486

小結

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總結

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