├── LICENSE ├── LSTM with multi variables.ipynb ├── README.md ├── auto_analyzer.py ├── auto_analyzer_rand.py ├── is_future_purchase.ipynb ├── purchacedata_base.csv ├── tokyo-weather-2003-2012.csv └── 転移学習テスト.ipynb /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # auto_analyzer.py 2 | - コマンドラインでデータセットのファイル名などと共に呼び出すことで、自動で分析を始めて完成したモデルを保存します。 3 | - 保存したモデルを使って予測を行い、結果を別のファイルに出力します。 4 | - 自動でバッチサイズ、エポック、ニューロン数、レイヤー数、ラーニングレートを変えてチューニングします。 5 | 6 | ## auto_analyzer_rand.py 7 | パラメタの組み合わせを総当りでなくランダムで学習します。学習に時間がかかりすぎる場合にお試しください。 8 | 9 | ## 準備するもの 10 | - 1行目にヘッダーを付けたデータセット 11 | 12 | 　　・ CSV形式を推奨 13 | 14 | 　　・ 欠損値がある行は自動で除外されます 15 | 16 | 　　・ 外れ値は修正しておいてください 17 | 18 | 　　・ 予測データのカテゴリ値の種類は、学習時と同じ数だけ入れてください 19 | 20 | - auto_analyzer.pyとデータセットを同じディレクトリに設置 21 | - 格納ディレクトリに書き込み権限を付与 22 | 23 | ## 使い方 24 | 1.コマンドラインを開く 25 | 26 | 2.データセットと本コードが格納されているディレクトリへ移動 27 | 28 | 3.「python auto_analyzer.py + パラメタ値」で呼び出す 29 | 30 | ### 呼び出し例 31 | python auto_analyzer.py --mode create --input_file xxx.csv --method regression --model_file test --definition str,int,int 32 | 33 | ## パラメタ説明 34 | ### --mode（必須） 35 | create：新しくモデルを作成する 36 | 37 | predict：作ったモデルで予測する 38 | 39 | ### --input_file（必須） 40 | データセットの入ったファイル名を入れる 41 | 42 | ### --method（必須） 43 | binary：二値分類 44 | 45 | multiple：多値分類 46 | 47 | regression：回帰 48 | 49 | ### --output_file（非必須） 50 | 予測時に結果を格納するファイル名を入れる 51 | 52 | ### --model_file（非必須） 53 | モデルを保存/読み込む際に参照 54 | 55 | ### --definition（非必須） 56 | データ型が自動認識で問題がある場合に入力してください。 57 | 全カラム分のデータ型を[str, int, float]の中から選び、「,」カンマ区切りで入れてください。 58 | 59 | ## 動作環境 60 | - python 3.5.0 61 | 62 | - keras 2.0.3 63 | 64 | -------------------------------------------------------------------------------- /auto_analyzer.py: -------------------------------------------------------------------------------- 1 | import numpy 2 | import pandas 3 | from keras.models import Sequential 4 | from keras.layers import Dense 5 | from keras import optimizers 6 | from keras.wrappers.scikit_learn import KerasRegressor 7 | from keras.wrappers.scikit_learn import KerasClassifier 8 | from sklearn.model_selection import cross_val_score 9 | from sklearn.model_selection import KFold 10 | from sklearn.grid_search import GridSearchCV 11 | from sklearn.preprocessing import StandardScaler 12 | from sklearn.pipeline import Pipeline 13 | from keras.models import load_model 14 | import os 15 | import argparse 16 | 17 | #---------------------------- 18 | # get command line variables 19 | #---------------------------- 20 | parser = argparse.ArgumentParser(description='Make models by keras. Place Y on the head column in the cleaned dataset with header names on the top row. Rows containing null values will be deleated.') 21 | parser.add_argument('--mode', choices=['create', 'predict'], dest='mode', metavar='create/predict', type=str, nargs='+', required=True, 22 | help='an integer for the accumulator') 23 | parser.add_argument('--input_file', dest='input_file', type=str, nargs='+', required=True, 24 | help='path to dataset or model') 25 | parser.add_argument('--method', choices=['binary', 'multiple', 'regression'], metavar='binary/multiple/regression', dest='method', type=str, nargs='+', required=True, 26 | help='Model type you solve') 27 | parser.add_argument('--output_file', dest='output_file', default=False, required=False, 28 | help='If you input output_file it will save result as directed path.') 29 | parser.add_argument('--model_file', dest='model_file', default=False, nargs='*', 30 | help='If you input model_file it will save or load a model.') 31 | parser.add_argument('--definition', metavar='array of data type such as str, int and float with delimiter [,]', dest='definition', default=False, nargs='*', 32 | help='If you define data type of columns, send array of full column definitions.') 33 | 34 | args = parser.parse_args() 35 | 36 | #---------------------------- 37 | # functions 38 | #---------------------------- 39 | class MakeModel: 40 | #init 41 | def __init__(self, args): 42 | self.X = self.Y = [] 43 | self.row_length = self.column_length = 0 44 | self.method = args.method[0] 45 | self.ifp = args.input_file[0] 46 | 47 | if args.model_file != False: 48 | self.mfp = args.model_file[0] 49 | else: 50 | self.mfp = False 51 | 52 | if args.output_file != False: 53 | self.ofp = args.output_file[0] 54 | else: 55 | self.ofp = False 56 | 57 | if args.definition != False: 58 | self.dfin = args.definition.split(",") 59 | else: 60 | self.dfin = False 61 | 62 | #create layers 63 | def create_model(self, evMethod, neurons, layers, act, learn_rate, cls, mtr): 64 | # Criate model 65 | model = Sequential() 66 | model.add(Dense(neurons, input_dim=self.column_length, kernel_initializer='normal', activation='relu')) 67 | for i in range(1, layers): 68 | model.add(Dense(int(numpy.ceil(numpy.power(neurons,1/i)*2)), kernel_initializer='normal', activation='relu')) 69 | model.add(Dense(cls, kernel_initializer='normal', activation=act)) 70 | # Compile model 71 | adam = optimizers.Adam(lr=learn_rate) 72 | model.compile(loss=evMethod, optimizer=adam, metrics=mtr) 73 | return model 74 | 75 | #load dataset 76 | def load_dataset(self): 77 | dataframe = pandas.read_csv(self.ifp, header=0).dropna() 78 | if self.dfin != False: 79 | dataframe[dataframe.columns].apply(lambda x: x.astype(self.dfin[dataframe.columns.get_loc(x.name)])) 80 | dataframe_X = pandas.get_dummies(dataframe[dataframe.columns[1:]]) #create dummy variables 81 | if self.method == 'multiple': 82 | dataframe_Y = pandas.get_dummies(dataframe[dataframe.columns[0]]) #create dummy variables 83 | else: 84 | dataframe_Y = dataframe[dataframe.columns[0]] 85 | #print(dataframe_Y.head()) 86 | #print(dataframe_X.head()) 87 | self.row_length, self.column_length = dataframe_X.shape 88 | self.X = dataframe_X.values 89 | self.Y = dataframe_Y.values 90 | 91 | #train 92 | def train_model(self): 93 | #pipe to Grid Search 94 | estimators = [] 95 | estimators.append(('standardize', StandardScaler())) 96 | 97 | #rely on chosen method parameters 98 | if self.method == 'binary': 99 | evMethod = ['binary_crossentropy'] 100 | activation = ['sigmoid'] 101 | metr = [['accuracy']] 102 | estimators.append(('mlp', KerasClassifier(build_fn=self.create_model, epochs=10, batch_size=200, verbose=1))) 103 | cls = [1] 104 | elif self.method == 'multiple': 105 | evMethod = [['categorical_crossentropy']] 106 | activation = ['softmax'] 107 | metr = [['accuracy']] 108 | estimators.append(('mlp', KerasClassifier(build_fn=self.create_model, epochs=10, batch_size=200, verbose=1))) 109 | cls = [self.Y.shape[1]] 110 | else: 111 | evMethod = ['mean_squared_error'] 112 | activation = [None] 113 | metr = [None] 114 | estimators.append(('mlp', KerasRegressor(build_fn=self.create_model, epochs=10, batch_size=200, verbose=1))) 115 | cls = [1] 116 | 117 | pipeline = Pipeline(estimators) 118 | 119 | #test parameters 120 | batch_size = list(set([int(numpy.ceil(self.row_length/i)) for i in [1000,300,100]])) 121 | epochs = [10, 50, 100] 122 | neurons = list(set([int(numpy.ceil(self.column_length/i)*2) for i in numpy.arange(1,3,0.4)])) 123 | learn_rate = [0.001, 0.005, 0.01, 0.07] 124 | layers = [1,2,3,4,5] 125 | #test parameter 126 | """batch_size = [31] 127 | epochs = [100] 128 | neurons = [32] 129 | learn_rate = [0.01] 130 | layers = [5]""" 131 | #execution 132 | param_grid = dict(mlp__neurons = neurons, mlp__batch_size = batch_size, mlp__epochs=epochs, mlp__learn_rate=learn_rate, mlp__layers=layers, mlp__act=activation, mlp__evMethod=evMethod, mlp__cls=cls, mlp__mtr=metr) 133 | grid = GridSearchCV(estimator=pipeline, param_grid=param_grid) 134 | grid_result = grid.fit(self.X, self.Y) 135 | 136 | #output best parameter condition 137 | clf = [] 138 | clf = grid_result.best_estimator_ 139 | print(clf.get_params()) 140 | accuracy = clf.score(self.X, self.Y) 141 | if self.method in ['binary', 'multiple']: 142 | print("\nAccuracy: %.2f" % (accuracy)) 143 | else: 144 | print("Results: %.2f (%.2f) MSE" % (accuracy.mean(), accuracy.std())) 145 | 146 | #save model 147 | if self.mfp != False: 148 | clf.steps[1][1].model.save(self.mfp) 149 | 150 | #predict dataset 151 | def predict_ds(self): 152 | model = load_model(self.mfp) 153 | model.summary() 154 | sc = StandardScaler() 155 | self.X = sc.fit_transform(self.X) 156 | pr_Y = model.predict(self.X) 157 | if len([self.Y != '__null__']) > 0: 158 | if self.method == 'binary': 159 | predictions = [float(numpy.round(x)) for x in pr_Y] 160 | accuracy = numpy.mean(predictions == self.Y) 161 | print("Prediction Accuracy: %.2f%%" % (accuracy*100)) 162 | elif self.method == 'multiple': 163 | predictions = [] 164 | for i in range(0, len(pr_Y)-1): 165 | for j in range(0, len(pr_Y[i])-1): 166 | predictions.append(int(round(pr_Y[i][j]) - self.Y[i][j])) 167 | accuracy_total = len([x for x in predictions if x == 0])/len(predictions) 168 | accuracy_tooneg = len([x for x in predictions if x == -1])/len(predictions) 169 | accuracy_toopos = len([x for x in predictions if x == 1])/len(predictions) 170 | print("Prediction Accuracy: %.2f%% (positive-error:%.2f%%/negative-error:%.2f%%)" % (accuracy_total*100, accuracy_tooneg*100, accuracy_toopos*100)) 171 | else: 172 | accuracy = numpy.mean((self.Y - pr_Y)**2) 173 | print("MSE: %.2f" % (numpy.sqrt(accuracy))) 174 | 175 | #save predicted result 176 | if self.ofp != False: 177 | numpy.savetxt(self.ofp, pr_Y, fmt='%5s') 178 | 179 | #---------------------------- 180 | # select mode 181 | #---------------------------- 182 | m = MakeModel(args) 183 | if args.mode == ['create']: 184 | #make model 185 | m.load_dataset() 186 | m.train_model() 187 | else: 188 | #predict dataset 189 | m.load_dataset() 190 | m.predict_ds() 191 | -------------------------------------------------------------------------------- /auto_analyzer_rand.py: -------------------------------------------------------------------------------- 1 | import numpy 2 | import pandas 3 | from keras.models import Sequential 4 | from keras.layers import Dense 5 | from keras import optimizers 6 | from keras.wrappers.scikit_learn import KerasRegressor 7 | from keras.wrappers.scikit_learn import KerasClassifier 8 | from sklearn.model_selection import cross_val_score 9 | from sklearn.model_selection import KFold 10 | from sklearn.model_selection import RandomizedSearchCV 11 | from sklearn.preprocessing import StandardScaler 12 | from sklearn.pipeline import Pipeline 13 | from keras.models import load_model 14 | import os 15 | import argparse 16 | 17 | #---------------------------- 18 | # get command line variables 19 | #---------------------------- 20 | parser = argparse.ArgumentParser(description='Make models by keras. Place Y on the head column in the cleaned dataset with header names on the top row. Rows containing null values will be deleated.') 21 | parser.add_argument('--mode', choices=['create', 'predict'], dest='mode', metavar='create/predict', type=str, nargs='+', required=True, 22 | help='an integer for the accumulator') 23 | parser.add_argument('--input_file', dest='input_file', type=str, nargs='+', required=True, 24 | help='path to dataset or model') 25 | parser.add_argument('--method', choices=['binary', 'multiple', 'regression'], metavar='binary/multiple/regression', dest='method', type=str, nargs='+', required=True, 26 | help='Model type you solve') 27 | parser.add_argument('--output_file', dest='output_file', default=False, nargs='*', 28 | help='If you input output_file it will save result as directed path.') 29 | parser.add_argument('--model_file', dest='model_file', default=False, nargs='*', 30 | help='If you input model_file it will save or load a model.') 31 | parser.add_argument('--definition', metavar='array of data type such as str, int and float with delimiter [,]', dest='definition', default=False, nargs='*', 32 | help='If you define data type of columns, send array of full column definitions.') 33 | 34 | args = parser.parse_args() 35 | 36 | #---------------------------- 37 | # functions 38 | #---------------------------- 39 | class MakeModel: 40 | #init 41 | def __init__(self, args): 42 | self.X = self.Y = [] 43 | self.row_length = self.column_length = 0 44 | self.method = args.method[0] 45 | self.ifp = args.input_file[0] 46 | 47 | if args.model_file != False: 48 | self.mfp = args.model_file[0] 49 | else: 50 | self.mfp = False 51 | 52 | if args.output_file != False: 53 | self.ofp = args.output_file[0] 54 | else: 55 | self.ofp = False 56 | 57 | if args.definition != False: 58 | self.dfin = args.definition.split(",") 59 | else: 60 | self.dfin = False 61 | 62 | #create layers 63 | def create_model(self, evMethod, neurons, layers, act, learn_rate, cls, mtr): 64 | # Criate model 65 | model = Sequential() 66 | model.add(Dense(neurons, input_dim=self.column_length, kernel_initializer='normal', activation='relu')) 67 | for i in range(2, layers+1): 68 | model.add(Dense(int(numpy.ceil(numpy.power(neurons,1/i)*2)), kernel_initializer='normal', activation='relu')) 69 | model.add(Dense(cls, kernel_initializer='normal', activation=act)) 70 | # Compile model 71 | adam = optimizers.Adam(lr=learn_rate) 72 | model.compile(loss=evMethod, optimizer=adam, metrics=mtr) 73 | return model 74 | 75 | #load dataset 76 | def load_dataset(self): 77 | dataframe = pandas.read_csv(self.ifp, header=0).dropna() 78 | if self.dfin != False: 79 | dataframe[dataframe.columns].apply(lambda x: x.astype(self.dfin[dataframe.columns.get_loc(x.name)])) 80 | dataframe_X = pandas.get_dummies(dataframe[dataframe.columns[1:]]) #create dummy variables 81 | if self.method == 'multiple': 82 | dataframe_Y = pandas.get_dummies(dataframe[dataframe.columns[0]]) #create dummy variables 83 | else: 84 | dataframe_Y = dataframe[dataframe.columns[0]] 85 | self.row_length, self.column_length = dataframe_X.shape 86 | self.X = dataframe_X.values 87 | self.Y = dataframe_Y.values 88 | 89 | #train 90 | def train_model(self): 91 | #pipe to Grid Search 92 | estimators = [] 93 | estimators.append(('standardize', StandardScaler())) 94 | 95 | #rely on chosen method parameters 96 | if self.method == 'binary': 97 | evMethod = ['binary_crossentropy'] 98 | activation = ['sigmoid'] 99 | metr = [['accuracy']] 100 | estimators.append(('mlp', KerasClassifier(build_fn=self.create_model, epochs=10, batch_size=200, verbose=1))) 101 | cls = [1] 102 | elif self.method == 'multiple': 103 | evMethod = [['categorical_crossentropy']] 104 | activation = ['softmax'] 105 | metr = [['accuracy']] 106 | estimators.append(('mlp', KerasClassifier(build_fn=self.create_model, epochs=10, batch_size=200, verbose=1))) 107 | cls = [self.Y.shape[1]] 108 | else: 109 | evMethod = ['mean_squared_error'] 110 | activation = [None] 111 | metr = [None] 112 | estimators.append(('mlp', KerasRegressor(build_fn=self.create_model, epochs=10, batch_size=200, verbose=1))) 113 | cls = [1] 114 | 115 | pipeline = Pipeline(estimators) 116 | 117 | #test parameters 118 | batch_size = list(set([int(numpy.ceil(self.row_length/i)) for i in [1000,300,100]])) 119 | epochs = [10, 50, 100] 120 | neurons = list(set([int(numpy.ceil(self.column_length/i)*2) for i in numpy.arange(1,3,0.4)])) 121 | learn_rate = [0.001, 0.005, 0.01, 0.07] 122 | layers = [1,2,3,4,5] 123 | #test parameter 124 | """batch_size = [10] 125 | epochs = [100] 126 | neurons = [self.column_length] 127 | learn_rate = [0.001] 128 | layers = [1]""" 129 | #execution 130 | n_iter_search = 30 131 | param_grid = dict(mlp__neurons = neurons, mlp__batch_size = batch_size, mlp__epochs=epochs, mlp__learn_rate=learn_rate, mlp__layers=layers, mlp__act=activation, mlp__evMethod=evMethod, mlp__cls=cls, mlp__mtr=metr) 132 | grid = RandomizedSearchCV(estimator=pipeline, param_distributions=param_grid, n_iter=n_iter_search) 133 | grid_result = grid.fit(self.X, self.Y) 134 | grid_result.predict(self.X) #refit weight of each variables 135 | 136 | #output best parameter condition 137 | clf = [] 138 | clf = grid_result.best_estimator_ 139 | print(clf.get_params()) 140 | accuracy = clf.score(self.X, self.Y) 141 | if self.method in ['binary', 'multiple']: 142 | print("\nAccuracy: %.2f" % (accuracy)) 143 | else: 144 | print("Results: %.2f (%.2f) MSE" % (accuracy.mean(), accuracy.std())) 145 | 146 | #save model 147 | if self.mfp != False: 148 | clf.steps[1][1].model.save(self.mfp) 149 | 150 | #predict dataset 151 | def predict_ds(self): 152 | model = load_model(self.mfp) 153 | model.summary() 154 | sc = StandardScaler() 155 | self.X = sc.fit_transform(self.X) 156 | pr_Y = model.predict(self.X) 157 | if len([self.Y != '__null__']) > 0: 158 | if self.method == 'binary': 159 | predictions = [float(numpy.round(x)) for x in pr_Y] 160 | accuracy = numpy.mean(predictions == self.Y) 161 | print("Prediction Accuracy: %.2f%%" % (accuracy*100)) 162 | elif self.method == 'multiple': 163 | predictions = [] 164 | for i in range(0, len(pr_Y)-1): 165 | for j in range(0, len(pr_Y[i])-1): 166 | predictions.append(int(round(pr_Y[i][j]) - self.Y[i][j])) 167 | accuracy_total = len([x for x in predictions if x == 0])/len(predictions) 168 | accuracy_tooneg = len([x for x in predictions if x == -1])/len(predictions) 169 | accuracy_toopos = len([x for x in predictions if x == 1])/len(predictions) 170 | print("Prediction Accuracy: %.2f%% (positive-error:%.2f%%/negative-error:%.2f%%)" % (accuracy_total*100, accuracy_tooneg*100, accuracy_toopos*100)) 171 | else: 172 | accuracy = numpy.mean((self.Y - pr_Y)**2) 173 | print("MSE: %.2f" % (numpy.sqrt(accuracy))) 174 | 175 | #save predicted result 176 | if self.ofp != False: 177 | numpy.savetxt(self.ofp, pr_Y, fmt='%5s') 178 | 179 | #---------------------------- 180 | # select mode 181 | #---------------------------- 182 | m = MakeModel(args) 183 | if args.mode == ['create']: 184 | #make model 185 | m.load_dataset() 186 | m.train_model() 187 | else: 188 | #predict dataset 189 | m.load_dataset() 190 | m.predict_ds() 191 | 192 | 193 | -------------------------------------------------------------------------------- /is_future_purchase.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 92, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "text/html": [ 11 | "
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YCustomerIDP1P2P3P4P5P6P7P8...P91P92P93P94P95P96P97P98P99P100
0012346604604.0NaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1012348120238.060.060.0109.0109.0181.0553.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
20123496032259.01333.0649.01072.0488.0344.0140.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
30123502171117.01073.01847.0384.02139.0102.0148.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
40123521155630.0779.01325.0402.0348.0524.01839.0...749.0537.01066.01196.011.0NaNNaNNaNNaNNaN
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5 rows × 102 columns

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" 164 | ], 165 | "text/plain": [ 166 | " Y CustomerID P1 P2 P3 P4 P5 P6 P7 P8 \\\n", 167 | "0 0 12346 604 604.0 NaN NaN NaN NaN NaN NaN \n", 168 | "1 0 12348 120 238.0 60.0 60.0 109.0 109.0 181.0 553.0 \n", 169 | "2 0 12349 603 2259.0 1333.0 649.0 1072.0 488.0 344.0 140.0 \n", 170 | "3 0 12350 217 1117.0 1073.0 1847.0 384.0 2139.0 102.0 148.0 \n", 171 | "4 0 12352 1155 630.0 779.0 1325.0 402.0 348.0 524.0 1839.0 \n", 172 | "\n", 173 | " ... P91 P92 P93 P94 P95 P96 P97 P98 P99 P100 \n", 174 | "0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", 175 | "1 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", 176 | "2 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", 177 | "3 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", 178 | "4 ... 749.0 537.0 1066.0 1196.0 11.0 NaN NaN NaN NaN NaN \n", 179 | "\n", 180 | "[5 rows x 102 columns]" 181 | ] 182 | }, 183 | "execution_count": 92, 184 | "metadata": {}, 185 | "output_type": "execute_result" 186 | } 187 | ], 188 | "source": [ 189 | "import numpy\n", 190 | "from keras.models import Sequential\n", 191 | "from keras.layers import Dense\n", 192 | "from keras.layers import LSTM\n", 193 | "from keras.layers.embeddings import Embedding\n", 194 | "from keras.preprocessing import sequence\n", 195 | "import pandas as pd\n", 196 | "from sklearn import cross_validation\n", 197 | "\n", 198 | "# load data\n", 199 | "top_words = 4214 # insert max index of items + 1\n", 200 | "max_length = 10 # length of sequential data\n", 201 | "df = pd.read_csv(\"c:/dev/dl/purchacedata_base.csv\", header=0)\n", 202 | "\n", 203 | "df.head()" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": 93, 209 | "metadata": {}, 210 | "outputs": [ 211 | { 212 | "name": "stdout", 213 | "output_type": "stream", 214 | "text": [ 215 | "(2835, 10)\n", 216 | "(946, 10)\n", 217 | "[0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0\n", 218 | " 0 0 0 0 0 1 1 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 1 0 0 0 1 1 1 0 1 0 0 1 0 0\n", 219 | " 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0\n", 220 | " 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 1 0 0\n", 221 | " 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0\n", 222 | " 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0\n", 223 | " 1 0 1 0 1 0 0 0 0 1 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1\n", 224 | " 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1 0 1 1 0 1 1 0 0\n", 225 | " 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 1 1 1 1 0 0 1 1 1\n", 226 | " 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 1 0 1\n", 227 | " 1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 1 0 1 0 1 1 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1\n", 228 | " 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 0 1 0 1 0\n", 229 | " 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0\n", 230 | " 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1\n", 231 | " 0 1 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1\n", 232 | " 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 1 0 0 0\n", 233 | " 0 1 1 0 0 0 0 0 0 1 1 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0\n", 234 | " 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0\n", 235 | " 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0\n", 236 | " 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0\n", 237 | " 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0\n", 238 | " 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0\n", 239 | " 1 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 1 0 0 1 1\n", 240 | " 0 1 0 1 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0\n", 241 | " 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0 0 0 1\n", 242 | " 1 1 0 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1]\n" 243 | ] 244 | } 245 | ], 246 | "source": [ 247 | "# extract columns and drop rows having NaN\n", 248 | "df = df[df.columns[0:(max_length + 2)]].dropna().astype(int)\n", 249 | "\n", 250 | "train_X, test_X, train_Y, test_Y = cross_validation.train_test_split(df[df.columns[2:(max_length + 2)]], df[\"Y\"])\n", 251 | "\n", 252 | "train_X = train_X.values\n", 253 | "train_Y = train_Y.values\n", 254 | "test_X = test_X.values\n", 255 | "test_Y = test_Y.values\n", 256 | "\n", 257 | "print(train_X.shape)\n", 258 | "print(test_X.shape)\n", 259 | "print(test_Y)" 260 | ] 261 | }, 262 | { 263 | "cell_type": "code", 264 | "execution_count": 107, 265 | "metadata": {}, 266 | "outputs": [ 267 | { 268 | "name": "stdout", 269 | "output_type": "stream", 270 | "text": [ 271 | "_________________________________________________________________\n", 272 | "Layer (type) Output Shape Param # \n", 273 | "=================================================================\n", 274 | "embedding_29 (Embedding) (None, 10, 5) 21070 \n", 275 | "_________________________________________________________________\n", 276 | "lstm_29 (LSTM) (None, 3) 108 \n", 277 | "_________________________________________________________________\n", 278 | "dense_29 (Dense) (None, 1) 4 \n", 279 | "=================================================================\n", 280 | "Total params: 21,182\n", 281 | "Trainable params: 21,182\n", 282 | "Non-trainable params: 0\n", 283 | "_________________________________________________________________\n", 284 | "None\n", 285 | "Train on 2835 samples, validate on 946 samples\n", 286 | "Epoch 1/3\n", 287 | "2835/2835 [==============================] - 1s - loss: 0.6792 - acc: 0.6942 - val_loss: 0.6594 - val_acc: 0.7241\n", 288 | "Epoch 2/3\n", 289 | "2835/2835 [==============================] - 0s - loss: 0.6411 - acc: 0.7012 - val_loss: 0.6106 - val_acc: 0.7241\n", 290 | "Epoch 3/3\n", 291 | "2835/2835 [==============================] - 0s - loss: 0.6002 - acc: 0.7012 - val_loss: 0.5859 - val_acc: 0.7241\n" 292 | ] 293 | }, 294 | { 295 | "data": { 296 | "text/plain": [ 297 | "" 298 | ] 299 | }, 300 | "execution_count": 107, 301 | "metadata": {}, 302 | "output_type": "execute_result" 303 | } 304 | ], 305 | "source": [ 306 | "# create the model\n", 307 | "embedding_vector_length = 5\n", 308 | "model = Sequential()\n", 309 | "model.add(Embedding(top_words, embedding_vector_length, input_length=max_length))\n", 310 | "model.add(LSTM(3))\n", 311 | "model.add(Dense(1, activation='sigmoid'))\n", 312 | "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", 313 | "print(model.summary())\n", 314 | "model.fit(train_X, train_Y, validation_data=(test_X, test_Y), epochs=3, batch_size=64)" 315 | ] 316 | }, 317 | { 318 | "cell_type": "code", 319 | "execution_count": 108, 320 | "metadata": {}, 321 | "outputs": [ 322 | { 323 | "name": "stdout", 324 | "output_type": "stream", 325 | "text": [ 326 | "Accuracy: 72.41%\n" 327 | ] 328 | } 329 | ], 330 | "source": [ 331 | "# Final evaluation of the model\n", 332 | "scores = model.evaluate(test_X, test_Y, verbose=0)\n", 333 | "print(\"Accuracy: %.2f%%\" % (scores[1]*100))" 334 | ] 335 | }, 336 | { 337 | "cell_type": "code", 338 | "execution_count": null, 339 | "metadata": { 340 | "collapsed": true 341 | }, 342 | "outputs": [], 343 | "source": [] 344 | } 345 | ], 346 | "metadata": { 347 | "kernelspec": { 348 | "display_name": "Python 3", 349 | "language": "python", 350 | "name": "python3" 351 | }, 352 | "language_info": { 353 | "codemirror_mode": { 354 | "name": "ipython", 355 | "version": 3 356 | }, 357 | "file_extension": ".py", 358 | "mimetype": "text/x-python", 359 | "name": "python", 360 | "nbconvert_exporter": "python", 361 | "pygments_lexer": "ipython3", 362 | "version": "3.5.0" 363 | } 364 | }, 365 | "nbformat": 4, 366 | "nbformat_minor": 2 367 | } 368 | -------------------------------------------------------------------------------- /tokyo-weather-2003-2012.csv: -------------------------------------------------------------------------------- 1 | ice_sales,year,month,avg_temp,total_rain,humidity,num_day_over25deg 2 | 331,2003,1,9.3,101,46,0 3 | 268,2003,2,9.9,53.5,52,0 4 | 365,2003,3,12.7,159.5,49,0 5 | 492,2003,4,19.2,121,61,3 6 | 632,2003,5,22.4,172.5,65,7 7 | 730,2003,6,26.6,85,69,21 8 | 821,2003,7,26,187.5,75,21 9 | 1057,2003,8,29.5,370,73,26 10 | 724,2003,9,28.1,150,66,23 11 | 430,2003,10,21.4,171.5,59,3 12 | 363,2003,11,17.4,229.5,67,0 13 | 415,2003,12,13.2,53,50,0 14 | 351,2004,1,10.1,3.5,43,0 15 | 322,2004,2,12.9,20,45,0 16 | 367,2004,3,14,129.5,53,0 17 | 508,2004,4,21.3,69.5,51,3 18 | 667,2004,5,23.7,149,67,13 19 | 772,2004,6,27.5,112.5,66,24 20 | 1148,2004,7,33.1,23.5,62,31 21 | 1080,2004,8,31,79.5,65,28 22 | 653,2004,9,28.7,195,68,26 23 | 434,2004,10,20.7,780,69,3 24 | 358,2004,11,19,108.5,60,0 25 | 388,2004,12,13.4,79.5,49,0 26 | 323,2005,1,10,77,47,0 27 | 283,2005,2,9.9,48,45,0 28 | 357,2005,3,13.1,71,49,0 29 | 543,2005,4,19.6,81,54,2 30 | 667,2005,5,21.9,180.5,58,6 31 | 812,2005,6,26.7,170.5,70,22 32 | 1037,2005,7,29.1,247.5,71,27 33 | 1179,2005,8,31.8,189.5,68,31 34 | 739,2005,9,28.2,177.5,67,24 35 | 453,2005,10,22.3,201.5,69,7 36 | 315,2005,11,17,34.5,52,0 37 | 359,2005,12,10.2,3.5,39,0 38 | 322,2006,1,8.5,67,44,0 39 | 279,2006,2,10.5,113,53,0 40 | 373,2006,3,14,79.5,48,0 41 | 457,2006,4,17.5,123,57,0 42 | 602,2006,5,22.7,99,65,7 43 | 748,2006,6,25.4,138.5,71,15 44 | 973,2006,7,28.6,165,74,26 45 | 1193,2006,8,31.1,126,69,31 46 | 654,2006,9,27.1,175.5,68,20 47 | 493,2006,10,22.9,318,66,9 48 | 336,2006,11,17.9,135,59,0 49 | 392,2006,12,12.5,200.5,52,0 50 | 347,2007,1,10.9,42,45,0 51 | 292,2007,2,12.8,57,45,0 52 | 387,2007,3,15,77,44,0 53 | 466,2007,4,17.9,134,59,1 54 | 652,2007,5,24,115.5,58,13 55 | 768,2007,6,27.1,80,66,23 56 | 908,2007,7,27.4,253,74,26 57 | 1279,2007,8,33,9.5,66,31 58 | 784,2007,9,28.5,319.5,71,25 59 | 469,2007,10,22.2,135.5,63,4 60 | 324,2007,11,16.6,37,56,0 61 | 405,2007,12,12.6,72,54,0 62 | 346,2008,1,9.4,17.5,45,0 63 | 288,2008,2,9.6,57,41,0 64 | 404,2008,3,14.9,119.5,52,0 65 | 501,2008,4,18.4,240,59,1 66 | 689,2008,5,22,255,65,8 67 | 727,2008,6,24.6,225.5,72,15 68 | 1182,2008,7,30.9,48,71,31 69 | 1190,2008,8,30.7,387.5,74,28 70 | 691,2008,9,27.7,158.5,69,25 71 | 477,2008,10,22.6,204.5,66,1 72 | 355,2008,11,16.4,74,56,0 73 | 414,2008,12,13.7,70.5,53,0 74 | 351,2009,1,10.2,142,48,0 75 | 303,2009,2,11.5,46.5,50,0 76 | 386,2009,3,13.7,98.5,48,0 77 | 569,2009,4,20.2,162.5,54,1 78 | 768,2009,5,23.6,242,64,9 79 | 819,2009,6,25.8,226,72,16 80 | 1072,2009,7,29.3,78.5,72,28 81 | 1215,2009,8,30.1,242,69,30 82 | 723,2009,9,26.5,53,64,21 83 | 495,2009,10,22.3,276.5,64,5 84 | 402,2009,11,17,151.5,63,1 85 | 440,2009,12,12.4,82.5,51,0 86 | 362,2010,1,11,9,41,0 87 | 305,2010,2,9.9,115,60,0 88 | 383,2010,3,13.2,143.5,61,0 89 | 464,2010,4,16.6,214,62,1 90 | 752,2010,5,23,114,60,8 91 | 841,2010,6,27.5,108,67,24 92 | 1211,2010,7,31.6,70,70,31 93 | 1451,2010,8,33.5,27,67,31 94 | 864,2010,9,29,428,68,22 95 | 504,2010,10,21.8,211,68,6 96 | 351,2010,11,17.2,94.5,56,0 97 | 423,2010,12,13.7,145.5,50,0 98 | 346,2011,1,9.1,3.5,36,0 99 | 289,2011,2,11.2,151,52,0 100 | 329,2011,3,12.3,74,47,0 101 | 462,2011,4,18.9,96,50,0 102 | 672,2011,5,22.2,213.5,63,8 103 | 791,2011,6,26,116.5,71,15 104 | 1265,2011,7,30.9,54.5,67,29 105 | 1241,2011,8,31.2,244,71,29 106 | 767,2011,9,28.8,235,68,23 107 | 516,2011,10,23,119.5,61,9 108 | 393,2011,11,18.3,112.5,58,0 109 | 423,2011,12,11.1,59.5,48,0 110 | 339,2012,1,8.3,50,43,0 111 | 274,2012,2,9.1,94,49,0 112 | 385,2012,3,12.5,144.5,59,0 113 | 524,2012,4,18.5,118.5,63,1 114 | 671,2012,5,23.6,231,65,12 115 | 798,2012,6,24.8,185,73,15 116 | 1165,2012,7,30.1,130,75,27 117 | 1332,2012,8,33.1,25,69,31 118 | 849,2012,9,29.8,214.5,73,27 119 | 515,2012,10,23,154.5,65,9 120 | 326,2012,11,16.3,154,58,0 121 | 414,2012,12,11.2,69,52,0 122 | -------------------------------------------------------------------------------- /転移学習テスト.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "Using TensorFlow backend.\n" 13 | ] 14 | }, 15 | { 16 | "name": "stdout", 17 | "output_type": "stream", 18 | "text": [ 19 | "(120, 4) (30, 4) (120, 3) (30, 3)\n" 20 | ] 21 | } 22 | ], 23 | "source": [ 24 | "import numpy as np\n", 25 | "\n", 26 | "from sklearn import datasets\n", 27 | "from sklearn.model_selection import train_test_split\n", 28 | "\n", 29 | "from keras.models import Sequential\n", 30 | "from keras.layers.core import Dense, Activation\n", 31 | "from keras.layers import Dense, Dropout, Flatten\n", 32 | "from keras.utils import np_utils\n", 33 | "from sklearn import preprocessing\n", 34 | "import matplotlib.pyplot as plt\n", 35 | "\n", 36 | "a = l = []\n", 37 | "\n", 38 | "def build_multilayer_perceptron():\n", 39 | " \"\"\"多層パーセプトロンモデルを構築\"\"\"\n", 40 | " model = Sequential()\n", 41 | " model.add(Dense(4, input_shape=(4, ), name='l1'))\n", 42 | " model.add(Activation('relu'))\n", 43 | " model.add(Dense(4, input_shape=(4, ), name='l2'))\n", 44 | " model.add(Activation('relu'))\n", 45 | " #model.add(Dropout(0.5))\n", 46 | " model.add(Dense(3, name='cls'))\n", 47 | " model.add(Activation('softmax'))\n", 48 | " return model\n", 49 | "\n", 50 | "# Irisデータをロード\n", 51 | "iris = datasets.load_iris()\n", 52 | "X = iris.data\n", 53 | "Y = iris.target\n", 54 | "\n", 55 | "# データの標準化\n", 56 | "X = preprocessing.scale(X)\n", 57 | "\n", 58 | "# ラベルをone-hot-encoding形式に変換\n", 59 | "Y = np_utils.to_categorical(Y)\n", 60 | "\n", 61 | "# 訓練データとテストデータに分割\n", 62 | "train_X, test_X, train_Y, test_Y = train_test_split(X, Y, train_size=0.8)\n", 63 | "print(train_X.shape, test_X.shape, train_Y.shape, test_Y.shape)" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": 2, 69 | "metadata": {}, 70 | "outputs": [ 71 | { 72 | "name": "stdout", 73 | "output_type": "stream", 74 | "text": [ 75 | "Epoch 1/50\n", 76 | "120/120 [==============================] - 0s - loss: 1.2139 - acc: 0.3833 \n", 77 | "Epoch 2/50\n", 78 | "120/120 [==============================] - 0s - loss: 1.1321 - acc: 0.5250 \n", 79 | "Epoch 3/50\n", 80 | "120/120 [==============================] - 0s - loss: 1.0962 - acc: 0.5917 \n", 81 | "Epoch 4/50\n", 82 | "120/120 [==============================] - 0s - loss: 1.0738 - acc: 0.6083 \n", 83 | "Epoch 5/50\n", 84 | "120/120 [==============================] - 0s - loss: 1.0572 - acc: 0.6000 \n", 85 | "Epoch 6/50\n", 86 | "120/120 [==============================] - 0s - loss: 1.0434 - acc: 0.6250 \n", 87 | "Epoch 7/50\n", 88 | "120/120 [==============================] - 0s - loss: 1.0319 - acc: 0.6333 \n", 89 | "Epoch 8/50\n", 90 | "120/120 [==============================] - 0s - loss: 1.0205 - acc: 0.6333 \n", 91 | "Epoch 9/50\n", 92 | "120/120 [==============================] - 0s - loss: 1.0094 - acc: 0.6333 \n", 93 | "Epoch 10/50\n", 94 | "120/120 [==============================] - 0s - loss: 0.9990 - acc: 0.6250 \n", 95 | "Epoch 11/50\n", 96 | "120/120 [==============================] - 0s - loss: 0.9877 - acc: 0.6250 \n", 97 | "Epoch 12/50\n", 98 | "120/120 [==============================] - 0s - loss: 0.9764 - acc: 0.6333 \n", 99 | "Epoch 13/50\n", 100 | "120/120 [==============================] - 0s - loss: 0.9639 - acc: 0.6250 \n", 101 | "Epoch 14/50\n", 102 | "120/120 [==============================] - 0s - loss: 0.9502 - acc: 0.6333 \n", 103 | "Epoch 15/50\n", 104 | "120/120 [==============================] - 0s - loss: 0.9354 - acc: 0.6417 \n", 105 | "Epoch 16/50\n", 106 | "120/120 [==============================] - 0s - loss: 0.9204 - acc: 0.6583 \n", 107 | "Epoch 17/50\n", 108 | "120/120 [==============================] - 0s - loss: 0.9053 - acc: 0.6417 \n", 109 | "Epoch 18/50\n", 110 | "120/120 [==============================] - 0s - loss: 0.8875 - acc: 0.6500 \n", 111 | "Epoch 19/50\n", 112 | "120/120 [==============================] - 0s - loss: 0.8714 - acc: 0.6500 \n", 113 | "Epoch 20/50\n", 114 | "120/120 [==============================] - 0s - loss: 0.8537 - acc: 0.6500 \n", 115 | "Epoch 21/50\n", 116 | "120/120 [==============================] - 0s - loss: 0.8368 - acc: 0.6417 \n", 117 | "Epoch 22/50\n", 118 | "120/120 [==============================] - 0s - loss: 0.8197 - acc: 0.6417 \n", 119 | "Epoch 23/50\n", 120 | "120/120 [==============================] - 0s - loss: 0.8035 - acc: 0.6333 \n", 121 | "Epoch 24/50\n", 122 | "120/120 [==============================] - 0s - loss: 0.7868 - acc: 0.6250 \n", 123 | "Epoch 25/50\n", 124 | "120/120 [==============================] - 0s - loss: 0.7716 - acc: 0.6250 \n", 125 | "Epoch 26/50\n", 126 | "120/120 [==============================] - 0s - loss: 0.7556 - acc: 0.6167 \n", 127 | "Epoch 27/50\n", 128 | "120/120 [==============================] - 0s - loss: 0.7407 - acc: 0.6167 \n", 129 | "Epoch 28/50\n", 130 | "120/120 [==============================] - 0s - loss: 0.7257 - acc: 0.6167 \n", 131 | "Epoch 29/50\n", 132 | "120/120 [==============================] - 0s - loss: 0.7129 - acc: 0.5917 \n", 133 | "Epoch 30/50\n", 134 | "120/120 [==============================] - 0s - loss: 0.7001 - acc: 0.5583 \n", 135 | "Epoch 31/50\n", 136 | "120/120 [==============================] - 0s - loss: 0.6875 - acc: 0.5667 \n", 137 | "Epoch 32/50\n", 138 | "120/120 [==============================] - 0s - loss: 0.6759 - acc: 0.5333 \n", 139 | "Epoch 33/50\n", 140 | "120/120 [==============================] - 0s - loss: 0.6651 - acc: 0.5417 \n", 141 | "Epoch 34/50\n", 142 | "120/120 [==============================] - 0s - loss: 0.6552 - acc: 0.5500 \n", 143 | "Epoch 35/50\n", 144 | "120/120 [==============================] - 0s - loss: 0.6440 - acc: 0.5167 \n", 145 | "Epoch 36/50\n", 146 | "120/120 [==============================] - 0s - loss: 0.6345 - acc: 0.5167 \n", 147 | "Epoch 37/50\n", 148 | "120/120 [==============================] - 0s - loss: 0.6260 - acc: 0.5417 \n", 149 | "Epoch 38/50\n", 150 | "120/120 [==============================] - 0s - loss: 0.6176 - acc: 0.5500 \n", 151 | "Epoch 39/50\n", 152 | "120/120 [==============================] - 0s - loss: 0.6088 - acc: 0.5833 \n", 153 | "Epoch 40/50\n", 154 | "120/120 [==============================] - 0s - loss: 0.6002 - acc: 0.6000 \n", 155 | "Epoch 41/50\n", 156 | "120/120 [==============================] - 0s - loss: 0.5923 - acc: 0.6417 \n", 157 | "Epoch 42/50\n", 158 | "120/120 [==============================] - 0s - loss: 0.5868 - acc: 0.6417 \n", 159 | "Epoch 43/50\n", 160 | "120/120 [==============================] - 0s - loss: 0.5771 - acc: 0.6500 \n", 161 | "Epoch 44/50\n", 162 | "120/120 [==============================] - 0s - loss: 0.5702 - acc: 0.6500 \n", 163 | "Epoch 45/50\n", 164 | "120/120 [==============================] - 0s - loss: 0.5627 - acc: 0.6500 \n", 165 | "Epoch 46/50\n", 166 | "120/120 [==============================] - 0s - loss: 0.5563 - acc: 0.6500 \n", 167 | "Epoch 47/50\n", 168 | "120/120 [==============================] - 0s - loss: 0.5495 - acc: 0.6500 \n", 169 | "Epoch 48/50\n", 170 | "120/120 [==============================] - 0s - loss: 0.5372 - acc: 0.6500 \n", 171 | "Epoch 49/50\n", 172 | "120/120 [==============================] - 0s - loss: 0.5218 - acc: 0.6500 \n", 173 | "Epoch 50/50\n", 174 | "120/120 [==============================] - 0s - loss: 0.4948 - acc: 0.6500 \n", 175 | "Accuracy = 0.73\n" 176 | ] 177 | } 178 | ], 179 | "source": [ 180 | "# モデル構築\n", 181 | "model = build_multilayer_perceptron()\n", 182 | "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", 183 | "\n", 184 | "# モデル訓練\n", 185 | "model.fit(train_X, train_Y, epochs=50, batch_size=1, verbose=1)\n", 186 | "\n", 187 | "# モデル評価\n", 188 | "loss, accuracy = model.evaluate(test_X, test_Y, verbose=0)\n", 189 | "print(\"Accuracy = {:.2f}\".format(accuracy))\n" 190 | ] 191 | }, 192 | { 193 | "cell_type": "code", 194 | "execution_count": 21, 195 | "metadata": { 196 | "collapsed": true 197 | }, 198 | "outputs": [], 199 | "source": [ 200 | "#モデル保存\n", 201 | "model.save_weights('my_model_weights.h5')\n" 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": 4, 207 | "metadata": {}, 208 | "outputs": [ 209 | { 210 | "name": "stdout", 211 | "output_type": "stream", 212 | "text": [ 213 | "_________________________________________________________________\n", 214 | "Layer (type) Output Shape Param # \n", 215 | "=================================================================\n", 216 | "l1 (Dense) (None, 4) 20 \n", 217 | "_________________________________________________________________\n", 218 | "activation_7 (Activation) (None, 4) 0 \n", 219 | "_________________________________________________________________\n", 220 | "l2 (Dense) (None, 4) 20 \n", 221 | "_________________________________________________________________\n", 222 | "activation_8 (Activation) (None, 4) 0 \n", 223 | "_________________________________________________________________\n", 224 | "dropout_2 (Dropout) (None, 4) 0 \n", 225 | "_________________________________________________________________\n", 226 | "cls (Dense) (None, 3) 15 \n", 227 | "_________________________________________________________________\n", 228 | "activation_9 (Activation) (None, 3) 0 \n", 229 | "=================================================================\n", 230 | "Total params: 55\n", 231 | "Trainable params: 55\n", 232 | "Non-trainable params: 0\n", 233 | "_________________________________________________________________\n" 234 | ] 235 | } 236 | ], 237 | "source": [ 238 | "#モデルのロード\n", 239 | "model = Sequential()\n", 240 | "model.add(Dense(4, input_shape=(4, ), name='l1'))\n", 241 | "model.add(Activation('relu'))\n", 242 | "model.add(Dense(4, input_shape=(4, ), name='l2'))\n", 243 | "model.add(Activation('relu'))\n", 244 | "model.add(Dropout(0.5))\n", 245 | "model.add(Dense(3, name='cls'))\n", 246 | "model.add(Activation('softmax'))\n", 247 | "\n", 248 | "model.load_weights('my_model_weights.h5', by_name=True)\n", 249 | "model.summary()\n", 250 | "\n", 251 | "#\n", 252 | "#for layer in model.layers[:2]:\n", 253 | "# layer.trainable = False" 254 | ] 255 | }, 256 | { 257 | "cell_type": "code", 258 | "execution_count": 5, 259 | "metadata": {}, 260 | "outputs": [ 261 | { 262 | "name": "stdout", 263 | "output_type": "stream", 264 | "text": [ 265 | "Epoch 1/50\n", 266 | "30/30 [==============================] - 0s - loss: 1.9885 - acc: 0.5333 \n", 267 | "Epoch 2/50\n", 268 | "30/30 [==============================] - 0s - loss: 1.2767 - acc: 0.6667 \n", 269 | "Epoch 3/50\n", 270 | "30/30 [==============================] - 0s - loss: 0.4599 - acc: 0.7667 \n", 271 | "Epoch 4/50\n", 272 | "30/30 [==============================] - 0s - loss: 1.4425 - acc: 0.6333 \n", 273 | "Epoch 5/50\n", 274 | "30/30 [==============================] - 0s - loss: 1.2697 - acc: 0.7000 \n", 275 | "Epoch 6/50\n", 276 | "30/30 [==============================] - 0s - loss: 0.9079 - acc: 0.7667 \n", 277 | "Epoch 7/50\n", 278 | "30/30 [==============================] - 0s - loss: 1.2881 - acc: 0.6667 \n", 279 | "Epoch 8/50\n", 280 | "30/30 [==============================] - 0s - loss: 1.5422 - acc: 0.6333 \n", 281 | "Epoch 9/50\n", 282 | "30/30 [==============================] - 0s - loss: 0.5990 - acc: 0.8333 \n", 283 | "Epoch 10/50\n", 284 | "30/30 [==============================] - 0s - loss: 1.8923 - acc: 0.5333 \n", 285 | "Epoch 11/50\n", 286 | "30/30 [==============================] - 0s - loss: 1.1823 - acc: 0.7333 \n", 287 | "Epoch 12/50\n", 288 | "30/30 [==============================] - 0s - loss: 1.2670 - acc: 0.6333 \n", 289 | "Epoch 13/50\n", 290 | "30/30 [==============================] - 0s - loss: 0.8398 - acc: 0.7333 \n", 291 | "Epoch 14/50\n", 292 | "30/30 [==============================] - 0s - loss: 0.9550 - acc: 0.7333 \n", 293 | "Epoch 15/50\n", 294 | "30/30 [==============================] - 0s - loss: 0.9578 - acc: 0.7333 \n", 295 | "Epoch 16/50\n", 296 | "30/30 [==============================] - ETA: 0s - loss: 0.0011 - acc: 1.000 - 0s - loss: 1.0942 - acc: 0.7000 \n", 297 | "Epoch 17/50\n", 298 | "30/30 [==============================] - 0s - loss: 0.6804 - acc: 0.7333 \n", 299 | "Epoch 18/50\n", 300 | "30/30 [==============================] - 0s - loss: 0.8193 - acc: 0.7667 \n", 301 | "Epoch 19/50\n", 302 | "30/30 [==============================] - 0s - loss: 1.0397 - acc: 0.7333 \n", 303 | "Epoch 20/50\n", 304 | "30/30 [==============================] - 0s - loss: 1.1131 - acc: 0.6333 \n", 305 | "Epoch 21/50\n", 306 | "30/30 [==============================] - 0s - loss: 0.7545 - acc: 0.7667 \n", 307 | "Epoch 22/50\n", 308 | "30/30 [==============================] - 0s - loss: 0.5940 - acc: 0.8333 \n", 309 | "Epoch 23/50\n", 310 | "30/30 [==============================] - 0s - loss: 0.7878 - acc: 0.7000 \n", 311 | "Epoch 24/50\n", 312 | "30/30 [==============================] - 0s - loss: 0.8876 - acc: 0.7667 \n", 313 | "Epoch 25/50\n", 314 | "30/30 [==============================] - 0s - loss: 0.8782 - acc: 0.7333 \n", 315 | "Epoch 26/50\n", 316 | "30/30 [==============================] - 0s - loss: 0.7679 - acc: 0.7333 \n", 317 | "Epoch 27/50\n", 318 | "30/30 [==============================] - 0s - loss: 0.8210 - acc: 0.7333 \n", 319 | "Epoch 28/50\n", 320 | "30/30 [==============================] - 0s - loss: 0.5197 - acc: 0.8333 \n", 321 | "Epoch 29/50\n", 322 | "30/30 [==============================] - 0s - loss: 0.7196 - acc: 0.7333 \n", 323 | "Epoch 30/50\n", 324 | "30/30 [==============================] - 0s - loss: 0.9312 - acc: 0.6667 \n", 325 | "Epoch 31/50\n", 326 | "30/30 [==============================] - 0s - loss: 0.4061 - acc: 0.8667 \n", 327 | "Epoch 32/50\n", 328 | "30/30 [==============================] - 0s - loss: 0.8994 - acc: 0.6667 \n", 329 | "Epoch 33/50\n", 330 | "30/30 [==============================] - 0s - loss: 0.5981 - acc: 0.8000 \n", 331 | "Epoch 34/50\n", 332 | "30/30 [==============================] - 0s - loss: 0.7241 - acc: 0.7333 \n", 333 | "Epoch 35/50\n", 334 | "30/30 [==============================] - 0s - loss: 0.8799 - acc: 0.7000 \n", 335 | "Epoch 36/50\n", 336 | "30/30 [==============================] - 0s - loss: 0.5681 - acc: 0.8000 \n", 337 | "Epoch 37/50\n", 338 | "30/30 [==============================] - 0s - loss: 0.8932 - acc: 0.6667 \n", 339 | "Epoch 38/50\n", 340 | "30/30 [==============================] - 0s - loss: 0.8078 - acc: 0.7333 \n", 341 | "Epoch 39/50\n", 342 | "30/30 [==============================] - 0s - loss: 0.9197 - acc: 0.7000 \n", 343 | "Epoch 40/50\n", 344 | "30/30 [==============================] - 0s - loss: 0.8454 - acc: 0.7667 \n", 345 | "Epoch 41/50\n", 346 | "30/30 [==============================] - 0s - loss: 0.6268 - acc: 0.8000 \n", 347 | "Epoch 42/50\n", 348 | "30/30 [==============================] - 0s - loss: 0.6760 - acc: 0.7333 \n", 349 | "Epoch 43/50\n", 350 | "30/30 [==============================] - 0s - loss: 1.0148 - acc: 0.6667 \n", 351 | "Epoch 44/50\n", 352 | "30/30 [==============================] - 0s - loss: 0.9666 - acc: 0.6667 \n", 353 | "Epoch 45/50\n", 354 | "30/30 [==============================] - 0s - loss: 0.5963 - acc: 0.8000 \n", 355 | "Epoch 46/50\n", 356 | "30/30 [==============================] - 0s - loss: 0.7851 - acc: 0.7000 \n", 357 | "Epoch 47/50\n", 358 | "30/30 [==============================] - 0s - loss: 0.5623 - acc: 0.8333 \n", 359 | "Epoch 48/50\n", 360 | "30/30 [==============================] - 0s - loss: 0.7574 - acc: 0.7333 \n", 361 | "Epoch 49/50\n", 362 | "30/30 [==============================] - 0s - loss: 0.4094 - acc: 0.8667 \n", 363 | "Epoch 50/50\n", 364 | "30/30 [==============================] - 0s - loss: 0.6300 - acc: 0.8000 \n", 365 | "Accuracy = 0.93\n" 366 | ] 367 | } 368 | ], 369 | "source": [ 370 | "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", 371 | "# モデル訓練\n", 372 | "model.fit(test_X, test_Y, epochs=50, batch_size=1, verbose=1)\n", 373 | "\n", 374 | "loss, accuracy = model.evaluate(test_X, test_Y, verbose=0)\n", 375 | "print(\"Accuracy = {:.2f}\".format(accuracy))" 376 | ] 377 | }, 378 | { 379 | "cell_type": "code", 380 | "execution_count": null, 381 | "metadata": { 382 | "collapsed": true 383 | }, 384 | "outputs": [], 385 | "source": [] 386 | } 387 | ], 388 | "metadata": { 389 | "kernelspec": { 390 | "display_name": "Python 3", 391 | "language": "python", 392 | "name": "python3" 393 | }, 394 | "language_info": { 395 | "codemirror_mode": { 396 | "name": "ipython", 397 | "version": 3 398 | }, 399 | "file_extension": ".py", 400 | "mimetype": "text/x-python", 401 | "name": "python", 402 | "nbconvert_exporter": "python", 403 | "pygments_lexer": "ipython3", 404 | "version": "3.5.0" 405 | } 406 | }, 407 | "nbformat": 4, 408 | "nbformat_minor": 2 409 | } 410 | --------------------------------------------------------------------------------