質問編集履歴
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大幅に質問内容を変更しました
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現在は,時系列データを1パターンだけ学習させ,テストデータを与えるところまでは出ています.
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具体的には,時系列データのdatファイルが30個あり,15個を学習データ,15個をテストデータとして用いたいのです.
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###該当のソースコード
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```python
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import numpy as np
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import matplotlib.pyplot as plt
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from keras.models import Sequential
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from keras.layers.core import Dense, Activation
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from keras.layers.recurrent import GRU
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from keras.optimizers import Adam
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from keras.callbacks import EarlyStopping
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from sklearn.model_selection import train_test_split
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import os
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np.random.seed(0)
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def zscore(x, axis = None):
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xmean = x.mean(axis=axis, keepdims=True)
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xstd = np.std(x, axis=axis, keepdims=True)
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zscore = (x-xmean)/xstd
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return zscore
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def read(N=10, T=200):
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a=np.loadtxt('pre10.dat',delimiter=' ',usecols=0)
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b=np.loadtxt('pre10.dat',delimiter=' ',usecols=3)
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c=np.loadtxt('pre10.dat',delimiter=' ',usecols=1)
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d=np.loadtxt('pre10.dat',delimiter=' ',usecols=2)
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a=zscore(a)
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c=zscore(c)
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d=zscore(d)
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signals = np.zeros((N,T))
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for i in range(N):
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signals[i] = a[i]
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sig2=np.zeros((N,T))
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for i in range(N):
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sig2[i] = c[i]
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sig3=np.zeros((N,T))
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for i in range(N):
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sig3[i] = d[i]
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masks = np.zeros((N, T))
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for i in range(N):
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masks[i] = b[i]
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data = np.zeros((N, T, 3))
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data[:, :, 0] = signals[:]
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data[:, :, 1] = sig2[:]
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data[:, :, 2] = sig3[:]
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target = np.zeros((N,T))
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for i in range(N):
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target[i]=b[i]
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return (data, target)
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def read_test(N=10, T=200):
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a=np.loadtxt('pre14.dat',delimiter=' ',usecols=0)
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c=np.loadtxt('pre14.dat',delimiter=' ',usecols=1)
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d=np.loadtxt('pre14.dat',delimiter=' ',usecols=2)
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a=zscore(a)
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c=zscore(c)
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d=zscore(d)
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signals = np.zeros((N,T))
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for i in range(N):
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signals[i] = a[i]
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sig2=np.zeros((N,T))
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for i in range(N):
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sig2[i] = c[i]
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sig3=np.zeros((N,T))
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for i in range(N):
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sig3[i] = d[i]
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data = np.zeros((N, T, 3))
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data[:, :, 0] = signals[:]
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data[:, :, 1] = sig2[:]
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data[:, :, 2] = sig3[:]
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return (data)
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'''
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モデルファイル用設定
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'''
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MODEL_DIR = os.path.join(os.path.dirname(__file__), 'model')
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if os.path.exists(MODEL_DIR) is False:
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os.mkdir(MODEL_DIR)
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'''
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データの読み取り
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'''
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N = 13999
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T = 200
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maxlen = T
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X, Y = read(N=N, T=T)
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X_test=read_test(N=N,T=T)
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N_train = int(N * 0.9)
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N_validation = N - N_train
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'''
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モデル設定
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'''
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n_in = len(X[0][0]) # 2
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n_hidden = 100
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n_out = len(Y[0]) # 1
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def weight_variable(shape, name=None):
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return np.random.normal(scale=.01, size=shape)
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early_stopping = EarlyStopping(monitor='loss', patience=100, verbose=1)
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kernel=weight_variable
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kernel2=weight_variable
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model = Sequential()
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model.add(GRU(n_hidden,
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#kernel_initializer=kernel,
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input_shape=(maxlen, n_in)))
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model.add(Dense(n_out))
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model.add(Activation('linear'))
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optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999)
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model.compile(loss='mean_squared_error',
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optimizer=optimizer)
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'''
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モデル学習
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'''
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epochs = 1000
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batch_size = 100
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hist = model.fit(X, Y,
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batch_size=batch_size,
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epochs=epochs,
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callbacks=[early_stopping])
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model.save(MODEL_DIR+'/model_relu.hdf5')
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print('Model saved')
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'''
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学習の進み具合を可視化
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'''
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loss = hist.history['loss']
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plt.figure(1)
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plt.rc('font', family='serif')
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plt.plot(range(len(loss)), loss, label='loss', color='black')
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plt.xlabel('epochs')
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predicted = model.predict(X_test)
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A = np.loadtxt('pre14.dat',delimiter=' ',usecols=0)
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A=zscore(A)
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plt.figure(2)
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plt.ylim([-3, 3])
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plt.plot(A, color='#aaaaaa')
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#plt.plot(B,color='black' )
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plt.plot(predicted, color='red')
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plt.show()
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#plt.savefig(__file__ + '.eps')
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```
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###試したこと
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https://teratail.com/questions/105989 で回答を頂いたように,
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```
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dat_paths = glob.glob("pre*.dat")
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a = [np.loadtxt(dat_path,delimiter=' ',usecols=0)for dat_path in dat_paths]
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b = [np.loadtxt(dat_path,delimiter=' ',usecols=3)for dat_path in dat_paths]
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c = [np.loadtxt(dat_path,delimiter=' ',usecols=1)for dat_path in dat_paths]
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d = [np.loadtxt(dat_path,delimiter=' ',usecols=2)for dat_path in dat_paths]
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```
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上記のように読み込み関数を書き換えたのですが,これだけではそれ以降の処理でエラーが出るのと,なにより複数サンプル学習させる方法として正しいのかわかりません.
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ちなみに,他にも少し書き換えているのでコードの全容を載せておきます
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```
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import numpy as np
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import matplotlib.pyplot as plt
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from keras.models import Sequential
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from keras.layers.core import Dense, Activation
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from keras.layers.recurrent import GRU
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from keras.optimizers import Adam
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from keras.callbacks import EarlyStopping
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from sklearn.model_selection import train_test_split
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from sklearn.utils import shuffle
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import os
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import glob
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np.random.seed(0)
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def zscore(x, axis = None):
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xmean = x.mean(axis=axis, keepdims=True)
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xstd = np.std(x, axis=axis, keepdims=True)
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zscore = (x-xmean)/xstd
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return zscore
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def read(N=10, T=200):
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dat_paths = glob.glob("pre*.dat")
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A = [np.loadtxt(dat_path,delimiter=' ',usecols=0)for dat_path in dat_paths]
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b = [np.loadtxt(dat_path,delimiter=' ',usecols=3)for dat_path in dat_paths]
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C = [np.loadtxt(dat_path,delimiter=' ',usecols=1)for dat_path in dat_paths]
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D = [np.loadtxt(dat_path,delimiter=' ',usecols=2)for dat_path in dat_paths]
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#a=np.loadtxt('*.dat',delimiter=' ',usecols=0)
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#b=np.loadtxt('*.dat',delimiter=' ',usecols=3)
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#c=np.loadtxt('*.dat',delimiter=' ',usecols=1)
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#d=np.loadtxt('*.dat',delimiter=' ',usecols=2)
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signals = np.zeros((N,T))
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for i in range(N):
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signals[i] = a[i]
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sig2=np.zeros((N,T))
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for i in range(N):
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sig2[i] = c[i]
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sig3=np.zeros((N,T))
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for i in range(N):
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sig3[i] = d[i]
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masks = np.zeros((N, T))
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for i in range(N):
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masks[i] = b[i]
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data = np.zeros((N, T, 3))
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data[:, :, 0] = signals[:]
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data[:, :, 1] = sig2[:]
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data[:, :, 2] = sig3[:]
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target = np.zeros((N,T))
|
464
|
-
|
465
|
-
for i in range(N):
|
466
|
-
|
467
|
-
target[i]=b[i]
|
468
|
-
|
469
|
-
return (data, target)
|
470
|
-
|
471
|
-
|
472
|
-
|
473
|
-
|
474
|
-
|
475
|
-
'''
|
476
|
-
|
477
|
-
モデルファイル用設定
|
478
|
-
|
479
|
-
'''
|
480
|
-
|
481
|
-
MODEL_DIR = os.path.join(os.path.dirname(__file__), 'model')
|
482
|
-
|
483
|
-
|
484
|
-
|
485
|
-
if os.path.exists(MODEL_DIR) is False:
|
486
|
-
|
487
|
-
os.mkdir(MODEL_DIR)
|
488
|
-
|
489
|
-
|
490
|
-
|
491
|
-
|
492
|
-
|
493
|
-
|
494
|
-
|
495
|
-
'''
|
496
|
-
|
497
|
-
データの読み取り
|
498
|
-
|
499
|
-
'''
|
500
|
-
|
501
|
-
N = 500000
|
502
|
-
|
503
|
-
T = 200
|
504
|
-
|
505
|
-
maxlen = T
|
506
|
-
|
507
|
-
|
508
|
-
|
509
|
-
X, Y = read(N=N, T=T)
|
510
|
-
|
511
|
-
N_train = int(N * 0.9)
|
512
|
-
|
513
|
-
N_validation = N - N_train
|
514
|
-
|
515
|
-
|
516
|
-
|
517
|
-
X_train,X_test,Y_train,Y_test=\
|
518
|
-
|
519
|
-
train_test_split(X,Y,train_size=train_size)
|
520
|
-
|
521
|
-
X_train,X_validation,Y_train,Y_validation= \
|
522
|
-
|
523
|
-
train_test_split(X_train,Y_train,train_size=N_validation)
|
524
|
-
|
525
|
-
|
526
|
-
|
527
|
-
'''
|
528
|
-
|
529
|
-
モデル設定
|
530
|
-
|
531
|
-
'''
|
532
|
-
|
533
|
-
n_in = len(X[0][0]) # 2
|
534
|
-
|
535
|
-
n_hidden = 100
|
536
|
-
|
537
|
-
n_out = len(Y[0]) # 1
|
538
|
-
|
539
|
-
|
540
|
-
|
541
|
-
|
542
|
-
|
543
|
-
def weight_variable(shape, name=None):
|
544
|
-
|
545
|
-
return np.random.normal(scale=.01, size=shape)
|
546
|
-
|
547
|
-
|
548
|
-
|
549
|
-
|
550
|
-
|
551
|
-
early_stopping = EarlyStopping(monitor='loss', patience=100, verbose=1)
|
552
|
-
|
553
|
-
|
554
|
-
|
555
|
-
kernel=weight_variable
|
556
|
-
|
557
|
-
kernel2=weight_variable
|
558
|
-
|
559
|
-
|
560
|
-
|
561
|
-
model = Sequential()
|
562
|
-
|
563
|
-
model.add(GRU(n_hidden,
|
564
|
-
|
565
|
-
#kernel_initializer=kernel,
|
566
|
-
|
567
|
-
input_shape=(maxlen, n_in)))
|
568
|
-
|
569
|
-
model.add(Dense(n_out))
|
570
|
-
|
571
|
-
model.add(Activation('linear'))
|
572
|
-
|
573
|
-
|
574
|
-
|
575
|
-
optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999)
|
576
|
-
|
577
|
-
model.compile(loss='mean_squared_error',
|
578
|
-
|
579
|
-
optimizer=optimizer)
|
580
|
-
|
581
|
-
|
582
|
-
|
583
|
-
'''
|
584
|
-
|
585
|
-
モデル学習
|
586
|
-
|
587
|
-
'''
|
588
|
-
|
589
|
-
|
590
|
-
|
591
|
-
epochs = 1000
|
592
|
-
|
593
|
-
batch_size = 100
|
594
|
-
|
595
|
-
|
596
|
-
|
597
|
-
hist = model.fit(X_train, Y_train,
|
598
|
-
|
599
|
-
batch_size=batch_size,
|
600
|
-
|
601
|
-
epochs=epochs,
|
602
|
-
|
603
|
-
validation_data=(X_validation,Y_validation),
|
604
|
-
|
605
|
-
callbacks=[early_stopping])
|
606
|
-
|
607
|
-
|
608
|
-
|
609
|
-
model.save(MODEL_DIR+'/model_GRU.hdf5')
|
610
|
-
|
611
|
-
print('Model saved')
|
612
|
-
|
613
|
-
|
614
|
-
|
615
|
-
'''
|
616
|
-
|
617
|
-
学習の進み具合を可視化
|
618
|
-
|
619
|
-
'''
|
620
|
-
|
621
|
-
|
622
|
-
|
623
|
-
|
624
|
-
|
625
|
-
val_loss=hist.history['val_loss']
|
626
|
-
|
627
|
-
val_acc=hist.history['val_acc']
|
628
|
-
|
629
|
-
|
630
|
-
|
631
|
-
loss_and_metrics=model.evaluate(X_test,Y_test)
|
632
|
-
|
633
|
-
print(loss_and_metrics)
|
634
|
-
|
635
|
-
|
636
|
-
|
637
|
-
plt.figure(1)
|
638
|
-
|
639
|
-
plt.plot(range(len(val_acc)),val_acc,label='acc',color='red')
|
640
|
-
|
641
|
-
plt.xlabel('epochs')
|
642
|
-
|
643
|
-
|
644
|
-
|
645
|
-
plt.figure(2)
|
646
|
-
|
647
|
-
plt.plot(range(len(val_loss)),val_loss,label='loss',color='red')
|
648
|
-
|
649
|
-
plt.xlabel('epochs')
|
650
|
-
|
651
|
-
|
652
|
-
|
653
|
-
plt.show()
|
654
|
-
|
655
|
-
|
656
|
-
|
657
|
-
```
|
1
タグの編集
test
CHANGED
File without changes
|
test
CHANGED
File without changes
|