Keras LSTM training over multiple sequences
I am new to deep learning and trying to train a NN to predict a sequence Y_n given X_n, where Y_n is a number and X_n is a vector. Assume I have multiple training sequences (X_n, Y_n), each with different lengths, what is the best way of training? Below is my code, would this work or do I need something like reset state? My goal is to update the weights every time I feed a new training sequence, but I do not want the old training to be completely overwritten as well. Thanks.
inputD = len(feature)
model = Sequential()
model.add(LSTM(input_dim=inputD,output_dim=10,return_sequences=True))
model.add(LSTM(10,return_sequences=True))
model.add(Dense(5,activation='relu'))
model.add(Dense(5,activation='relu'))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
for j in range(5):
trainX = df[df['id'] == j][feature].values
trainY = df[df['id'] == j]['Y'].values
trainX = numpy.reshape(trainX, (1, trainX.shape[0], trainX.shape[1]))
trainY = numpy.reshape(trainY, (1, trainY.shape[0], 1))
model.fit(trainX, trainY, epochs=100, batch_size=50,verbose=2)
python tensorflow machine-learning keras lstm
asked Nov 25 '18 at 20:36
physcis_beginnerphyscis_beginner
34
add a comment |
I am new to deep learning and trying to train a NN to predict a sequence Y_n given X_n, where Y_n is a number and X_n is a vector. Assume I have multiple training sequences (X_n, Y_n), each with different lengths, what is the best way of training? Below is my code, would this work or do I need something like reset state? My goal is to update the weights every time I feed a new training sequence, but I do not want the old training to be completely overwritten as well. Thanks.
inputD = len(feature)
model = Sequential()
model.add(LSTM(input_dim=inputD,output_dim=10,return_sequences=True))
model.add(LSTM(10,return_sequences=True))
model.add(Dense(5,activation='relu'))
model.add(Dense(5,activation='relu'))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
for j in range(5):
trainX = df[df['id'] == j][feature].values
trainY = df[df['id'] == j]['Y'].values
trainX = numpy.reshape(trainX, (1, trainX.shape[0], trainX.shape[1]))
trainY = numpy.reshape(trainY, (1, trainY.shape[0], 1))
model.fit(trainX, trainY, epochs=100, batch_size=50,verbose=2)
python tensorflow machine-learning keras lstm
asked Nov 25 '18 at 20:36
physcis_beginnerphyscis_beginner
34
feature is a list of columns names in df
– physcis_beginner
Nov 25 '18 at 20:36
add a comment |
I am new to deep learning and trying to train a NN to predict a sequence Y_n given X_n, where Y_n is a number and X_n is a vector. Assume I have multiple training sequences (X_n, Y_n), each with different lengths, what is the best way of training? Below is my code, would this work or do I need something like reset state? My goal is to update the weights every time I feed a new training sequence, but I do not want the old training to be completely overwritten as well. Thanks.
inputD = len(feature)
model = Sequential()
model.add(LSTM(input_dim=inputD,output_dim=10,return_sequences=True))
model.add(LSTM(10,return_sequences=True))
model.add(Dense(5,activation='relu'))
model.add(Dense(5,activation='relu'))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
for j in range(5):
trainX = df[df['id'] == j][feature].values
trainY = df[df['id'] == j]['Y'].values
trainX = numpy.reshape(trainX, (1, trainX.shape[0], trainX.shape[1]))
trainY = numpy.reshape(trainY, (1, trainY.shape[0], 1))
model.fit(trainX, trainY, epochs=100, batch_size=50,verbose=2)
python tensorflow machine-learning keras lstm
asked Nov 25 '18 at 20:36
physcis_beginnerphyscis_beginner
34
I am new to deep learning and trying to train a NN to predict a sequence Y_n given X_n, where Y_n is a number and X_n is a vector. Assume I have multiple training sequences (X_n, Y_n), each with different lengths, what is the best way of training? Below is my code, would this work or do I need something like reset state? My goal is to update the weights every time I feed a new training sequence, but I do not want the old training to be completely overwritten as well. Thanks.
inputD = len(feature)
model = Sequential()
model.add(LSTM(input_dim=inputD,output_dim=10,return_sequences=True))
model.add(LSTM(10,return_sequences=True))
model.add(Dense(5,activation='relu'))
model.add(Dense(5,activation='relu'))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
for j in range(5):
trainX = df[df['id'] == j][feature].values
trainY = df[df['id'] == j]['Y'].values
trainX = numpy.reshape(trainX, (1, trainX.shape[0], trainX.shape[1]))
trainY = numpy.reshape(trainY, (1, trainY.shape[0], 1))
model.fit(trainX, trainY, epochs=100, batch_size=50,verbose=2)
python tensorflow machine-learning keras lstm
python tensorflow machine-learning keras lstm
asked Nov 25 '18 at 20:36
physcis_beginnerphyscis_beginner
34
asked Nov 25 '18 at 20:36
physcis_beginnerphyscis_beginner
34
edited Nov 26 '18 at 1:48
physcis_beginner
asked Nov 25 '18 at 20:36
physcis_beginnerphyscis_beginner
34
asked Nov 25 '18 at 20:36
physcis_beginnerphyscis_beginner
34
asked Nov 25 '18 at 20:36
physcis_beginnerphyscis_beginner
34
34
feature is a list of columns names in df
– physcis_beginner
Nov 25 '18 at 20:36
add a comment |
feature is a list of columns names in df
– physcis_beginner
Nov 25 '18 at 20:36
feature is a list of columns names in df
– physcis_beginner
Nov 25 '18 at 20:36
feature is a list of columns names in df
– physcis_beginner
Nov 25 '18 at 20:36
add a comment |
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feature is a list of columns names in df
– physcis_beginner
Nov 25 '18 at 20:36