Training speed on a shallow neural network with a small dataset
My data contains of 1 feature and a label per feature
ie. ["smallBigTest", "toastBob"] <- feature
4 labels ["mix", "small", "big", "medium"]
I have converted my features to numbers based on alphabet
ie.
smallBigTest -> 18, 12, 0, 53, 53, 27, 8, 6, 45, 4, 18, 19
toastBob -> 19, 14, 0, 18, 19, 27, 14, 1, -1, -1, -1, -1
which later on I hot-encoded and reshaped so the final array of features would look like [[hotencoded(18,12,0,53,53,27,8,6,45,4,18,19)], [hotencoded(19,14,0,18,19,27,14,1,-1,-1,-1,-1)]
simply made it into a 2d array from 3d array to match my labels shape,
i have also hot encoded labels
the training data is about 60k lines of text 1.2mb csv file
and here is my model:
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(16, activation=tf.nn.sigmoid))
model.add(tf.keras.layers.Dense(labelsDictSize, activation=tf.nn.softmax))
optimizer = tf.train.GradientDescentOptimizer(0.05)
model.compile(optimizer, loss=tf.losses.softmax_cross_entropy)
model.fit(featuresOneHot,labelsOneHot, steps_per_epoch=dataCount, epochs=5, verbose=1)
I'm new to ML, so I might be doing something completely wrong or completely stupid, I thought though that this amount of data would be fine.
Training on my machine with gtx870m takes an hour per epoch and on google collaboratory around 20-30 minutes per epoch
python tensorflow keras training-data
asked Nov 13 '18 at 22:05
HigeathHigeath
2416
add a comment |
My data contains of 1 feature and a label per feature
ie. ["smallBigTest", "toastBob"] <- feature
4 labels ["mix", "small", "big", "medium"]
I have converted my features to numbers based on alphabet
ie.
smallBigTest -> 18, 12, 0, 53, 53, 27, 8, 6, 45, 4, 18, 19
toastBob -> 19, 14, 0, 18, 19, 27, 14, 1, -1, -1, -1, -1
which later on I hot-encoded and reshaped so the final array of features would look like [[hotencoded(18,12,0,53,53,27,8,6,45,4,18,19)], [hotencoded(19,14,0,18,19,27,14,1,-1,-1,-1,-1)]
simply made it into a 2d array from 3d array to match my labels shape,
i have also hot encoded labels
the training data is about 60k lines of text 1.2mb csv file
and here is my model:
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(16, activation=tf.nn.sigmoid))
model.add(tf.keras.layers.Dense(labelsDictSize, activation=tf.nn.softmax))
optimizer = tf.train.GradientDescentOptimizer(0.05)
model.compile(optimizer, loss=tf.losses.softmax_cross_entropy)
model.fit(featuresOneHot,labelsOneHot, steps_per_epoch=dataCount, epochs=5, verbose=1)
I'm new to ML, so I might be doing something completely wrong or completely stupid, I thought though that this amount of data would be fine.
Training on my machine with gtx870m takes an hour per epoch and on google collaboratory around 20-30 minutes per epoch
python tensorflow keras training-data
asked Nov 13 '18 at 22:05
HigeathHigeath
2416
add a comment |
My data contains of 1 feature and a label per feature
ie. ["smallBigTest", "toastBob"] <- feature
4 labels ["mix", "small", "big", "medium"]
I have converted my features to numbers based on alphabet
ie.
smallBigTest -> 18, 12, 0, 53, 53, 27, 8, 6, 45, 4, 18, 19
toastBob -> 19, 14, 0, 18, 19, 27, 14, 1, -1, -1, -1, -1
which later on I hot-encoded and reshaped so the final array of features would look like [[hotencoded(18,12,0,53,53,27,8,6,45,4,18,19)], [hotencoded(19,14,0,18,19,27,14,1,-1,-1,-1,-1)]
simply made it into a 2d array from 3d array to match my labels shape,
i have also hot encoded labels
the training data is about 60k lines of text 1.2mb csv file
and here is my model:
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(16, activation=tf.nn.sigmoid))
model.add(tf.keras.layers.Dense(labelsDictSize, activation=tf.nn.softmax))
optimizer = tf.train.GradientDescentOptimizer(0.05)
model.compile(optimizer, loss=tf.losses.softmax_cross_entropy)
model.fit(featuresOneHot,labelsOneHot, steps_per_epoch=dataCount, epochs=5, verbose=1)
I'm new to ML, so I might be doing something completely wrong or completely stupid, I thought though that this amount of data would be fine.
Training on my machine with gtx870m takes an hour per epoch and on google collaboratory around 20-30 minutes per epoch
python tensorflow keras training-data
asked Nov 13 '18 at 22:05
HigeathHigeath
2416
My data contains of 1 feature and a label per feature
ie. ["smallBigTest", "toastBob"] <- feature
4 labels ["mix", "small", "big", "medium"]
I have converted my features to numbers based on alphabet
ie.
smallBigTest -> 18, 12, 0, 53, 53, 27, 8, 6, 45, 4, 18, 19
toastBob -> 19, 14, 0, 18, 19, 27, 14, 1, -1, -1, -1, -1
which later on I hot-encoded and reshaped so the final array of features would look like [[hotencoded(18,12,0,53,53,27,8,6,45,4,18,19)], [hotencoded(19,14,0,18,19,27,14,1,-1,-1,-1,-1)]
simply made it into a 2d array from 3d array to match my labels shape,
i have also hot encoded labels
the training data is about 60k lines of text 1.2mb csv file
and here is my model:
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(16, activation=tf.nn.sigmoid))
model.add(tf.keras.layers.Dense(labelsDictSize, activation=tf.nn.softmax))
optimizer = tf.train.GradientDescentOptimizer(0.05)
model.compile(optimizer, loss=tf.losses.softmax_cross_entropy)
model.fit(featuresOneHot,labelsOneHot, steps_per_epoch=dataCount, epochs=5, verbose=1)
I'm new to ML, so I might be doing something completely wrong or completely stupid, I thought though that this amount of data would be fine.
Training on my machine with gtx870m takes an hour per epoch and on google collaboratory around 20-30 minutes per epoch
python tensorflow keras training-data
python tensorflow keras training-data
asked Nov 13 '18 at 22:05
HigeathHigeath
2416
asked Nov 13 '18 at 22:05
HigeathHigeath
2416
asked Nov 13 '18 at 22:05
HigeathHigeath
2416
asked Nov 13 '18 at 22:05
HigeathHigeath
2416
asked Nov 13 '18 at 22:05
HigeathHigeath
2416
2416
add a comment |
add a comment |
1 Answer
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It's not unusual for NLP models to take so much time to train. The only thing I would change about your model to speed up the learning process is changing the optimizer to something that doesn't have a fixed learning rate. That should speed up the process. I would suggest using adam as it's one of the fastest optimizers with good performance.
Just replace
model.compile(optimizer, loss=tf.losses.softmax_cross_entropy)
with
model.compile(optimizer='adam', loss=tf.losses.softmax_cross_entropy)
answered Nov 13 '18 at 22:57
Tadej MagajnaTadej Magajna
1,1201332
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
It's not unusual for NLP models to take so much time to train. The only thing I would change about your model to speed up the learning process is changing the optimizer to something that doesn't have a fixed learning rate. That should speed up the process. I would suggest using adam as it's one of the fastest optimizers with good performance.
Just replace
model.compile(optimizer, loss=tf.losses.softmax_cross_entropy)
with
model.compile(optimizer='adam', loss=tf.losses.softmax_cross_entropy)
answered Nov 13 '18 at 22:57
Tadej MagajnaTadej Magajna
1,1201332
add a comment |
It's not unusual for NLP models to take so much time to train. The only thing I would change about your model to speed up the learning process is changing the optimizer to something that doesn't have a fixed learning rate. That should speed up the process. I would suggest using adam as it's one of the fastest optimizers with good performance.
Just replace
model.compile(optimizer, loss=tf.losses.softmax_cross_entropy)
with
model.compile(optimizer='adam', loss=tf.losses.softmax_cross_entropy)
answered Nov 13 '18 at 22:57
Tadej MagajnaTadej Magajna
1,1201332
add a comment |
It's not unusual for NLP models to take so much time to train. The only thing I would change about your model to speed up the learning process is changing the optimizer to something that doesn't have a fixed learning rate. That should speed up the process. I would suggest using adam as it's one of the fastest optimizers with good performance.
Just replace
model.compile(optimizer, loss=tf.losses.softmax_cross_entropy)
with
model.compile(optimizer='adam', loss=tf.losses.softmax_cross_entropy)
answered Nov 13 '18 at 22:57
Tadej MagajnaTadej Magajna
1,1201332
It's not unusual for NLP models to take so much time to train. The only thing I would change about your model to speed up the learning process is changing the optimizer to something that doesn't have a fixed learning rate. That should speed up the process. I would suggest using adam as it's one of the fastest optimizers with good performance.
Just replace
model.compile(optimizer, loss=tf.losses.softmax_cross_entropy)
with
model.compile(optimizer='adam', loss=tf.losses.softmax_cross_entropy)
answered Nov 13 '18 at 22:57
Tadej MagajnaTadej Magajna
1,1201332
answered Nov 13 '18 at 22:57
Tadej MagajnaTadej Magajna
1,1201332
answered Nov 13 '18 at 22:57
Tadej MagajnaTadej Magajna
1,1201332
answered Nov 13 '18 at 22:57
Tadej MagajnaTadej Magajna
1,1201332
1,1201332
add a comment |
add a comment |
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