Data Science Asked on March 9, 2021
ubuntu – 20.04
tensorflow 2.2
dataset used = MNIST
I am testing tensorflow and i notice that validation sparse_categorical_accuracy (accuracy) and validation SparseCategoricalCrossentropy (loss) both are increasing together which, does not make sense to me. I think the validation loss should be going down and validation accuracy increasing as the training progresses. Or, incase of overfitting, validation loss increasing and validation accuracy going down. But, validation loss and validation accuracy both are increasing as the training progresses. The training schedule however, is progressing according to expectation i.e training loss going down and training accuracy going up
Here is the code and the output:
#testing without preprocess monsoon
import tensorflow as tf
from tensorflow import keras as k
from tensorflow.keras import layers as l
import tensorflow_addons as tfa
mnist = tf.keras.datasets.mnist
(x_t,y_t),(x_te,y_te) = mnist.load_data()
x_t = x_t.reshape(60000,-1)
x_te = x_te.reshape(10000,-1)
d_x_t = tf.data.Dataset.from_tensor_slices(x_t)
d_y_t = tf.data.Dataset.from_tensor_slices(y_t)
dataset = tf.data.Dataset.zip((d_x_t,d_y_t)).shuffle(1000).batch(32)
d_x_te = tf.data.Dataset.from_tensor_slices(x_te)
d_y_te = tf.data.Dataset.from_tensor_slices(y_te)
dataset_test = tf.data.Dataset.zip((d_x_te,d_y_te)).shuffle(1000,seed=42).batch(32)
inp = k.Input((784,))
x = l.BatchNormalization()(inp)
x1 = l.Dense(1024,activation='relu',name='dense_1')(x)
x1=l.Dropout(0.5)(x1)
x1 = l.BatchNormalization()(x1)
x2 = l.Dense(512,activation='relu',name='dense_2')(x1)
x3 = l.Dense(512,activation='relu',name='dense_3')(x)
x = x3+x2
x=l.Dropout(0.5)(x)
x = l.BatchNormalization()(x)
x = l.Dense(10,activation='relu',name='dense_4')(x)
predictions = l.Dense(10,activation=None,name='preds')(x)
model = k.Model(inputs=inp,outputs=predictions)
opt=tfa.optimizers.MovingAverage(
k.optimizers.Adam(),
True,
0.99,
None,
'MovingAverage',
clipnorm=5
)
model.compile(optimizer=opt,
loss=k.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['sparse_categorical_accuracy'])
print('# Fit model on training data')
history = model.fit(dataset,
epochs=30,
steps_per_epoch=1875,
validation_data = dataset_test,
validation_steps = 313)
print('nhistory dict:', history.history)
model.evaluate(dataset_test,batch_size=32,steps=331)
The learning evolution that i am getting is:
# Fit model on training data
Epoch 1/30
WARNING:tensorflow:From /home/nitin/anaconda3/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
1875/1875 [==============================] - 49s 26ms/step - loss: 0.3614 - sparse_categorical_accuracy: 0.8913 - val_loss: 0.3355 - val_sparse_categorical_accuracy: 0.9548
Epoch 2/30
1875/1875 [==============================] - 49s 26ms/step - loss: 0.1899 - sparse_categorical_accuracy: 0.9427 - val_loss: 1.2028 - val_sparse_categorical_accuracy: 0.9641
Epoch 3/30
1875/1875 [==============================] - 51s 27ms/step - loss: 0.1546 - sparse_categorical_accuracy: 0.9521 - val_loss: 1.6385 - val_sparse_categorical_accuracy: 0.9673
Epoch 4/30
1875/1875 [==============================] - 38s 20ms/step - loss: 0.1357 - sparse_categorical_accuracy: 0.9585 - val_loss: 2.8285 - val_sparse_categorical_accuracy: 0.9697
Epoch 5/30
1875/1875 [==============================] - 38s 20ms/step - loss: 0.1253 - sparse_categorical_accuracy: 0.9608 - val_loss: 3.8489 - val_sparse_categorical_accuracy: 0.9697
Epoch 6/30
1875/1875 [==============================] - 29s 16ms/step - loss: 0.1149 - sparse_categorical_accuracy: 0.9646 - val_loss: 2.1872 - val_sparse_categorical_accuracy: 0.9699
Epoch 7/30
1875/1875 [==============================] - 29s 16ms/step - loss: 0.1094 - sparse_categorical_accuracy: 0.9646 - val_loss: 2.9429 - val_sparse_categorical_accuracy: 0.9695
Epoch 8/30
1875/1875 [==============================] - 29s 16ms/step - loss: 0.1066 - sparse_categorical_accuracy: 0.9667 - val_loss: 5.6166 - val_sparse_categorical_accuracy: 0.9710
Epoch 9/30
1875/1875 [==============================] - 30s 16ms/step - loss: 0.0991 - sparse_categorical_accuracy: 0.9688 - val_loss: 3.9547 - val_sparse_categorical_accuracy: 0.9710
Epoch 10/30
1875/1875 [==============================] - 29s 16ms/step - loss: 0.0948 - sparse_categorical_accuracy: 0.9701 - val_loss: 4.8149 - val_sparse_categorical_accuracy: 0.9713
Epoch 11/30
1875/1875 [==============================] - 29s 16ms/step - loss: 0.0850 - sparse_categorical_accuracy: 0.9727 - val_loss: 7.4974 - val_sparse_categorical_accuracy: 0.9712
Epoch 12/30
1875/1875 [==============================] - 29s 16ms/step - loss: 0.0879 - sparse_categorical_accuracy: 0.9719 - val_loss: 4.3669 - val_sparse_categorical_accuracy: 0.9714
Epoch 13/30
1875/1875 [==============================] - 30s 16ms/step - loss: 0.0817 - sparse_categorical_accuracy: 0.9743 - val_loss: 9.2499 - val_sparse_categorical_accuracy: 0.9725
Epoch 14/30
1875/1875 [==============================] - 30s 16ms/step - loss: 0.0805 - sparse_categorical_accuracy: 0.9737 - val_loss: 7.5436 - val_sparse_categorical_accuracy: 0.9716
Epoch 15/30
1875/1875 [==============================] - 30s 16ms/step - loss: 0.0798 - sparse_categorical_accuracy: 0.9751 - val_loss: 14.2331 - val_sparse_categorical_accuracy: 0.9712
Epoch 16/30
1875/1875 [==============================] - 29s 16ms/step - loss: 0.0745 - sparse_categorical_accuracy: 0.9757 - val_loss: 7.9517 - val_sparse_categorical_accuracy: 0.9715
Epoch 17/30
1875/1875 [==============================] - 30s 16ms/step - loss: 0.0745 - sparse_categorical_accuracy: 0.9761 - val_loss: 7.9719 - val_sparse_categorical_accuracy: 0.9702
Epoch 18/30
1875/1875 [==============================] - 30s 16ms/step - loss: 0.0741 - sparse_categorical_accuracy: 0.9763 - val_loss: 13.8696 - val_sparse_categorical_accuracy: 0.9665
Epoch 19/30
1875/1875 [==============================] - 30s 16ms/step - loss: 0.0728 - sparse_categorical_accuracy: 0.9760 - val_loss: 20.2949 - val_sparse_categorical_accuracy: 0.9688
Epoch 20/30
1875/1875 [==============================] - 45s 24ms/step - loss: 0.0699 - sparse_categorical_accuracy: 0.9775 - val_loss: 8.8696 - val_sparse_categorical_accuracy: 0.9713
Epoch 21/30
1875/1875 [==============================] - 29s 16ms/step - loss: 0.0699 - sparse_categorical_accuracy: 0.9777 - val_loss: 12.9682 - val_sparse_categorical_accuracy: 0.9723
Epoch 22/30
1875/1875 [==============================] - 30s 16ms/step - loss: 0.0674 - sparse_categorical_accuracy: 0.9781 - val_loss: 61.1677 - val_sparse_categorical_accuracy: 0.9692
Epoch 23/30
1875/1875 [==============================] - 30s 16ms/step - loss: 0.0651 - sparse_categorical_accuracy: 0.9798 - val_loss: 21.3270 - val_sparse_categorical_accuracy: 0.9697
Epoch 24/30
1875/1875 [==============================] - 31s 16ms/step - loss: 0.0624 - sparse_categorical_accuracy: 0.9800 - val_loss: 62.2778 - val_sparse_categorical_accuracy: 0.9685
Epoch 25/30
1875/1875 [==============================] - 30s 16ms/step - loss: 0.0665 - sparse_categorical_accuracy: 0.9792 - val_loss: 24.9327 - val_sparse_categorical_accuracy: 0.9687
Epoch 26/30
1875/1875 [==============================] - 46s 24ms/step - loss: 0.0605 - sparse_categorical_accuracy: 0.9805 - val_loss: 42.0141 - val_sparse_categorical_accuracy: 0.9700
Epoch 27/30
1875/1875 [==============================] - 29s 16ms/step - loss: 0.0601 - sparse_categorical_accuracy: 0.9806 - val_loss: 54.8586 - val_sparse_categorical_accuracy: 0.9695
Epoch 28/30
1875/1875 [==============================] - 30s 16ms/step - loss: 0.0583 - sparse_categorical_accuracy: 0.9811 - val_loss: 25.3613 - val_sparse_categorical_accuracy: 0.9680
Epoch 29/30
1875/1875 [==============================] - 29s 16ms/step - loss: 0.0576 - sparse_categorical_accuracy: 0.9811 - val_loss: 23.2299 - val_sparse_categorical_accuracy: 0.9710
Epoch 30/30
1875/1875 [==============================] - 30s 16ms/step - loss: 0.0566 - sparse_categorical_accuracy: 0.9817 - val_loss: 16.5671 - val_sparse_categorical_accuracy: 0.9728
history dict: {'loss': [0.36135926842689514, 0.1898646354675293, 0.15456895530223846, 0.13569727540016174, 0.12525275349617004, 0.1148592159152031, 0.10943067818880081, 0.1066298857331276, 0.09912335127592087, 0.09476170688867569, 0.08501157909631729, 0.0879492461681366, 0.08170024305582047, 0.08047273010015488, 0.07976552098989487, 0.07453753799200058, 0.07450901716947556, 0.07413797080516815, 0.07278618961572647, 0.0698995441198349, 0.06988336145877838, 0.06740442663431168, 0.06507138162851334, 0.06242847815155983, 0.0665266141295433, 0.06050613150000572, 0.06005210056900978, 0.05830719694495201, 0.05763527378439903, 0.05664650723338127], 'sparse_categorical_accuracy': [0.8913000226020813, 0.9427499771118164, 0.9521499872207642, 0.9585333466529846, 0.9607999920845032, 0.9645500183105469, 0.9645666480064392, 0.9666833281517029, 0.9687666893005371, 0.9701166749000549, 0.9726999998092651, 0.9719499945640564, 0.9742666482925415, 0.9736999869346619, 0.9750999808311462, 0.9757000207901001, 0.9760833382606506, 0.9763166904449463, 0.9759833216667175, 0.977483332157135, 0.9777166843414307, 0.9780833125114441, 0.9798333048820496, 0.9800000190734863, 0.9792333245277405, 0.9805499911308289, 0.9805999994277954, 0.9810666441917419, 0.9810666441917419, 0.9816833138465881], 'val_loss': [0.33551061153411865, 1.2028071880340576, 1.6384732723236084, 2.828489065170288, 3.8488738536834717, 2.187160015106201, 2.9428975582122803, 5.6166462898254395, 3.954725503921509, 4.814915657043457, 7.4974141120910645, 4.366909503936768, 9.24986457824707, 7.543578147888184, 14.233136177062988, 7.951717853546143, 7.971870422363281, 13.869564056396484, 20.29490089416504, 8.869643211364746, 12.968180656433105, 61.167701721191406, 21.327049255371094, 62.27778625488281, 24.932708740234375, 42.01411437988281, 54.85857009887695, 25.361297607421875, 23.229896545410156, 16.56712532043457], 'val_sparse_categorical_accuracy': [0.954800009727478, 0.9641000032424927, 0.9672999978065491, 0.9696999788284302, 0.9696999788284302, 0.9699000120162964, 0.9695000052452087, 0.9710000157356262, 0.9710000157356262, 0.9713000059127808, 0.9711999893188477, 0.9714000225067139, 0.9725000262260437, 0.9715999960899353, 0.9711999893188477, 0.9714999794960022, 0.9702000021934509, 0.9664999842643738, 0.9688000082969666, 0.9713000059127808, 0.9722999930381775, 0.9692000150680542, 0.9696999788284302, 0.968500018119812, 0.9686999917030334, 0.9700000286102295, 0.9695000052452087, 0.9679999947547913, 0.9710000157356262, 0.9728000164031982]}
302/331 [==========================>...] - ETA: 0s - loss: 17.1192 - sparse_categorical_accuracy: 0.9725WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 331 batches). You may need to use the repeat() function when building your dataset.
313/331 [===========================>..] - 1s 3ms/step - loss: 16.5671 - sparse_categorical_accuracy: 0.9728
[16.567113876342773, 0.9728000164031982]
```
Usually this is due to a learning rate that is too high, it passes over the Loss function minimum and starts overshooting. Of course I can't be sure that's the reason but this is my best guess.
Try to simplify your optimizer, use Adam() optimizer alone (without moving average) and set a fairly small learning rate, something like 0.001 or even 0.0001. Let's see how it goes and let us know.
Answered by Leevo on March 9, 2021
You're just overfitting here. That's a fairly complex network for the simple MNIST data set. It's fairly easy to separate the MNIST classes, so even though your overfit network is starting to do worse on the validation set - it's getting less certain about the correct answers, believing the wrong ones more - the most-probable class is still almost always right. Accuracy is a function of the largest probability; loss is a function of them all. I would use val_loss here to decide to stop training earlier with early stopping.
Answered by Sean Owen on March 9, 2021
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