CODEWITHSUNDEEP
pythontensorflowmachine-learningkerasneural-network
Minh-Long Luu
Minh-Long Luu

Reputation: 2751

Keras: val_loss is increasing and evaluate loss is too high

I'm new to Keras and I'm using it to build a normal Neural Network to classify number MNIST dataset.
Beforehand I have already split the data into 3 parts: 55000 to train, 5000 to evaluate and 10000 to test, and I have scaled the pixel density down (by dividing it by 255.0)
My model looks like this:

model = keras.models.Sequential()
model.add(keras.layers.Flatten(input_shape=[28,28]))
model.add(keras.layers.Dense(100, activation='relu'))
model.add(keras.layers.Dense(10, activation='softmax'))

And here is the compile:

model.compile(loss='sparse_categorical_crossentropy',
              optimizer = 'Adam',
              metrics=['accuracy'])

I train the model:

his = model.fit(xTrain, yTrain, epochs = 20, validation_data=(xValid, yValid))

At first the val_loss decreases, then it increases although the accuracy is increasing.

Train on 55000 samples, validate on 5000 samples
Epoch 1/20
55000/55000 [==============================] - 5s 91us/sample - loss: 0.2822 - accuracy: 0.9199 - val_loss: 0.1471 - val_accuracy: 0.9588
Epoch 2/20
55000/55000 [==============================] - 5s 82us/sample - loss: 0.1274 - accuracy: 0.9626 - val_loss: 0.1011 - val_accuracy: 0.9710
Epoch 3/20
55000/55000 [==============================] - 5s 83us/sample - loss: 0.0899 - accuracy: 0.9734 - val_loss: 0.0939 - val_accuracy: 0.9742
Epoch 4/20
55000/55000 [==============================] - 5s 84us/sample - loss: 0.0674 - accuracy: 0.9796 - val_loss: 0.0760 - val_accuracy: 0.9770
Epoch 5/20
55000/55000 [==============================] - 5s 94us/sample - loss: 0.0541 - accuracy: 0.9836 - val_loss: 0.0842 - val_accuracy: 0.9742

Epoch 15/20
55000/55000 [==============================] - 4s 82us/sample - loss: 0.0103 - accuracy: 0.9967 - val_loss: 0.0963 - val_accuracy: 0.9788
Epoch 16/20
55000/55000 [==============================] - 5s 84us/sample - loss: 0.0092 - accuracy: 0.9973 - val_loss: 0.0956 - val_accuracy: 0.9774
Epoch 17/20
55000/55000 [==============================] - 5s 82us/sample - loss: 0.0081 - accuracy: 0.9977 - val_loss: 0.0977 - val_accuracy: 0.9770
Epoch 18/20
55000/55000 [==============================] - 5s 85us/sample - loss: 0.0076 - accuracy: 0.9977 - val_loss: 0.1057 - val_accuracy: 0.9760
Epoch 19/20
55000/55000 [==============================] - 5s 83us/sample - loss: 0.0063 - accuracy: 0.9980 - val_loss: 0.1108 - val_accuracy: 0.9774
Epoch 20/20
55000/55000 [==============================] - 5s 85us/sample - loss: 0.0066 - accuracy: 0.9980 - val_loss: 0.1056 - val_accuracy: 0.9768

And when I evaluate the loss is too high:

model.evaluate(xTest, yTest)

Result:

10000/10000 [==============================] - 0s 41us/sample - loss: 25.7150 - accuracy: 0.9740
[25.714989705941953, 0.974]

Is this ok, or is it a sign of overfitting? Should I do something to improve it? Thanks in advance.

Upvotes: 0

Views: 5348

Answers (1)

sxeros
sxeros

Reputation: 672

Usually, it is not Ok. You want the loss rate to be as small as possible. Your result is typical for overfitting. Your Network 'knows' its training data, but isn't capable of analysing new Images. You may want to add some layers. Maybe Convolutional Layers, Dropout Layer... another idea would be to augment your training images. The ImageDataGenerator-Class provided by Keras might help you out here

Another thing to look at could be your hyperparameters. Why do you use 100 nodes in the first dense layer? maybe something like 784 (28*28) seems more interesting if you want to start with a dense layer. I would suggest some combination of Convolutional-Dropout-Dense. Then your dense -layer maybe doesn't need that many nodes...

Upvotes: 0

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