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mikef0x OP t1_iv00teb wrote

Reply to comment by BlazeObsidian in [R] Keras image classification high loss by mikef0x

I am beginner at ML. So it would be like this?

model = models.Sequential() model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu'))

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BlazeObsidian t1_iv027v3 wrote

The general idea is to start with larger shapes and proceed to smaller ones. So the network starts off wide and the size of the tensors gradually reduces.

So maybe you can do:-

Conv2D(64) -> MaxPooling -> Conv2D(32) -> MaxPooling -> Conv2D(8) -> MaxPooling -> Dense(relu) [Optional] -> Dense(softmax)

Start with one Conv layer first and see if there are any improvements. Gradually add layers. If adding layers isn't giving you much of an improvement in accuracy, I'd recommend checking your data.

Making the network too deep (too many layers) might result in overfitting. So the network architecture/size is also a hyperparameter that has an optimal value for the dataset. Do update how it goes

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mikef0x OP t1_iv0d8up wrote

model = tf.keras.models.Sequential()model.add(tf.keras.layers.Conv2D(64, (3, 3), input_shape=(124, 124, 3)))model.add(tf.keras.layers.MaxPooling2D((2, 2)))model.add(tf.keras.layers.Conv2D(32, (3, 3)))model.add(tf.keras.layers.MaxPooling2D((2, 2)))model.add(tf.keras.layers.Conv2D(8, (3, 3)))model.add(tf.keras.layers.Flatten())model.add(tf.keras.layers.Dense(4, activation='softmax'))

so, i've done like this. loss is low now but accuracy is to high :D i mean on epoch 10 its around 0.99.

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update: on 20 epoch: accuracy is 1

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BlazeObsidian t1_iv0hlz1 wrote

Haha. It might be overfitting now. How does it perform on the test set ? If the accuracy is > 90 on test set I would think it's a good model.

How does it perform on the test set ? If accuracy is bad on test data, you would have to reduce the Conv layers and see if you can get more data.

Can you post the train vs test loss and accuracy here ?

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