How to reduce model file size in ".h5"

Question

I’m using tensorflow and keras 2.8.0 version.

I have the following network:

#defining model
  model=Sequential()
  #adding convolution layer
  model.add(Conv2D(256,(3,3),activation='relu',input_shape=(256,256,3)))
  #adding pooling layer
  model.add(MaxPool2D(2,2))
  #adding fully connected layer
  model.add(Flatten())
  model.add(Dense(100,activation='relu'))
  #adding output layer
  model.add(Dense(len(classes),activation='softmax'))

  #compiling the model
  model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])

  #fitting the model
  model.fit(x_tr,y_tr,epochs=epochs, ) 
  # Alla 12-esima epoca, va a converge a 1
  # batch size è 125 credo, non so il motivo

  #evaluting the model
  loss_value, accuracy = model.evaluate(x_te, y_te)
  #loss_value, accuracy, top_k_accuracy = model.evaluate(x_te, y_te, batch_size=batch_size)
  print("loss_value: " + str(loss_value))
  print("acuracy: " + str(accuracy))

  #predict first 4 images in the test set
  ypred = model.predict(x_te)

The point is that now i’m trying to save the model in ".h5" format but if i train it for 100 epochs or for 1 epochs i will get a 4.61Gb file model.

Why the size of this file is that big?
How can i reduce this model size ?

Asked By: Diego Rando

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Source

Answer 1

General reason: The size of your h5 file is based only on the number of parameters your model has.

After constructing the model add the line model.summary() and look at the number of parameters the model has in general.

Steps to reduce model size: You have a LOT of filters in your conv layer. Since I don’t know what you want to achieve with your model, I would still advise you to seperate the number of filters to different conv layers and add Pooling layers in between. The will scale down the image and will especially reduce the number of parameters for the Flatten layer.
More information on Pooling layers can be found here.

Answered By: Noltibus

Answer 2

What I find out, after 5 months of experience, is that the steps to do in order to reduce the model size, improve the accuracy score and reduce the loss value are the following:

categorize the labels and then change the loss function of the model
normalize data in values in the range [-1,1]
use the dense layer increase the parameters and then the dimension of the model: is not even helpful sometimes. Having more parameters doesn’t mean have more accuracy. In order to find a solution you have to do several try changing the network, using different activation function and optimizer such as SGD or Adam.
Choose good parameters for learning_rate, decay_rate, decay_values and so on. These parameters give you a better or worse result.
Use batch_size = 32 or 64
Use function that load the dataset step by step and not all in one time in RAM because it makes the process slower and is not even needed: if you are using keras/tensorflow then you can use tf.data.Dataset.from_tensor_slices((x, y)).batch(32 , drop_remainder=True) of course it should be done for train,test,validation

Hope that it helps

Answered By: Diego Rando

How to reduce model file size in ".h5"

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