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 ?
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 .
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