Before training the CNN network score on the testing data-CodePudding

I have a simple binary image classification CNN network. Below is the code

model = Sequential()

model.add(Conv2D(32, kernel_size=(3,3), padding='same',
                     kernel_initializer=gabor_init, input_shape=(32, 32, 1)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))

model.add(Conv2D(32, kernel_size=(3,3), padding='same', kernel_initializer=gabor_init))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))

model.add(Conv2D(64, kernel_size=(3,3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))


model.add(Flatten())
model.add(Dense(128,activation='relu'))
model.add(Dropout(0.4))

model.add(Dense(2,input_dim=128,activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='rmsprop',metrics=['accuracy'])

model.summary()

from sklearn.model_selection import train_test_split
trainX,testX,trainY,testY=train_test_split(Xdata,Ytarget,test_size=.3)

history=model.fit(trainX,trainY,epochs=70,batch_size = 64, 
                         verbose = 1,validation_split=.3)

print(model.evaluate(testX,testY))

Here I am training the model then validating the model. My question is

I want to check the model on the test data before training; as I am using the Gabor Kernel Initializer, I want to see how this filter works before training. In that case, do I need to add `model.fit()? I am little confused.

Any suggestion or modification for the last part of the code so the model can be tested on test data before training?

CodePudding user response：

After you have defined your model in keras, you are only required to compile it using the model.compile() in order to be able to invoke predictions on the initial untrained weights. model.fit() only updates the weights as the model is trained and does not contribute to any configuration setup.