I am working on a project in which i am trying to implement transfer learning to classify ECG signals (1-Dimentional). I have a pretrained model with pretty good accuracy, but the model was trained on a different dataset which have an input shape of (4096,12) and output shape (6). I want to fine tune this pre-trained model on my data which have an input shape of (350,5). To do so, i have added some layers before the input of the pretrained model in order to get the shape (4096,12) and added an output dense layer with shape (5). the code of my model is as below:
from tensorflow.keras.layers import Dense,Input,Conv1D, BatchNormalization,
Activation,Flatten,Reshape,Dropout
from tensorflow.keras.models import Model
#layer to get the desired shape for pre-trained model
new_inp = Input(shape=(300,5))
net = Flatten()(new_inp)
net = Dense(1000, activation='relu')(net)
net = Dropout(0.3)(net)
net = Dense(4096, activation='relu')(net)
net = Dropout(0.3)(net)
net = Reshape([4096,1])(net)
net = Conv1D (filters = 64, kernel_size = 11, strides = 1, padding = 'same')(net)
net = BatchNormalization()(net)
net = Activation('relu')(net)
net = Conv1D (filters = 12, kernel_size = 9, strides = 1, padding = 'same')(net)
net = BatchNormalization()(net)
net = Activation('relu')(net)
# pre-trained model
net = mod(net)
# output layer
ll = Dense(4,activation="softmax")(net)
newModel = Model(new_inp, ll)
my training and validation accuracy is not improving... it improved upto 55%. any idea about the problem.
Thank you.
The idea behind transfer learning is that you concatenate new trainable layers to the end of a pre-trained model, freeze the pre-trained layers, and train the new layers. When you add these new layers to the beginning of the pre-trained model and training the whole network, you are essentially overwriting the pre-trained coefficients.
It is possible to add preprocessing layers (or any layer that does not require back-propagation) to the beginning, but you have added a whole DNN.