CNN with LSTM-Layer-CodePudding

I have implemented a CNN with an LSTM layer. My input consists of four images. The images were transformed into a tensor by feature extraction. The input shape is (4,256,256,3).

The following is the structure of my model:

model = keras.models.Sequential()
model.add(TimeDistributed(Conv2D(32,(3,3),padding = 'same', activation = 'relu'),input_shape = (4,256,256,3)))
model.add(TimeDistributed(MaxPooling2D((2,2))))
model.add(TimeDistributed(Dropout(0.25)))


model.add(TimeDistributed(Conv2D(64,(3,3),padding = 'same', activation = 'relu')))
model.add(TimeDistributed(MaxPooling2D((4,4))))
model.add(TimeDistributed(Dropout(0.25)))


model.add(TimeDistributed(Conv2D(128,(3,3),padding = 'same', activation = 'relu')))
model.add(TimeDistributed(MaxPooling2D((2,2))))
model.add(TimeDistributed(Dropout(0.25)))

model.add(TimeDistributed(Flatten()))

model.add(LSTM(128, activation='tanh'))# finalize with standard Dense, Dropout...
model.add(Dense(64, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(1, activation='relu'))

optim = keras.optimizers.Adam(learning_rate=0.001)
model.compile(optimizer=optim, loss=['MSE'])
history = model.fit(x=X, y=Y, batch_size=4, epochs=5, validation_split=0.2, validation_data=(X,Y))

My problem is that my model predicts the same values for all inputs.

What could be the problem?

CodePudding user response：

if you are working with a classification problem specifically binary classification, then use sigmoid activation instead softmax And MSE is loss in not a good choice for binary classification.

CodePudding user response：

you use the same data for training and validation. this kills the whole point of validation. Perhaps the mistake lies in this. Try to split the data, or apply cross validation.

Also, the application of the relu activation function to the last layer in combination with the mse error looks strange. At least the real can give an unlimited result, and the data should be normalized.

I hope this will help you