训练中的验证 Keras

Question

I'm very new to Keras and machine learning in general, and am training a model like so:

history = model.fit_generator(flight_generator(train_files_train, 4), steps_per_epoch=500, epochs=50)

Where flight_generator is a function that prepares the training data and formats it, and then yields it back to the model to fit. this works great, so now I want to add some validation and after much looking online I still don't know how to implement it.

My best guess would be something like:

history = model.fit_generator(flight_generator(train_files_train, 4), steps_per_epoch=500, epochs=50, validation_data=flight_generator(train_files_cv, 4))

But when I run the code it just freezes in the first epoch. What am I missing?

EDIT:

Code for flight_generator:

def flight_generator(files, batch_size):

    while True:
          batch_inputs  = numpy.random.choice(a    = files, 
                                          size = batch_size)
          batch_input_X = []
          batch_input_Y = []
          c=0
          for batch_input in batch_inputs:
            # reshape into X=t and Y=t+1
            trainX, trainY = create_dataset(batch_input, look_back)
            # reshape input to be [samples, time steps, features]
            trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))

            if c is 0:
              batch_input_X = trainX
              batch_input_Y = trainY

            else:
              batch_input_X = numpy.concatenate((batch_input_X, trainX), axis = 0)
              batch_input_Y = numpy.concatenate((batch_input_Y, trainY), axis = 0)

            c += 1


          # Return a tuple of (input) to feed the network

          batch_x = numpy.array( batch_input_X )
          batch_y = numpy.array( batch_input_Y )


          yield(batch_x, batch_y)

Answer 1

Your validation_data should be in format of tuple. So you should try changing it:

history = model.fit_generator(flight_generator(train_files_train, 4), steps_per_epoch=500, epochs=50,batch_size=32,validation_data=(flight_generator(train_files_cv, 4)))

Answer 2

I guess you should be using model.fit(........) Do not try to use generator unless you actually require it In whatever code I have seen, model.fit() does the magic

Please refer to Keras documentation for fit() https://keras.io/api/models/sequential/ And please mention the optimizer and the metrics