Python utils 模块，batch_generator() 实例源码

def train_model(model, args, X_train, X_valid, y_train, y_valid):
    """
    Train the model
    """
    checkpoint = ModelCheckpoint('model-{epoch:03d}.h5',
                                 monitor='val_loss',
                                 verbose=0,
                                 save_best_only=args.save_best_only,
                                 mode='auto')

    model.compile(loss='mean_squared_error', optimizer=Adam(lr=args.learning_rate))

    model.fit_generator(batch_generator(args.data_dir, X_train, y_train, args.batch_size, True),
                        args.samples_per_epoch,
                        args.nb_epoch,
                        max_q_size=1,
                        validation_data=batch_generator(args.data_dir, X_valid, y_valid, args.batch_size, False),
                        nb_val_samples=len(X_valid),
                        callbacks=[checkpoint],
                        verbose=1)

def train_model(model, args, X_train, X_valid, y_train, y_valid):
    """
    Train the model
    """
    #Saves the model after every epoch.
    #quantity to monitor, verbosity i.e logging mode (0 or 1), 
    #if save_best_only is true the latest best model according to the quantity monitored will not be overwritten.
    #mode: one of {auto, min, max}. If save_best_only=True, the decision to overwrite the current save file is
    # made based on either the maximization or the minimization of the monitored quantity. For val_acc, 
    #this should be max, for val_loss this should be min, etc. In auto mode, the direction is automatically
    # inferred from the name of the monitored quantity.
    checkpoint = ModelCheckpoint('model-{epoch:03d}.h5',
                                 monitor='val_loss',
                                 verbose=0,
                                 save_best_only=args.save_best_only,
                                 mode='auto')

    #calculate the difference between expected steering angle and actual steering angle
    #square the difference
    #add up all those differences for as many data points as we have
    #divide by the number of them
    #that value is our mean squared error! this is what we want to minimize via
    #gradient descent
    model.compile(loss='mean_squared_error', optimizer=Adam(lr=args.learning_rate))

    #Fits the model on data generated batch-by-batch by a Python generator.

    #The generator is run in parallel to the model, for efficiency. 
    #For instance, this allows you to do real-time data augmentation on images on CPU in 
    #parallel to training your model on GPU.
    #so we reshape our data into their appropriate batches and train our model simulatenously
    model.fit_generator(batch_generator(args.data_dir, X_train, y_train, args.batch_size, True),
                        args.samples_per_epoch,
                        args.nb_epoch,
                        max_q_size=1,
                        validation_data=batch_generator(args.data_dir, X_valid, y_valid, args.batch_size, False),
                        nb_val_samples=len(X_valid),
                        callbacks=[checkpoint],
                        verbose=1)

#for command line args