(self, num_steps, model_load_path, num_test_rec)
Initializes the Adversarial Video Generation Runner.

        @param num_steps: The number of training steps to run.
        @param model_load_path: The path from which to load a previously-saved model.
                                Default = None.
        @param num_test_rec: The number of recursive generations to produce when testing. Recursive
                             generations use previous generations as input to predict further into
                             the future.
Initializes the Adversarial Video Generation Runner.
Initializes the Adversarial Video Generation Runner .
def __init__(self, num_steps, model_load_path, num_test_rec):
        """
        Initializes the Adversarial Video Generation Runner.

        @param num_steps: The number of training steps to run.
        @param model_load_path: The path from which to load a previously-saved model.
                                Default = None.
        @param num_test_rec: The number of recursive generations to produce when testing. Recursive
                             generations use previous generations as input to predict further into
                             the future.
        """

        self.global_step = 0
        self.num_steps = num_steps
        self.num_test_rec = num_test_rec

        self.sess = tf.Session()
        self.summary_writer = tf.train.SummaryWriter(c.SUMMARY_SAVE_DIR, graph=self.sess.graph)

        if c.ADVERSARIAL:
            print 'Init discriminator...'
            self.d_model = DiscriminatorModel(self.sess,
                                              self.summary_writer,
                                              c.TRAIN_HEIGHT,
                                              c.TRAIN_WIDTH,
                                              c.SCALE_CONV_FMS_D,
                                              c.SCALE_KERNEL_SIZES_D,
                                              c.SCALE_FC_LAYER_SIZES_D)

        print 'Init generator...'
        self.g_model = GeneratorModel(self.sess,
                                      self.summary_writer,
                                      c.TRAIN_HEIGHT,
                                      c.TRAIN_WIDTH,
                                      c.FULL_HEIGHT,
                                      c.FULL_WIDTH,
                                      c.SCALE_FMS_G,
                                      c.SCALE_KERNEL_SIZES_G)

        print 'Init variables...'
        self.saver = tf.train.Saver(keep_checkpoint_every_n_hours=2)
        self.sess.run(tf.global_variables_initializer())

        # if load path specified, load a saved model
        if model_load_path is not None:
            self.saver.restore(self.sess, model_load_path)
            print 'Model restored from ' + model_load_path
def __init__ ( self , num_steps , model_load_path , num_test_rec ) : self . global_step = 0 self . num_steps = num_steps self . num_test_rec = num_test_rec self . sess = tf . Session ( ) self . summary_writer = tf . train . SummaryWriter ( c . SUMMARY_SAVE_DIR , graph = self . sess . graph ) if c . ADVERSARIAL : print 'Init discriminator...' self . d_model = DiscriminatorModel ( self . sess , self . summary_writer , c . TRAIN_HEIGHT , c . TRAIN_WIDTH , c . SCALE_CONV_FMS_D , c . SCALE_KERNEL_SIZES_D , c . SCALE_FC_LAYER_SIZES_D ) print 'Init generator...' self . g_model = GeneratorModel ( self . sess , self . summary_writer , c . TRAIN_HEIGHT , c . TRAIN_WIDTH , c . FULL_HEIGHT , c . FULL_WIDTH , c . SCALE_FMS_G , c . SCALE_KERNEL_SIZES_G ) print 'Init variables...' self . saver = tf . train . Saver ( keep_checkpoint_every_n_hours = 2 ) self . sess . run ( tf . global_variables_initializer ( ) ) # if load path specified, load a saved model if model_load_path is not None : self . saver . restore ( self . sess , model_load_path ) print 'Model restored from ' + model_load_path