print "loading", discriminator_sequence_filename with open(discriminator_sequence_filename, "r") as f: try: discriminator_params = json.load(f) except Exception as e: raise Exception( "could not load {}".format(discriminator_sequence_filename)) else: config = DiscriminatorParams() config.a = 0 config.b = 1 config.c = 1 config.weight_std = 0.01 config.weight_initializer = "Normal" config.use_weightnorm = False config.nonlinearity = "leaky_relu" config.optimizer = "adam" config.learning_rate = 0.0001 config.momentum = 0.5 config.gradient_clipping = 1 config.weight_decay = 0 discriminator = Sequential() discriminator.add(Linear(None, 128, use_weightnorm=config.use_weightnorm)) discriminator.add(Activation(config.nonlinearity)) # discriminator.add(BatchNormalization(128)) discriminator.add(Linear(None, 128, use_weightnorm=config.use_weightnorm)) discriminator.add(Activation(config.nonlinearity)) # discriminator.add(BatchNormalization(128)) discriminator.add(Linear(None, 1, use_weightnorm=config.use_weightnorm))
discriminator_sequence_filename = args.model_dir + "/discriminator.json" if os.path.isfile(discriminator_sequence_filename): print "loading", discriminator_sequence_filename with open(discriminator_sequence_filename, "r") as f: try: params = json.load(f) except Exception as e: raise Exception( "could not load {}".format(discriminator_sequence_filename)) else: config = DiscriminatorParams() config.weight_init_std = 0.001 config.weight_initializer = "Normal" config.use_weightnorm = False config.nonlinearity = "elu" config.optimizer = "Adam" config.learning_rate = 0.0001 config.momentum = 0.5 config.gradient_clipping = 10 config.weight_decay = 0 config.use_feature_matching = False config.use_minibatch_discrimination = False discriminator = Sequential(weight_initializer=config.weight_initializer, weight_init_std=config.weight_init_std) discriminator.add(gaussian_noise(std=0.3)) discriminator.add( Convolution2D(3, 32, ksize=4,
if os.path.isfile(discriminator_sequence_filename): print "loading", discriminator_sequence_filename with open(discriminator_sequence_filename, "r") as f: try: params = json.load(f) except Exception as e: raise Exception( "could not load {}".format(discriminator_sequence_filename)) else: config = DiscriminatorParams() config.ndim_input = image_width * image_height config.ndim_output = 10 config.weight_init_std = 1 config.weight_initializer = "GlorotNormal" config.use_weightnorm = False config.nonlinearity = "softplus" config.optimizer = "Adam" config.learning_rate = 0.001 config.momentum = 0.5 config.gradient_clipping = 10 config.weight_decay = 0 config.use_feature_matching = False config.use_minibatch_discrimination = False discriminator = Sequential(weight_initializer=config.weight_initializer, weight_init_std=config.weight_init_std) discriminator.add(gaussian_noise(std=0.3)) discriminator.add( Linear(config.ndim_input, 1000, use_weightnorm=config.use_weightnorm)) discriminator.add(gaussian_noise(std=0.5)) discriminator.add(Activation(config.nonlinearity))