def load(file):
model = Sequential.load(file)
tf.train.Saver().restore(model.session, file + '_model.ckpt')
return model
config.learning_rate = 0.0001
config.momentum = 0.9
config.gradient_clipping = 1
config.weight_decay = 0
model = Sequential()
model.add(Convolution2D(1, 32, ksize=4, stride=2, pad=1))
model.add(BatchNormalization(32))
model.add(Activation(config.nonlinearity))
model.add(Convolution2D(32, 64, ksize=4, stride=2, pad=1))
model.add(BatchNormalization(64))
model.add(Activation(config.nonlinearity))
model.add(Convolution2D(64, 128, ksize=3, stride=2, pad=1))
model.add(BatchNormalization(128))
model.add(Activation(config.nonlinearity))
model.add(Linear(None, config.num_classes))
params = {
"config": config.to_dict(),
"model": model.to_dict(),
}
with open(sequence_filename, "w") as f:
json.dump(params, f, indent=4, sort_keys=True, separators=(',', ': '))
model = Discriminator(params)
model.load(args.model_dir)
if args.gpu_device != -1:
cuda.get_device(args.gpu_device).use()
model.to_gpu()