dcgan_theano.py 文件源码

def gen_samples(self, z0=None, n=32, batch_size=32, use_transform=True):
        assert n % batch_size == 0

        samples = []

        if z0 is None:
            z0 = np_rng.uniform(-1., 1., size=(n, self.nz))
        else:
            n = len(z0)
            batch_size = max(n, 64)
        n_batches = int(np.ceil(n/float(batch_size)))
        for i in range(n_batches):
            zmb = floatX(z0[batch_size * i:min(n, batch_size * (i + 1)), :])
            xmb = self._gen(zmb)
            samples.append(xmb)

        samples = np.concatenate(samples, axis=0)
        if use_transform:
            samples = self.inverse_transform(samples, npx=self.npx, nc=self.nc)
            samples = (samples * 255).astype(np.uint8)
        return samples