def autoencoder_sampler(): xsamples = n_choice(xtest, 10) xrep = np.repeat(xsamples, 9, axis=0) xgen = dim_ordering_unfix(autoencoder.predict(xrep)).reshape((10, 9, 3, 32, 32)) xsamples = dim_ordering_unfix(xsamples).reshape((10, 1, 3, 32, 32)) samples = np.concatenate((xsamples, xgen), axis=1) samples = samples.transpose((0, 1, 3, 4, 2)) return samples
def autoencoder_sampler(): xsamples = n_choice(xtest, 10) xrep = np.repeat(xsamples, 9, axis=0) xgen = dim_ordering_unfix(autoencoder.predict(xrep)).reshape( (10, 9, 3, 32, 32)) xsamples = dim_ordering_unfix(xsamples).reshape((10, 1, 3, 32, 32)) samples = np.concatenate((xsamples, xgen), axis=1) samples = samples.transpose((0, 1, 3, 4, 2)) return samples
def fun(): state = np.random.get_state() np.random.seed(0) zsamples = np.random.normal(size=(10 * 10, latent_dim)) np.random.set_state(state) images = dim_ordering_unfix(generator.predict(zsamples)).transpose((0, 2, 3, 1)) return images.reshape((10, 10, 28, 28))
def generator_skampler(latent_dim, generator): zsamples = np.random.normal(size=(10 * 10, latent_dim)) gen = dim_ordering_unfix(generator.predict(zsamples)) return gen.reshape((10, 10, 92, 92))
def generator_sampler(): xpred = dim_ordering_unfix(generator.predict(zsamples)).transpose((0, 2, 3, 1)) return xpred.reshape((10, 10) + xpred.shape[1:])
def fun(): zsamples = np.random.normal(size=(10 * 10, latent_dim)) xpred = dim_ordering_unfix(generator.predict(zsamples)).transpose( (0, 2, 3, 1)) return xpred.reshape((10, 10) + xpred.shape[1:])
def fun(): zsamples = np.random.normal(size=(10 * 10, latent_dim)) gen = dim_ordering_unfix(generator.predict(zsamples)) return gen.reshape((10, 10, 28, 28))
def generator_sampler(): xpred = generator.predict(zsamples) xpred = dim_ordering_unfix(xpred.transpose((0, 2, 3, 1))) return xpred.reshape((10, 10) + xpred.shape[1:])
def generator_sampler(): zsamples = np.random.normal(size=(10 * 10, latent_dim)) return dim_ordering_unfix(generator.predict(zsamples)).transpose( (0, 2, 3, 1)).reshape((10, 10, 32, 32, 3))
def generator_sampler(): zsamples = np.random.normal(size=(10 * 10, latent_dim)) return dim_ordering_unfix(generator.predict(zsamples)).transpose((0, 2, 3, 1)).reshape((10, 10, 32, 32, 3))
def generator_sampler(): images = dim_ordering_unfix(generator.predict(zsamples)).transpose( (0, 2, 3, 1)) images = scale_value( images, [0.0, 1.0]) #rescale tanh output to [0, 1] for display return images.reshape((10, 10, 28, 28))
def fun(): zsamples = np.random.normal(size=(10 * 10, latent_dim)) gen = dim_ordering_unfix(generator.predict(zsamples)) return gen.reshape((10, 10, 28, 28))
def generator_sampler(): xpred = dim_ordering_unfix(generator.predict(zsamples)).transpose((0, 2, 3, 1)) xpred = scale_value(xpred, [0.0, 1.0]) return xpred.reshape((10, 10) + xpred.shape[1:])