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:])
