def anom_det(self, data, k):
"""
Calculate the reconstruction loss when evaluating test-data, and plot the most anomalous images.
"""
data_amount = data.shape[0]
flata = np.reshape(data, (data_amount, -1))
recon = np.reshape(self.auto_encoder.predict(data), (data_amount, -1))
anomality = np.zeros((data_amount, ))
# quick safeguard against logarithm bs
e = 1E-8
recon[recon < e] = e
recon[recon > 1. - e] = 1. - e
for data_idx in range(
data_amount
): # Basically cross-entropy but the keras thing was buggy.
anomality[data_idx] = np.sum(
np.log(recon[data_idx, :]) * flata[data_idx, :]) + np.sum(
np.log(1. - recon[data_idx, :]) *
(1. - flata[data_idx, :]))
# keep only most anomalous data
data = data[np.argsort(anomality)[:k]]
# show and push
format_imgs(images=data, show=True)
def reconstruct(self, data, plot=False, file_name: str = None):
rec = self.auto_encoder.predict(data)
if plot:
format_imgs(np.concatenate([data, rec], axis=0),
vert=2,
show=True,
file_name=file_name)
return rec
def generate_random_images(self,
amount,
plot: bool,
file_name: str = None):
data = self.decoder.predict(np.random.randn(amount, self.encoding_dim))
if plot:
format_imgs(images=data[:amount], show=True, file_name=file_name)
return data
def generate_random_images(self,
batch_size: np.int = 144,
show_images=False):
"""
Throw random noise on the generator, show the images.
"""
# throw random noise on the generator and get our generated images from the prediction.
generated_images = self.generator.predict(np.random.uniform(
-1, 1, (batch_size, self.encoding_dim)),
verbose=0)
# Show the images
format_imgs(generated_images, show=show_images)
return generated_images
def anom_det(self, data, k):
data_amount = data.shape[0]
anomality = np.zeros((data_amount,))
samples = self.decoder.predict(np.random.randn(1000, self.encoding_dim))
# quick safeguard against logarithm bs
e = 1e-8
samples[samples < e] = e
samples[samples > 1. - e] = 1. - e
flata = np.reshape(data, (data_amount, -1))
samples = np.reshape(samples, (samples.shape[0], -1))
for idx in range(data_amount): # Reconstruction probability and reconstruction error
anomality[idx] = np.sum(np.mean(np.log(samples), axis=0) * flata[idx, :]) + np.sum(np.mean(np.log(1 - samples), axis=0) * 1 - flata[idx, :])
data = data[np.argsort(anomality)[:k]]
format_imgs(images=data, show=True, file_name='vae-anomalies.png')
return