def main():
test_anomaly = load_dataset('anomaly')
test_normal = load_dataset('normal')
model = VAE()
model, loss = model.vae_net()
model.load_weights("weight/vae_model.h5")
anomaly_detector(model, test_normal, test_anomaly)
latent_dim = 124
input_dim = len(int2labels_map) - 1
dropout = .1
maxnorm = None
vae_b1 , vae_b2 = .02 , .1
print('Reinitiating VAE Model')
# Build Model
model = VAE(latent_dim, input_dim, measures, measure_len, dropout,
maxnorm, vae_b1 , vae_b2)
# Reload Saved Weights
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "model_ckpt")
model.load_weights(checkpoint_prefix)
model.build(tf.TensorShape([None, measures, measure_len, ]))
# Print Summary of Model
model.summary()
### Sample Latent Variable Distributions
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Here we use SVD to more effectively sample from the orthogonal components
# of our latent space
# Parameters for sampling
num_songs = 10
print('Generating Latent Samples to Generate {} New Tracks'.format(num_songs))
