def encode_dataset(model_path, min_std=0.0):
VAE = VariationalAutoencoder(
dim_x=28 * 28, dim_z=50) #Should be consistent with model being loaded
with VAE.session:
VAE.saver.restore(VAE.session, VAE_model_path)
enc_x_lab_mean, enc_x_lab_var = VAE.encode(x_lab)
enc_x_ulab_mean, enc_x_ulab_var = VAE.encode(x_ulab)
enc_x_valid_mean, enc_x_valid_var = VAE.encode(x_valid)
enc_x_test_mean, enc_x_test_var = VAE.encode(x_test)
id_x_keep = np.std(enc_x_ulab_mean, axis=0) > min_std
print(id_x_keep)
# enc_x_lab_mean, enc_x_lab_var = enc_x_lab_mean[ :, id_x_keep ], enc_x_lab_var[ :, id_x_keep ]
# enc_x_ulab_mean, enc_x_ulab_var = enc_x_ulab_mean[ :, id_x_keep ], enc_x_ulab_var[ :, id_x_keep ]
# enc_x_valid_mean, enc_x_valid_var = enc_x_valid_mean[ :, id_x_keep ], enc_x_valid_var[ :, id_x_keep ]
# enc_x_test_mean, enc_x_test_var = enc_x_test_mean[ :, id_x_keep ], enc_x_test_var[ :, id_x_keep ]
# data_lab = np.hstack( [ enc_x_lab_mean, enc_x_lab_var ] )
# data_ulab = np.hstack( [ enc_x_ulab_mean, enc_x_ulab_var ] )
# data_valid = np.hstack( [enc_x_valid_mean, enc_x_valid_var] )
# data_test = np.hstack( [enc_x_test_mean, enc_x_test_var] )
# print(data_lab.shape)
data_lab = np.hstack([enc_x_lab_mean, enc_x_lab_var])
data_ulab = np.hstack([enc_x_ulab_mean, enc_x_ulab_var])
data_valid = np.hstack([enc_x_valid_mean, enc_x_valid_var])
data_test = np.hstack([enc_x_test_mean, enc_x_test_var])
print(data_lab.shape)
return data_lab, data_ulab, data_valid, data_test
def encode_dataset(model_path, x_lab, x_ulab, x_valid, x_test, min_std=0.0):
dim_x = x_lab.shape[1]
VAE = VariationalAutoencoder(
dim_x=dim_x, dim_z=50) #Should be consistent with model being loaded
with VAE.session:
VAE.saver.restore(VAE.session, model_path)
enc_x_lab_mean, enc_x_lab_var = VAE.encode(x_lab)
enc_x_ulab_mean, enc_x_ulab_var = VAE.encode(x_ulab)
enc_x_valid_mean, enc_x_valid_var = VAE.encode(x_valid)
enc_x_test_mean, enc_x_test_var = VAE.encode(x_test)
id_x_keep = np.std(enc_x_ulab_mean, axis=0) > min_std
enc_x_lab_mean, enc_x_lab_var = enc_x_lab_mean[:,
id_x_keep], enc_x_lab_var[:,
id_x_keep]
enc_x_ulab_mean, enc_x_ulab_var = enc_x_ulab_mean[:,
id_x_keep], enc_x_ulab_var[:,
id_x_keep]
enc_x_valid_mean, enc_x_valid_var = enc_x_valid_mean[:,
id_x_keep], enc_x_valid_var[:,
id_x_keep]
enc_x_test_mean, enc_x_test_var = enc_x_test_mean[:,
id_x_keep], enc_x_test_var[:,
id_x_keep]
data_lab = np.hstack([enc_x_lab_mean, enc_x_lab_var])
data_ulab = np.hstack([enc_x_ulab_mean, enc_x_ulab_var])
data_valid = np.hstack([enc_x_valid_mean, enc_x_valid_var])
data_test = np.hstack([enc_x_test_mean, enc_x_test_var])
return data_lab, data_ulab, data_valid, data_test
def encode_dataset( model_path, min_std = 0.0 ):
VAE = VariationalAutoencoder( dim_x = 28*28, dim_z = 50 ) #Should be consistent with model being loaded
with VAE.session:
VAE.saver.restore( VAE.session, VAE_model_path )
enc_x_lab_mean, enc_x_lab_var = VAE.encode( x_lab )
enc_x_ulab_mean, enc_x_ulab_var = VAE.encode( x_ulab )
enc_x_valid_mean, enc_x_valid_var = VAE.encode( x_valid )
enc_x_test_mean, enc_x_test_var = VAE.encode( x_test )
id_x_keep = np.std( enc_x_ulab_mean, axis = 0 ) > min_std
enc_x_lab_mean, enc_x_lab_var = enc_x_lab_mean[ :, id_x_keep ], enc_x_lab_var[ :, id_x_keep ]
enc_x_ulab_mean, enc_x_ulab_var = enc_x_ulab_mean[ :, id_x_keep ], enc_x_ulab_var[ :, id_x_keep ]
enc_x_valid_mean, enc_x_valid_var = enc_x_valid_mean[ :, id_x_keep ], enc_x_valid_var[ :, id_x_keep ]
enc_x_test_mean, enc_x_test_var = enc_x_test_mean[ :, id_x_keep ], enc_x_test_var[ :, id_x_keep ]
data_lab = np.hstack( [ enc_x_lab_mean, enc_x_lab_var ] )
data_ulab = np.hstack( [ enc_x_ulab_mean, enc_x_ulab_var ] )
data_valid = np.hstack( [enc_x_valid_mean, enc_x_valid_var] )
data_test = np.hstack( [enc_x_test_mean, enc_x_test_var] )
return data_lab, data_ulab, data_valid, data_test
