# Save the model
###############################################################################
cnn.save_model_json(model_export_path, 'model_json')
cnn.save_model_weights(model_export_path, 'model_weights')
cnn.save_model_single_file(model_export_path, 'model_single')
#%%############################################################################
# Load the model
###############################################################################
if load_model == True:
import_path = os.path.join(os.getcwd(), 'model_export',
'2019-06-12_21-46-36')
#cnn.load_model_single_file(import_path, 'model_single')
cnn.load_model_json(import_path, 'model_json', 'model_weights')
#%%############################################################################
# Predict some data
###############################################################################
rand_int = np.random.randint(low=0, high=np.size(X_test_data, axis=0))
X_test = X_test_data[rand_int, ]
y_test = y_test_data[rand_int, ]
y_pred = cnn.predict_sample(X_test)
plt_slice = 8
if border == None:
plt.imshow(X_test[8, :, :])
else:
X_inner = impro.get_inner_slice(X_test, border)
#path_to_spheroid = os.path.join('..', '..', '..', 'Datensaetze', 'Aufnahmen_und_Segmentierungen', 'Datensatz2', category, spheroid_name)
category = 'none'
spheroid_name = 'X_scaled.nrrd'
path_to_spheroid = os.path.join('..', '..', '..', 'Datensaetze',
'OpenSegSPIM_Beispieldaten', 'Neurosphere',
spheroid_name)
#path_to_spheroid = os.path.join('Skalierung', 'NPC1', 'C3-2-1_1-3_upper.nrrd')
#%%############################################################################
# Initialize the CNN
###############################################################################
cnn = CNN(linear_output_scaling_factor=linear_output_scaling_factor,
standardization_mode=standardization_mode)
cnn.load_model_json(model_import_path, 'model_json', 'best_weights')
#%%############################################################################
# Predict the density-map
###############################################################################
spheroid_new, density_map, num_of_cells = cnn.predict_density_map(
path_to_spheroid=path_to_spheroid,
patch_sizes=patch_sizes,
strides=strides,
border=cut_border,
padding=padding)
plt.figure()
plt.imshow(spheroid_new[int(spheroid_new.shape[0] / 2), ])
plt.figure()
plt.imshow(density_map[int(density_map.shape[0] / 2), ])
print('Number of cells = ', num_of_cells)
train_spheroids=test_spheroids,
val_spheroids=test_spheroids,
test_spheroids=test_spheroids)
# Load unstandardized test data
X_test_data, y_test_data = datatools.load_data(path_to_dataset=path_to_dataset,
data_list=test_files,
input_shape=data_shape,
standardization_mode=None,
border=border)
###############################################################################
# Load the model
###############################################################################
#cnn.load_model_single_file(import_path, 'model_single')
cnn = CNN(linear_output_scaling_factor=linear_output_scaling_factor,
standardization_mode=standardization_mode)
cnn.load_model_json(model_import_path, 'model_json', weights)
# Evaluate the model on the test data
summary = cnn.compile_model(loss=loss, optimizer=optimizer, metrics=metrics)
test_loss_best_weights = cnn.evaluate_model(X_test=X_test_data,
y_test=y_test_data,
batch_size=batch_size)
print(test_loss_best_weights)
# Export the value of the test-loss
test_loss_export_path = os.path.join('test_loss_best_weights.txt')
with open(test_loss_export_path, 'w') as file:
for l in range(len(test_loss_best_weights)):
file.write(str(test_loss_best_weights[l]) + '\n')