def unet_run_model(self,
model,
X_in_test,
model_path,
logs_path,
plots_path,
test_result=0,
Y_out_test=0,
y_cop_test=0,
activate_tensorboard=0,
run_id=0,
tl_type='full_fine_tune'):
"""run_train_model function trains the model on the dataset and saves the trained model,logs and plots within the file structure, the function prints the training evaluation metrics
:param model: 3D CNN model compiled within the Deep Learning Class, refer https://keras.io/models/model/ for more information
:type model: keras.models (required)
:param X_in: Train dataset input (predictor variables), 3D Voxel representation of the cloud of point and node deviation data obtained from the VRM software based on the sampling input
:type X_in: numpy.array [samples*voxel_dim*voxel_dim*voxel_dim*deviation_channels] (required)
:param Y_out: Train dataset output (variables to predict), Process Parameters/KCCs obtained from sampling
:type Y_out: numpy.array [samples*assembly_kccs] (required)
:param model_path: model path at which the trained model is saved
:type model_path: str (required)
:param logs_path: logs path where the training metrics file is saved
:type logs_path: str (required)
:param plots_path: plots path where model training loss convergence plot is saved
:type plots_path: str (required)
:param activate_tensorboard: flag to indicate if tensorboard should be added in model callbacks for better visualization, 0 by default, set to 1 to activate tensorboard
:type activate_tensorboard: int
:param run_id: Run id index used in data study to conduct multiple training runs with different dataset sizes, defaults to 0
:type run_id: int
"""
import tensorflow as tf
from tensorflow.keras.models import load_model
import tensorflow.keras.backend as K
#model_file_path=model_path+'/unet_trained_model_'+str(run_id)+'.h5'
model_file_path = model_path + '/unet_trained_model_' + str(run_id)
#tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir='C:\\Users\\sinha_s\\Desktop\\dlmfg_package\\dlmfg\\trained_models\\inner_rf_assembly\\logs',histogram_freq=1)
#inference_model=load_model(model_file_path,custom_objects={'mse_scaled': mse_scaled} )
model.load_weights(model_file_path)
print("Trained Model Weights loaded successfully")
print("Conducting Inference...")
y_pred, y_cop_pred = model.predict(X_in_test)
print("Inference Completed !")
if (test_result == 1):
metrics_eval = MetricsEval()
eval_metrics, accuracy_metrics_df = metrics_eval.metrics_eval_base(
y_pred, Y_out_test, logs_path)
#y_cop_pred_flat=y_cop_pred.flatten()
#y_cop_test_flat=y_cop_test.flatten()
#combined_array=np.stack([y_cop_test_flat,y_cop_pred_flat],axis=1)
#filtered_array=combined_array[np.where(combined_array[:,0] >= 0.05)]
#y_cop_test_vector=filtered_array[:,0:1]
#y_cop_pred_vector=filtered_array[:,1:2]
y_cop_pred_vector = np.reshape(y_cop_pred,
(y_cop_pred.shape[0], -1))
y_cop_test_vector = np.reshape(y_cop_test,
(y_cop_test.shape[0], -1))
y_cop_pred_vector = y_cop_pred_vector.T
y_cop_test_vector = y_cop_test_vector.T
print(y_cop_pred_vector.shape)
#y_cop_test_flat=y_cop_test.flatten()
eval_metrics_cop, accuracy_metrics_df_cop = metrics_eval.metrics_eval_cop(
y_cop_pred_vector, y_cop_test_vector, logs_path)
return y_pred, y_cop_pred, model, eval_metrics, accuracy_metrics_df, eval_metrics_cop, accuracy_metrics_df_cop
return y_pred, y_cop_pred, model
index = 0
for i in range(output_heads):
y_cop_pred = model_outputs[2][:, :, :, :, t:t + 3]
y_cop_test = Y_out_test_list[2][:, :, :, :, t:t + 3]
y_cop_actual = y_cop_test
y_cop_pred_vector = np.reshape(y_cop_pred, (y_cop_pred.shape[0], -1))
y_cop_test_vector = np.reshape(y_cop_test, (y_cop_test.shape[0], -1))
y_cop_pred_vector = y_cop_pred_vector.T
y_cop_test_vector = y_cop_test_vector.T
print(y_cop_pred_vector.shape)
#y_cop_test_flat=y_cop_test.flatten()
eval_metrics_cop, accuracy_metrics_df_cop = metrics_eval.metrics_eval_cop(
y_cop_pred_vector, y_cop_test_vector, logs_path)
eval_metrics_cop_list.append(eval_metrics_cop)
accuracy_metrics_df_cop_list.append(accuracy_metrics_df_cop)
accuracy_metrics_df_cop.to_csv(logs_path + '/metrics_test_cop_' +
str(index) + '.csv')
print("The Model Segmentation Validation Metrics are ")
print(accuracy_metrics_df_cop.mean())
accuracy_metrics_df_cop.mean().to_csv(logs_path +
'/metrics_test_cop_summary_' +
str(index) + '.csv')
#Saving For Matlab Plotting