model = AlexNet(labels, types_of_poolings=POOLING_OPS, ksizes=None)
dim = (122, 257, 2)
model.build((BATCH_SIZE, *dim))
print(f"Class name: {model.name}")
# tf.keras.utils.plot_model(model.build_graph(), to_file="alexnetstyle.png", show_shapes=True, show_layer_names=False)
# print("Model saved as png!")
# model_metadata, model_save_folder = lab.start_training_loop(EPOCHS, model, train_dataset, train_cross_entr_metric,
# acc_metric, loss_op,
# optimizer,
# BATCH_SIZE, val_dataset, val_cross_entr_metric,
# task_name=TASK_NAME,
# exp_descr=f"{model.name}_"
# f"{''.join([str(p[0]) for p in POOLING_OPS])}",
# patience=3)
# model_save_path = "logs/commands_noise_train_F_val_F_test_F/checkpoints/alex_net_MMMM/20201107-202205/model_e_3_bias_0.32_l_0.317_var_0.003"
# model_save_path = "logs/commands_noise_train_F_val_F_test_F/checkpoints/alex_net_MMME/20201126-004842/model_e_3_bias_0.367_l_0.329_var_0.038"
# model_save_path = "logs/commands_noise_train_F_val_F_test_F/checkpoints/alex_net_EMMM/20201203-002909/model_e_3_bias_0.341_l_0.321_var_0.02"
# model_save_path = "logs/commands_noise_train_F_val_F_test_F/checkpoints/alex_net_EEEE/20201201-175648/model_e_3_bias_0.463_l_0.461_var_0.002"
# model_save_path = "logs/commands_noise_train_F_val_F_test_F/checkpoints/alex_net_EMEM/20201205-163419/model_e_2_bias_0.635_l_0.504_var_0.131"
# model_save_path = "logs/commands_noise_train_F_val_F_test_F/checkpoints/alex_net_MMEM/20201206-214919/model_e_3_bias_0.344_l_0.333_var_0.011"
model_save_path = "logs/commands_noise_train_F_val_F_test_F/checkpoints/alex_net_MEMM/20201210-012901/model_e_3_bias_0.34_l_0.344_var_0.004"
test_predictions = []
labels_test = []
model = AlexNet(labels, types_of_poolings=POOLING_OPS, ksizes=None)
model.load_weights(model_save_path)
lab.start_testing_loop(test_dataset, model, test_cross_entr_metric, acc_metric,
test_predictions, labels_test)
lab.calculate_confusion_matrix(labels[:-1], labels_test, test_predictions)
dim = (122, 257, 2)
model.build((BATCH_SIZE, *dim))
print(f"Class name: {model.name}")
# tf.keras.utils.plot_model(model.build_graph(), to_file="alexnetstyle.png", show_shapes=True, show_layer_names=False)
# print("Model saved as png!")
model_metadata, model_save_folder = lab.start_training_loop(
EPOCHS,
model,
train_dataset,
train_cross_entr_metric,
acc_metric,
loss_op,
optimizer,
BATCH_SIZE,
val_dataset,
val_cross_entr_metric,
task_name=TASK_NAME,
exp_descr=f"{model.name}_"
f"{''.join([str(p[0]) for p in POOLING_OPS])}",
patience=3)
test_predictions = []
labels_test = []
model = AlexNet(labels, types_of_poolings=POOLING_OPS, ksizes=None)
model.load_weights(model_metadata.model_save_path)
lab.start_testing_loop(test_dataset, model, test_cross_entr_metric, acc_metric,
test_predictions, labels_test, model_metadata,
model_save_folder)
lab.calculate_confusion_matrix(labels[:-1], labels_test, test_predictions,
model_save_folder)
