num_classes=num_classes, num_dense_layers=num_dense_layers, num_dense_units=num_dense_units, pooling=pooling, dropout_rate=dropout_rate, kernel_regularizer=dense_layer_regularizer, save_to=run_name, load_from=prev_run_name, print_model_summary=True, plot_model_summary=False, lr=lr) n_samples_train = x_train.shape[0] n_samples_valid = x_valid.shape[0] class_weights = compute_class_weights(y_train, wt_type=class_wt_type) batch_size = 32 use_data_aug = True horizontal_flip = True vertical_flip = True rotation_angle = 180 width_shift_range = 0.1 height_shift_range = 0.1 log_variable(var_name='num_dense_layers', var_value=num_dense_layers) log_variable(var_name='num_dense_units', var_value=num_dense_units) log_variable(var_name='dropout_rate', var_value=dropout_rate) log_variable(var_name='pooling', var_value=pooling) log_variable(var_name='class_wt_type', var_value=class_wt_type) log_variable(var_name='dense_layer_regularizer',
print("x_train shape: ", x_train.shape) print("y_train shape: ", y_train.shape) print("x_valid shape: ", x_valid.shape) print("y_valid shape: ", y_valid.shape) """ (x_train, y_train), (x_valid, y_valid), _ = load_training_data(task_idx=3, output_size=224, num_partitions=1, test_split = 0.8)""" class_wt_type = 'balanced' run_name = 'OriginalResNet152' num_classes = y_train.shape[1] class_weights = compute_class_weights(y, wt_type=class_wt_type) print(class_weights) callbacks = config_cls_callbacks(run_name) n_samples_train = x_train.shape[0] n_samples_valid = x_valid.shape[0] sys.stdout.flush() """#Size of training batch batch_train = x_train.shape[0] #Size of testing batch batch_test = x_test.shape[0] y_train = y_train.flatten()