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', var_value=dense_layer_regularizer) log_variable(var_name='class_wt_type', var_value=class_wt_type) log_variable(var_name='learning_rate', var_value=lr) log_variable(var_name='batch_size', var_value=batch_size) log_variable(var_name='use_data_aug', var_value=use_data_aug) if use_data_aug: log_variable(var_name='horizontal_flip', var_value=horizontal_flip)
bottleneck=bottleneck, init_nb_filters=init_nb_filters, growth_rate=growth_rate, nb_layers_per_block=nb_layers_per_block, max_nb_filters=max_nb_filters, activation=decoder_activation, use_activation=use_activation, save_to=run_name, print_model_summary=print_model_summary, plot_model_summary=plot_model_summary, lr=init_lr, loss='ce', metrics=metrics, name=model_name) log_variable(var_name='input_shape', var_value=x_train.shape[1:]) log_variable(var_name='num_classes', var_value=y_train.shape[3]) log_variable(var_name='upsampling_type', var_value=upsampling_type) log_variable(var_name='bottleneck', var_value=bottleneck) log_variable(var_name='init_nb_filters', var_value=init_nb_filters) log_variable(var_name='growth_rate', var_value=growth_rate) log_variable(var_name='nb_layers_per_block', var_value=nb_layers_per_block) log_variable(var_name='max_nb_filters', var_value=max_nb_filters) log_variable(var_name='encoder_activation', var_value=encoder_activation) log_variable(var_name='decoder_activation', var_value=decoder_activation) log_variable(var_name='batch_normalization', var_value=batch_normalization) log_variable(var_name='use_activation', var_value=use_activation)