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)
