def _write_metadata_one_cnn(model_object, argument_dict):
"""Writes metadata for one CNN to file.
:param model_object: Untrained CNN (instance of `keras.models.Model` or
`keras.models.Sequential`).
:param argument_dict: See doc for `_train_one_cnn`.
:return: metadata_dict: See doc for `cnn.write_model_metadata`.
:return: training_option_dict: Same.
"""
from gewittergefahr.deep_learning import cnn
from gewittergefahr.deep_learning import input_examples
from gewittergefahr.deep_learning import \
training_validation_io as trainval_io
from gewittergefahr.scripts import deep_learning_helper as dl_helper
# Read input args.
sounding_field_names = argument_dict[dl_helper.SOUNDING_FIELDS_ARG_NAME]
radar_field_name_by_channel = argument_dict[RADAR_FIELDS_KEY]
layer_op_name_by_channel = argument_dict[LAYER_OPERATIONS_KEY]
min_height_by_channel_m_agl = argument_dict[MIN_HEIGHTS_KEY]
max_height_by_channel_m_agl = argument_dict[MAX_HEIGHTS_KEY]
normalization_type_string = argument_dict[
dl_helper.NORMALIZATION_TYPE_ARG_NAME]
normalization_file_name = argument_dict[
dl_helper.NORMALIZATION_FILE_ARG_NAME]
min_normalized_value = argument_dict[dl_helper.MIN_NORM_VALUE_ARG_NAME]
max_normalized_value = argument_dict[dl_helper.MAX_NORM_VALUE_ARG_NAME]
target_name = argument_dict[dl_helper.TARGET_NAME_ARG_NAME]
downsampling_classes = numpy.array(
argument_dict[dl_helper.DOWNSAMPLING_CLASSES_ARG_NAME], dtype=int)
downsampling_fractions = numpy.array(
argument_dict[dl_helper.DOWNSAMPLING_FRACTIONS_ARG_NAME], dtype=float)
monitor_string = argument_dict[dl_helper.MONITOR_ARG_NAME]
weight_loss_function = bool(argument_dict[dl_helper.WEIGHT_LOSS_ARG_NAME])
x_translations_pixels = numpy.array(
argument_dict[dl_helper.X_TRANSLATIONS_ARG_NAME], dtype=int)
y_translations_pixels = numpy.array(
argument_dict[dl_helper.Y_TRANSLATIONS_ARG_NAME], dtype=int)
ccw_rotation_angles_deg = numpy.array(
argument_dict[dl_helper.ROTATION_ANGLES_ARG_NAME], dtype=float)
noise_standard_deviation = argument_dict[dl_helper.NOISE_STDEV_ARG_NAME]
num_noisings = argument_dict[dl_helper.NUM_NOISINGS_ARG_NAME]
flip_in_x = bool(argument_dict[dl_helper.FLIP_X_ARG_NAME])
flip_in_y = bool(argument_dict[dl_helper.FLIP_Y_ARG_NAME])
top_training_dir_name = argument_dict[dl_helper.TRAINING_DIR_ARG_NAME]
first_training_time_string = argument_dict[
dl_helper.FIRST_TRAINING_TIME_ARG_NAME]
last_training_time_string = argument_dict[
dl_helper.LAST_TRAINING_TIME_ARG_NAME]
num_examples_per_train_batch = argument_dict[
dl_helper.NUM_EX_PER_TRAIN_ARG_NAME]
top_validation_dir_name = argument_dict[dl_helper.VALIDATION_DIR_ARG_NAME]
first_validation_time_string = argument_dict[
dl_helper.FIRST_VALIDATION_TIME_ARG_NAME]
last_validation_time_string = argument_dict[
dl_helper.LAST_VALIDATION_TIME_ARG_NAME]
num_examples_per_validn_batch = argument_dict[
dl_helper.NUM_EX_PER_VALIDN_ARG_NAME]
num_epochs = argument_dict[dl_helper.NUM_EPOCHS_ARG_NAME]
num_training_batches_per_epoch = argument_dict[
dl_helper.NUM_TRAINING_BATCHES_ARG_NAME]
num_validation_batches_per_epoch = argument_dict[
dl_helper.NUM_VALIDATION_BATCHES_ARG_NAME]
output_dir_name = argument_dict[dl_helper.OUTPUT_DIR_ARG_NAME]
# Process input args.
first_training_time_unix_sec = time_conversion.string_to_unix_sec(
first_training_time_string, TIME_FORMAT)
last_training_time_unix_sec = time_conversion.string_to_unix_sec(
last_training_time_string, TIME_FORMAT)
first_validation_time_unix_sec = time_conversion.string_to_unix_sec(
first_validation_time_string, TIME_FORMAT)
last_validation_time_unix_sec = time_conversion.string_to_unix_sec(
last_validation_time_string, TIME_FORMAT)
if sounding_field_names[0] in ['', 'None']:
sounding_field_names = None
num_channels = len(radar_field_name_by_channel)
layer_operation_dicts = [{}] * num_channels
for k in range(num_channels):
layer_operation_dicts[k] = {
input_examples.RADAR_FIELD_KEY: radar_field_name_by_channel[k],
input_examples.OPERATION_NAME_KEY: layer_op_name_by_channel[k],
input_examples.MIN_HEIGHT_KEY: min_height_by_channel_m_agl[k],
input_examples.MAX_HEIGHT_KEY: max_height_by_channel_m_agl[k]
}
if len(downsampling_classes) > 1:
downsampling_dict = dict(
list(zip(downsampling_classes, downsampling_fractions)))
else:
downsampling_dict = None
translate_flag = (len(x_translations_pixels) > 1
or x_translations_pixels[0] != 0
or y_translations_pixels[0] != 0)
if not translate_flag:
x_translations_pixels = None
y_translations_pixels = None
if len(ccw_rotation_angles_deg) == 1 and ccw_rotation_angles_deg[0] == 0:
ccw_rotation_angles_deg = None
if num_noisings <= 0:
num_noisings = 0
noise_standard_deviation = None
# Find training and validation files.
training_file_names = input_examples.find_many_example_files(
top_directory_name=top_training_dir_name,
shuffled=True,
first_batch_number=FIRST_BATCH_NUMBER,
last_batch_number=LAST_BATCH_NUMBER,
raise_error_if_any_missing=False)
validation_file_names = input_examples.find_many_example_files(
top_directory_name=top_validation_dir_name,
shuffled=True,
first_batch_number=FIRST_BATCH_NUMBER,
last_batch_number=LAST_BATCH_NUMBER,
raise_error_if_any_missing=False)
# Write metadata.
metadata_dict = {
cnn.NUM_EPOCHS_KEY: num_epochs,
cnn.NUM_TRAINING_BATCHES_KEY: num_training_batches_per_epoch,
cnn.NUM_VALIDATION_BATCHES_KEY: num_validation_batches_per_epoch,
cnn.MONITOR_STRING_KEY: monitor_string,
cnn.WEIGHT_LOSS_FUNCTION_KEY: weight_loss_function,
cnn.CONV_2D3D_KEY: False,
cnn.VALIDATION_FILES_KEY: validation_file_names,
cnn.FIRST_VALIDN_TIME_KEY: first_validation_time_unix_sec,
cnn.LAST_VALIDN_TIME_KEY: last_validation_time_unix_sec,
cnn.LAYER_OPERATIONS_KEY: layer_operation_dicts,
cnn.NUM_EX_PER_VALIDN_BATCH_KEY: num_examples_per_validn_batch
}
input_tensor = model_object.input
if isinstance(input_tensor, list):
input_tensor = input_tensor[0]
num_grid_rows = input_tensor.get_shape().as_list()[1]
num_grid_columns = input_tensor.get_shape().as_list()[2]
training_option_dict = {
trainval_io.EXAMPLE_FILES_KEY: training_file_names,
trainval_io.TARGET_NAME_KEY: target_name,
trainval_io.FIRST_STORM_TIME_KEY: first_training_time_unix_sec,
trainval_io.LAST_STORM_TIME_KEY: last_training_time_unix_sec,
trainval_io.NUM_EXAMPLES_PER_BATCH_KEY: num_examples_per_train_batch,
trainval_io.SOUNDING_FIELDS_KEY: sounding_field_names,
trainval_io.SOUNDING_HEIGHTS_KEY: SOUNDING_HEIGHTS_M_AGL,
trainval_io.NUM_ROWS_KEY: num_grid_rows,
trainval_io.NUM_COLUMNS_KEY: num_grid_columns,
trainval_io.NORMALIZATION_TYPE_KEY: normalization_type_string,
trainval_io.NORMALIZATION_FILE_KEY: normalization_file_name,
trainval_io.MIN_NORMALIZED_VALUE_KEY: min_normalized_value,
trainval_io.MAX_NORMALIZED_VALUE_KEY: max_normalized_value,
trainval_io.BINARIZE_TARGET_KEY: False,
trainval_io.SAMPLING_FRACTIONS_KEY: downsampling_dict,
trainval_io.LOOP_ONCE_KEY: False,
trainval_io.X_TRANSLATIONS_KEY: x_translations_pixels,
trainval_io.Y_TRANSLATIONS_KEY: y_translations_pixels,
trainval_io.ROTATION_ANGLES_KEY: ccw_rotation_angles_deg,
trainval_io.NOISE_STDEV_KEY: noise_standard_deviation,
trainval_io.NUM_NOISINGS_KEY: num_noisings,
trainval_io.FLIP_X_KEY: flip_in_x,
trainval_io.FLIP_Y_KEY: flip_in_y
}
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
metafile_name = '{0:s}/model_metadata.p'.format(output_dir_name)
print('Writing metadata to: "{0:s}"...'.format(metafile_name))
cnn.write_model_metadata(pickle_file_name=metafile_name,
metadata_dict=metadata_dict,
training_option_dict=training_option_dict)
return metadata_dict, training_option_dict
def _run(input_model_file_name, sounding_field_names,
normalization_type_string, normalization_param_file_name,
min_normalized_value, max_normalized_value, target_name,
downsampling_classes, downsampling_fractions, monitor_string,
weight_loss_function, x_translations_pixels, y_translations_pixels,
ccw_rotation_angles_deg, noise_standard_deviation, num_noisings,
flip_in_x, flip_in_y, top_training_dir_name,
first_training_time_string, last_training_time_string,
num_examples_per_train_batch, top_validation_dir_name,
first_validation_time_string, last_validation_time_string,
num_examples_per_validn_batch, num_epochs,
num_training_batches_per_epoch, num_validation_batches_per_epoch,
output_dir_name):
"""Trains CNN with 2-D and 3-D MYRORSS images.
This is effectively the main method.
:param input_model_file_name: See documentation at top of file.
:param sounding_field_names: Same.
:param normalization_type_string: Same.
:param normalization_param_file_name: Same.
:param min_normalized_value: Same.
:param max_normalized_value: Same.
:param target_name: Same.
:param downsampling_classes: Same.
:param downsampling_fractions: Same.
:param monitor_string: Same.
:param weight_loss_function: Same.
:param x_translations_pixels: Same.
:param y_translations_pixels: Same.
:param ccw_rotation_angles_deg: Same.
:param noise_standard_deviation: Same.
:param num_noisings: Same.
:param flip_in_x: Same.
:param flip_in_y: Same.
:param top_training_dir_name: Same.
:param first_training_time_string: Same.
:param last_training_time_string: Same.
:param num_examples_per_train_batch: Same.
:param top_validation_dir_name: Same.
:param first_validation_time_string: Same.
:param last_validation_time_string: Same.
:param num_examples_per_validn_batch: Same.
:param num_epochs: Same.
:param num_training_batches_per_epoch: Same.
:param num_validation_batches_per_epoch: Same.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
# argument_file_name = '{0:s}/input_args.p'.format(output_dir_name)
# print('Writing input args to: "{0:s}"...'.format(argument_file_name))
#
# argument_file_handle = open(argument_file_name, 'wb')
# pickle.dump(INPUT_ARG_OBJECT.__dict__, argument_file_handle)
# argument_file_handle.close()
#
# return
# Process input args.
first_training_time_unix_sec = time_conversion.string_to_unix_sec(
first_training_time_string, TIME_FORMAT)
last_training_time_unix_sec = time_conversion.string_to_unix_sec(
last_training_time_string, TIME_FORMAT)
first_validation_time_unix_sec = time_conversion.string_to_unix_sec(
first_validation_time_string, TIME_FORMAT)
last_validation_time_unix_sec = time_conversion.string_to_unix_sec(
last_validation_time_string, TIME_FORMAT)
if sounding_field_names[0] in ['', 'None']:
sounding_field_names = None
if len(downsampling_classes) > 1:
downsampling_dict = dict(
list(zip(downsampling_classes, downsampling_fractions)))
else:
downsampling_dict = None
if (len(x_translations_pixels) == 1
and x_translations_pixels + y_translations_pixels == 0):
x_translations_pixels = None
y_translations_pixels = None
if len(ccw_rotation_angles_deg) == 1 and ccw_rotation_angles_deg[0] == 0:
ccw_rotation_angles_deg = None
if num_noisings <= 0:
num_noisings = 0
noise_standard_deviation = None
# Set output locations.
output_model_file_name = '{0:s}/model.h5'.format(output_dir_name)
history_file_name = '{0:s}/model_history.csv'.format(output_dir_name)
tensorboard_dir_name = '{0:s}/tensorboard'.format(output_dir_name)
model_metafile_name = '{0:s}/model_metadata.p'.format(output_dir_name)
# Find training and validation files.
training_file_names = input_examples.find_many_example_files(
top_directory_name=top_training_dir_name,
shuffled=True,
first_batch_number=FIRST_BATCH_NUMBER,
last_batch_number=LAST_BATCH_NUMBER,
raise_error_if_any_missing=False)
validation_file_names = input_examples.find_many_example_files(
top_directory_name=top_validation_dir_name,
shuffled=True,
first_batch_number=FIRST_BATCH_NUMBER,
last_batch_number=LAST_BATCH_NUMBER,
raise_error_if_any_missing=False)
# Read architecture.
print(
'Reading architecture from: "{0:s}"...'.format(input_model_file_name))
model_object = cnn.read_model(input_model_file_name)
# model_object = keras.models.clone_model(model_object)
# TODO(thunderhoser): This is a HACK.
model_object.compile(loss=keras.losses.binary_crossentropy,
optimizer=keras.optimizers.Adam(),
metrics=cnn_setup.DEFAULT_METRIC_FUNCTION_LIST)
print(SEPARATOR_STRING)
model_object.summary()
print(SEPARATOR_STRING)
# Write metadata.
metadata_dict = {
cnn.NUM_EPOCHS_KEY: num_epochs,
cnn.NUM_TRAINING_BATCHES_KEY: num_training_batches_per_epoch,
cnn.NUM_VALIDATION_BATCHES_KEY: num_validation_batches_per_epoch,
cnn.MONITOR_STRING_KEY: monitor_string,
cnn.WEIGHT_LOSS_FUNCTION_KEY: weight_loss_function,
cnn.CONV_2D3D_KEY: True,
cnn.VALIDATION_FILES_KEY: validation_file_names,
cnn.FIRST_VALIDN_TIME_KEY: first_validation_time_unix_sec,
cnn.LAST_VALIDN_TIME_KEY: last_validation_time_unix_sec,
cnn.NUM_EX_PER_VALIDN_BATCH_KEY: num_examples_per_validn_batch
}
if isinstance(model_object.input, list):
list_of_input_tensors = model_object.input
else:
list_of_input_tensors = [model_object.input]
upsample_refl = len(list_of_input_tensors) == 2
num_grid_rows = list_of_input_tensors[0].get_shape().as_list()[1]
num_grid_columns = list_of_input_tensors[0].get_shape().as_list()[2]
if upsample_refl:
num_grid_rows = int(numpy.round(num_grid_rows / 2))
num_grid_columns = int(numpy.round(num_grid_columns / 2))
training_option_dict = {
trainval_io.EXAMPLE_FILES_KEY: training_file_names,
trainval_io.TARGET_NAME_KEY: target_name,
trainval_io.FIRST_STORM_TIME_KEY: first_training_time_unix_sec,
trainval_io.LAST_STORM_TIME_KEY: last_training_time_unix_sec,
trainval_io.NUM_EXAMPLES_PER_BATCH_KEY: num_examples_per_train_batch,
trainval_io.RADAR_FIELDS_KEY:
input_examples.AZIMUTHAL_SHEAR_FIELD_NAMES,
trainval_io.RADAR_HEIGHTS_KEY: REFLECTIVITY_HEIGHTS_M_AGL,
trainval_io.SOUNDING_FIELDS_KEY: sounding_field_names,
trainval_io.SOUNDING_HEIGHTS_KEY: SOUNDING_HEIGHTS_M_AGL,
trainval_io.NUM_ROWS_KEY: num_grid_rows,
trainval_io.NUM_COLUMNS_KEY: num_grid_columns,
trainval_io.NORMALIZATION_TYPE_KEY: normalization_type_string,
trainval_io.NORMALIZATION_FILE_KEY: normalization_param_file_name,
trainval_io.MIN_NORMALIZED_VALUE_KEY: min_normalized_value,
trainval_io.MAX_NORMALIZED_VALUE_KEY: max_normalized_value,
trainval_io.BINARIZE_TARGET_KEY: False,
trainval_io.SAMPLING_FRACTIONS_KEY: downsampling_dict,
trainval_io.LOOP_ONCE_KEY: False,
trainval_io.X_TRANSLATIONS_KEY: x_translations_pixels,
trainval_io.Y_TRANSLATIONS_KEY: y_translations_pixels,
trainval_io.ROTATION_ANGLES_KEY: ccw_rotation_angles_deg,
trainval_io.NOISE_STDEV_KEY: noise_standard_deviation,
trainval_io.NUM_NOISINGS_KEY: num_noisings,
trainval_io.FLIP_X_KEY: flip_in_x,
trainval_io.FLIP_Y_KEY: flip_in_y,
trainval_io.UPSAMPLE_REFLECTIVITY_KEY: upsample_refl
}
print('Writing metadata to: "{0:s}"...'.format(model_metafile_name))
cnn.write_model_metadata(pickle_file_name=model_metafile_name,
metadata_dict=metadata_dict,
training_option_dict=training_option_dict)
cnn.train_cnn_2d3d_myrorss(
model_object=model_object,
model_file_name=output_model_file_name,
history_file_name=history_file_name,
tensorboard_dir_name=tensorboard_dir_name,
num_epochs=num_epochs,
num_training_batches_per_epoch=num_training_batches_per_epoch,
training_option_dict=training_option_dict,
monitor_string=monitor_string,
weight_loss_function=weight_loss_function,
num_validation_batches_per_epoch=num_validation_batches_per_epoch,
validation_file_names=validation_file_names,
first_validn_time_unix_sec=first_validation_time_unix_sec,
last_validn_time_unix_sec=last_validation_time_unix_sec,
num_examples_per_validn_batch=num_examples_per_validn_batch)
def _run(input_model_file_name, sounding_field_names, normalization_type_string,
normalization_param_file_name, min_normalized_value,
max_normalized_value, target_name, downsampling_classes,
downsampling_fractions, monitor_string, weight_loss_function,
top_training_dir_name, first_training_time_string,
last_training_time_string, num_examples_per_train_batch,
top_validation_dir_name, first_validation_time_string,
last_validation_time_string, num_examples_per_validn_batch, num_epochs,
num_training_batches_per_epoch, num_validation_batches_per_epoch,
output_dir_name):
"""Trains CNN with soundings only.
This is effectively the main method.
:param input_model_file_name: See documentation at top of file.
:param sounding_field_names: Same.
:param normalization_type_string: Same.
:param normalization_param_file_name: Same.
:param min_normalized_value: Same.
:param max_normalized_value: Same.
:param target_name: Same.
:param downsampling_classes: Same.
:param downsampling_fractions: Same.
:param monitor_string: Same.
:param weight_loss_function: Same.
:param top_training_dir_name: Same.
:param first_training_time_string: Same.
:param last_training_time_string: Same.
:param num_examples_per_train_batch: Same.
:param top_validation_dir_name: Same.
:param first_validation_time_string: Same.
:param last_validation_time_string: Same.
:param num_examples_per_validn_batch: Same.
:param num_epochs: Same.
:param num_training_batches_per_epoch: Same.
:param num_validation_batches_per_epoch: Same.
:param output_dir_name: Same.
"""
# Process input args.
first_training_time_unix_sec = time_conversion.string_to_unix_sec(
first_training_time_string, TIME_FORMAT)
last_training_time_unix_sec = time_conversion.string_to_unix_sec(
last_training_time_string, TIME_FORMAT)
first_validation_time_unix_sec = time_conversion.string_to_unix_sec(
first_validation_time_string, TIME_FORMAT)
last_validation_time_unix_sec = time_conversion.string_to_unix_sec(
last_validation_time_string, TIME_FORMAT)
if len(downsampling_classes) > 1:
downsampling_dict = dict(list(zip(
downsampling_classes, downsampling_fractions
)))
else:
downsampling_dict = None
# Set output locations.
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
output_model_file_name = '{0:s}/model.h5'.format(output_dir_name)
history_file_name = '{0:s}/model_history.csv'.format(output_dir_name)
tensorboard_dir_name = '{0:s}/tensorboard'.format(output_dir_name)
model_metafile_name = '{0:s}/model_metadata.p'.format(output_dir_name)
# Find training and validation files.
training_file_names = input_examples.find_many_example_files(
top_directory_name=top_training_dir_name, shuffled=True,
first_batch_number=FIRST_BATCH_NUMBER,
last_batch_number=LAST_BATCH_NUMBER, raise_error_if_any_missing=False)
validation_file_names = input_examples.find_many_example_files(
top_directory_name=top_validation_dir_name, shuffled=True,
first_batch_number=FIRST_BATCH_NUMBER,
last_batch_number=LAST_BATCH_NUMBER, raise_error_if_any_missing=False)
# Read architecture.
print('Reading architecture from: "{0:s}"...'.format(input_model_file_name))
model_object = cnn.read_model(input_model_file_name)
# model_object = keras.models.clone_model(model_object)
# TODO(thunderhoser): This is a HACK.
model_object.compile(
loss=keras.losses.binary_crossentropy,
optimizer=keras.optimizers.Adam(),
metrics=cnn_setup.DEFAULT_METRIC_FUNCTION_LIST)
print(SEPARATOR_STRING)
model_object.summary()
print(SEPARATOR_STRING)
# Write metadata.
metadata_dict = {
cnn.NUM_EPOCHS_KEY: num_epochs,
cnn.NUM_TRAINING_BATCHES_KEY: num_training_batches_per_epoch,
cnn.NUM_VALIDATION_BATCHES_KEY: num_validation_batches_per_epoch,
cnn.MONITOR_STRING_KEY: monitor_string,
cnn.WEIGHT_LOSS_FUNCTION_KEY: weight_loss_function,
cnn.CONV_2D3D_KEY: False,
cnn.VALIDATION_FILES_KEY: validation_file_names,
cnn.FIRST_VALIDN_TIME_KEY: first_validation_time_unix_sec,
cnn.LAST_VALIDN_TIME_KEY: last_validation_time_unix_sec,
cnn.NUM_EX_PER_VALIDN_BATCH_KEY: num_examples_per_validn_batch
}
training_option_dict = {
trainval_io.EXAMPLE_FILES_KEY: training_file_names,
trainval_io.NUM_EXAMPLES_PER_BATCH_KEY: num_examples_per_train_batch,
trainval_io.FIRST_STORM_TIME_KEY: first_training_time_unix_sec,
trainval_io.LAST_STORM_TIME_KEY: last_training_time_unix_sec,
trainval_io.SOUNDING_FIELDS_KEY: sounding_field_names,
trainval_io.SOUNDING_HEIGHTS_KEY: SOUNDING_HEIGHTS_M_AGL,
trainval_io.NORMALIZATION_TYPE_KEY: normalization_type_string,
trainval_io.NORMALIZATION_FILE_KEY: normalization_param_file_name,
trainval_io.MIN_NORMALIZED_VALUE_KEY: min_normalized_value,
trainval_io.MAX_NORMALIZED_VALUE_KEY: max_normalized_value,
trainval_io.TARGET_NAME_KEY: target_name,
trainval_io.BINARIZE_TARGET_KEY: False,
trainval_io.SAMPLING_FRACTIONS_KEY: downsampling_dict,
trainval_io.LOOP_ONCE_KEY: False
}
print('Writing metadata to: "{0:s}"...'.format(model_metafile_name))
cnn.write_model_metadata(
pickle_file_name=model_metafile_name, metadata_dict=metadata_dict,
training_option_dict=training_option_dict)
cnn.train_cnn_with_soundings(
model_object=model_object, model_file_name=output_model_file_name,
history_file_name=history_file_name,
tensorboard_dir_name=tensorboard_dir_name, num_epochs=num_epochs,
num_training_batches_per_epoch=num_training_batches_per_epoch,
training_option_dict=training_option_dict,
monitor_string=monitor_string,
weight_loss_function=weight_loss_function,
num_validation_batches_per_epoch=num_validation_batches_per_epoch,
validation_file_names=validation_file_names,
first_validn_time_unix_sec=first_validation_time_unix_sec,
last_validn_time_unix_sec=last_validation_time_unix_sec,
num_examples_per_validn_batch=num_examples_per_validn_batch)
def _run(input_model_file_name, radar_field_names, sounding_field_names,
normalization_type_string, normalization_param_file_name,
min_normalized_value, max_normalized_value, downsampling_keys,
downsampling_fractions, monitor_string, weight_loss_function,
refl_masking_threshold_dbz, x_translations_pixels,
y_translations_pixels, ccw_rotation_angles_deg,
noise_standard_deviation, num_noisings, flip_in_x, flip_in_y,
top_training_dir_name, first_training_time_string,
last_training_time_string, top_validation_dir_name,
first_validation_time_string, last_validation_time_string,
num_examples_per_batch, num_epochs, num_training_batches_per_epoch,
num_validation_batches_per_epoch, output_dir_name):
"""Trains CNN with native (3-D) GridRad images.
This is effectively the main method.
:param input_model_file_name: See documentation at top of file.
:param radar_field_names: Same.
:param sounding_field_names: Same.
:param normalization_type_string: Same.
:param normalization_param_file_name: Same.
:param min_normalized_value: Same.
:param max_normalized_value: Same.
:param downsampling_keys: Same.
:param downsampling_fractions: Same.
:param monitor_string: Same.
:param weight_loss_function: Same.
:param refl_masking_threshold_dbz: Same.
:param x_translations_pixels: Same.
:param y_translations_pixels: Same.
:param ccw_rotation_angles_deg: Same.
:param noise_standard_deviation: Same.
:param num_noisings: Same.
:param flip_in_x: Same.
:param flip_in_y: Same.
:param top_training_dir_name: Same.
:param first_training_time_string: Same.
:param last_training_time_string: Same.
:param top_validation_dir_name: Same.
:param first_validation_time_string: Same.
:param last_validation_time_string: Same.
:param num_examples_per_batch: Same.
:param num_epochs: Same.
:param num_training_batches_per_epoch: Same.
:param num_validation_batches_per_epoch: Same.
:param output_dir_name: Same.
"""
# Process input args.
first_training_time_unix_sec = time_conversion.string_to_unix_sec(
first_training_time_string, TIME_FORMAT)
last_training_time_unix_sec = time_conversion.string_to_unix_sec(
last_training_time_string, TIME_FORMAT)
first_validation_time_unix_sec = time_conversion.string_to_unix_sec(
first_validation_time_string, TIME_FORMAT)
last_validation_time_unix_sec = time_conversion.string_to_unix_sec(
last_validation_time_string, TIME_FORMAT)
if sounding_field_names[0] in ['', 'None']:
sounding_field_names = None
if len(downsampling_keys) > 1:
class_to_sampling_fraction_dict = dict(
zip(downsampling_keys, downsampling_fractions))
else:
class_to_sampling_fraction_dict = None
if (len(x_translations_pixels) == 1
and x_translations_pixels + y_translations_pixels == 0):
x_translations_pixels = None
y_translations_pixels = None
if len(ccw_rotation_angles_deg) == 1 and ccw_rotation_angles_deg[0] == 0:
ccw_rotation_angles_deg = None
if num_noisings <= 0:
num_noisings = 0
noise_standard_deviation = None
# Set output locations.
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
output_model_file_name = '{0:s}/model.h5'.format(output_dir_name)
history_file_name = '{0:s}/model_history.csv'.format(output_dir_name)
tensorboard_dir_name = '{0:s}/tensorboard'.format(output_dir_name)
model_metafile_name = '{0:s}/model_metadata.p'.format(output_dir_name)
# Find training and validation files.
training_file_names = input_examples.find_many_example_files(
top_directory_name=top_training_dir_name,
shuffled=True,
first_batch_number=FIRST_BATCH_NUMBER,
last_batch_number=LAST_BATCH_NUMBER,
raise_error_if_any_missing=False)
validation_file_names = input_examples.find_many_example_files(
top_directory_name=top_validation_dir_name,
shuffled=True,
first_batch_number=FIRST_BATCH_NUMBER,
last_batch_number=LAST_BATCH_NUMBER,
raise_error_if_any_missing=False)
# Read architecture.
print 'Reading architecture from: "{0:s}"...'.format(input_model_file_name)
model_object = cnn.read_model(input_model_file_name)
model_object = keras.models.clone_model(model_object)
# TODO(thunderhoser): This is a HACK.
model_object.compile(loss=keras.losses.binary_crossentropy,
optimizer=keras.optimizers.Adam(),
metrics=cnn_setup.DEFAULT_METRIC_FUNCTION_LIST)
print SEPARATOR_STRING
model_object.summary()
print SEPARATOR_STRING
# Write metadata.
this_example_dict = input_examples.read_example_file(
netcdf_file_name=training_file_names[0], metadata_only=True)
target_name = this_example_dict[input_examples.TARGET_NAME_KEY]
metadata_dict = {
cnn.TARGET_NAME_KEY: target_name,
cnn.NUM_EPOCHS_KEY: num_epochs,
cnn.NUM_TRAINING_BATCHES_KEY: num_training_batches_per_epoch,
cnn.NUM_VALIDATION_BATCHES_KEY: num_validation_batches_per_epoch,
cnn.MONITOR_STRING_KEY: monitor_string,
cnn.WEIGHT_LOSS_FUNCTION_KEY: weight_loss_function,
cnn.USE_2D3D_CONVOLUTION_KEY: False,
cnn.VALIDATION_FILES_KEY: validation_file_names,
cnn.FIRST_VALIDN_TIME_KEY: first_validation_time_unix_sec,
cnn.LAST_VALIDN_TIME_KEY: last_validation_time_unix_sec
}
input_tensor = model_object.input
if isinstance(input_tensor, list):
input_tensor = input_tensor[0]
num_grid_rows = input_tensor.get_shape().as_list()[1]
num_grid_columns = input_tensor.get_shape().as_list()[2]
training_option_dict = {
trainval_io.EXAMPLE_FILES_KEY: training_file_names,
trainval_io.FIRST_STORM_TIME_KEY: first_training_time_unix_sec,
trainval_io.LAST_STORM_TIME_KEY: last_training_time_unix_sec,
trainval_io.NUM_EXAMPLES_PER_BATCH_KEY: num_examples_per_batch,
trainval_io.RADAR_FIELDS_KEY: radar_field_names,
trainval_io.RADAR_HEIGHTS_KEY: RADAR_HEIGHTS_M_AGL,
trainval_io.SOUNDING_FIELDS_KEY: sounding_field_names,
trainval_io.SOUNDING_HEIGHTS_KEY: SOUNDING_HEIGHTS_M_AGL,
trainval_io.NUM_ROWS_KEY: num_grid_rows,
trainval_io.NUM_COLUMNS_KEY: num_grid_columns,
trainval_io.NORMALIZATION_TYPE_KEY: normalization_type_string,
trainval_io.NORMALIZATION_FILE_KEY: normalization_param_file_name,
trainval_io.MIN_NORMALIZED_VALUE_KEY: min_normalized_value,
trainval_io.MAX_NORMALIZED_VALUE_KEY: max_normalized_value,
trainval_io.BINARIZE_TARGET_KEY: False,
trainval_io.SAMPLING_FRACTIONS_KEY: class_to_sampling_fraction_dict,
trainval_io.LOOP_ONCE_KEY: False,
trainval_io.REFLECTIVITY_MASK_KEY: refl_masking_threshold_dbz,
trainval_io.X_TRANSLATIONS_KEY: x_translations_pixels,
trainval_io.Y_TRANSLATIONS_KEY: y_translations_pixels,
trainval_io.ROTATION_ANGLES_KEY: ccw_rotation_angles_deg,
trainval_io.NOISE_STDEV_KEY: noise_standard_deviation,
trainval_io.NUM_NOISINGS_KEY: num_noisings,
trainval_io.FLIP_X_KEY: flip_in_x,
trainval_io.FLIP_Y_KEY: flip_in_y
}
print 'Writing metadata to: "{0:s}"...'.format(model_metafile_name)
cnn.write_model_metadata(pickle_file_name=model_metafile_name,
metadata_dict=metadata_dict,
training_option_dict=training_option_dict)
cnn.train_cnn_2d_or_3d(
model_object=model_object,
model_file_name=output_model_file_name,
history_file_name=history_file_name,
tensorboard_dir_name=tensorboard_dir_name,
num_epochs=num_epochs,
num_training_batches_per_epoch=num_training_batches_per_epoch,
training_option_dict=training_option_dict,
monitor_string=monitor_string,
weight_loss_function=weight_loss_function,
num_validation_batches_per_epoch=num_validation_batches_per_epoch,
validation_file_names=validation_file_names,
first_validn_time_unix_sec=first_validation_time_unix_sec,
last_validn_time_unix_sec=last_validation_time_unix_sec)
def _run(input_model_file_name, radar_field_name_by_channel,
layer_op_name_by_channel, min_height_by_channel_m_agl,
max_height_by_channel_m_agl, sounding_field_names,
normalization_type_string, normalization_param_file_name,
min_normalized_value, max_normalized_value, target_name,
shuffle_target, downsampling_classes, downsampling_fractions,
monitor_string, weight_loss_function, x_translations_pixels,
y_translations_pixels, ccw_rotation_angles_deg,
noise_standard_deviation, num_noisings, flip_in_x, flip_in_y,
top_training_dir_name, first_training_time_string,
last_training_time_string, num_examples_per_train_batch,
top_validation_dir_name, first_validation_time_string,
last_validation_time_string, num_examples_per_validn_batch,
num_epochs, num_training_batches_per_epoch,
num_validation_batches_per_epoch, output_dir_name):
"""Trains CNN with 2-D GridRad images.
This is effectively the main method.
:param input_model_file_name: See documentation at top of file.
:param radar_field_name_by_channel: Same.
:param layer_op_name_by_channel: Same.
:param min_height_by_channel_m_agl: Same.
:param max_height_by_channel_m_agl: Same.
:param sounding_field_names: Same.
:param normalization_type_string: Same.
:param normalization_param_file_name: Same.
:param min_normalized_value: Same.
:param max_normalized_value: Same.
:param target_name: Same.
:param shuffle_target: Same.
:param downsampling_classes: Same.
:param downsampling_fractions: Same.
:param monitor_string: Same.
:param weight_loss_function: Same.
:param x_translations_pixels: Same.
:param y_translations_pixels: Same.
:param ccw_rotation_angles_deg: Same.
:param noise_standard_deviation: Same.
:param num_noisings: Same.
:param flip_in_x: Same.
:param flip_in_y: Same.
:param top_training_dir_name: Same.
:param first_training_time_string: Same.
:param last_training_time_string: Same.
:param num_examples_per_train_batch: Same.
:param top_validation_dir_name: Same.
:param first_validation_time_string: Same.
:param last_validation_time_string: Same.
:param num_examples_per_validn_batch: Same.
:param num_epochs: Same.
:param num_training_batches_per_epoch: Same.
:param num_validation_batches_per_epoch: Same.
:param output_dir_name: Same.
"""
# Process input args.
first_training_time_unix_sec = time_conversion.string_to_unix_sec(
first_training_time_string, TIME_FORMAT)
last_training_time_unix_sec = time_conversion.string_to_unix_sec(
last_training_time_string, TIME_FORMAT)
if top_validation_dir_name in ['', 'None']:
top_validation_dir_name = None
num_validation_batches_per_epoch = 0
first_validation_time_unix_sec = 0
last_validation_time_unix_sec = 0
else:
first_validation_time_unix_sec = time_conversion.string_to_unix_sec(
first_validation_time_string, TIME_FORMAT)
last_validation_time_unix_sec = time_conversion.string_to_unix_sec(
last_validation_time_string, TIME_FORMAT)
if sounding_field_names[0] in ['', 'None']:
sounding_field_names = None
if len(downsampling_classes) > 1:
class_to_sampling_fraction_dict = dict(
list(zip(downsampling_classes, downsampling_fractions)))
else:
class_to_sampling_fraction_dict = None
if (len(x_translations_pixels) == 1
and x_translations_pixels + y_translations_pixels == 0):
x_translations_pixels = None
y_translations_pixels = None
if len(ccw_rotation_angles_deg) == 1 and ccw_rotation_angles_deg[0] == 0:
ccw_rotation_angles_deg = None
if num_noisings <= 0:
num_noisings = 0
noise_standard_deviation = None
num_channels = len(radar_field_name_by_channel)
expected_dimensions = numpy.array([num_channels], dtype=int)
error_checking.assert_is_numpy_array(numpy.array(layer_op_name_by_channel),
exact_dimensions=expected_dimensions)
error_checking.assert_is_numpy_array(min_height_by_channel_m_agl,
exact_dimensions=expected_dimensions)
error_checking.assert_is_numpy_array(max_height_by_channel_m_agl,
exact_dimensions=expected_dimensions)
list_of_layer_operation_dicts = [{}] * num_channels
for m in range(num_channels):
list_of_layer_operation_dicts[m] = {
input_examples.RADAR_FIELD_KEY: radar_field_name_by_channel[m],
input_examples.OPERATION_NAME_KEY: layer_op_name_by_channel[m],
input_examples.MIN_HEIGHT_KEY: min_height_by_channel_m_agl[m],
input_examples.MAX_HEIGHT_KEY: max_height_by_channel_m_agl[m]
}
# Set output locations.
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
output_model_file_name = '{0:s}/model.h5'.format(output_dir_name)
history_file_name = '{0:s}/model_history.csv'.format(output_dir_name)
tensorboard_dir_name = '{0:s}/tensorboard'.format(output_dir_name)
model_metafile_name = '{0:s}/model_metadata.p'.format(output_dir_name)
# Find training and validation files.
training_file_names = input_examples.find_many_example_files(
top_directory_name=top_training_dir_name,
shuffled=True,
first_batch_number=FIRST_BATCH_NUMBER,
last_batch_number=LAST_BATCH_NUMBER,
raise_error_if_any_missing=False)
if top_validation_dir_name is None:
validation_file_names = []
else:
validation_file_names = input_examples.find_many_example_files(
top_directory_name=top_validation_dir_name,
shuffled=True,
first_batch_number=FIRST_BATCH_NUMBER,
last_batch_number=LAST_BATCH_NUMBER,
raise_error_if_any_missing=False)
# Read architecture.
print(
'Reading architecture from: "{0:s}"...'.format(input_model_file_name))
model_object = cnn.read_model(input_model_file_name)
# model_object = clone_model(model_object)
# TODO(thunderhoser): This is a HACK.
model_object.compile(loss=keras.losses.binary_crossentropy,
optimizer=keras.optimizers.Adam(),
metrics=cnn_setup.DEFAULT_METRIC_FUNCTION_LIST)
print(SEPARATOR_STRING)
model_object.summary()
print(SEPARATOR_STRING)
print(K.eval(model_object.get_layer(name='radar_conv2d_2').weights[0]))
print(SEPARATOR_STRING)
# Write metadata.
metadata_dict = {
cnn.NUM_EPOCHS_KEY: num_epochs,
cnn.NUM_TRAINING_BATCHES_KEY: num_training_batches_per_epoch,
cnn.NUM_VALIDATION_BATCHES_KEY: num_validation_batches_per_epoch,
cnn.MONITOR_STRING_KEY: monitor_string,
cnn.WEIGHT_LOSS_FUNCTION_KEY: weight_loss_function,
cnn.CONV_2D3D_KEY: False,
cnn.VALIDATION_FILES_KEY: validation_file_names,
cnn.FIRST_VALIDN_TIME_KEY: first_validation_time_unix_sec,
cnn.LAST_VALIDN_TIME_KEY: last_validation_time_unix_sec,
cnn.NUM_EX_PER_VALIDN_BATCH_KEY: num_examples_per_validn_batch
}
input_tensor = model_object.input
if isinstance(input_tensor, list):
input_tensor = input_tensor[0]
num_grid_rows = input_tensor.get_shape().as_list()[1]
num_grid_columns = input_tensor.get_shape().as_list()[2]
training_option_dict = {
trainval_io.EXAMPLE_FILES_KEY: training_file_names,
trainval_io.TARGET_NAME_KEY: target_name,
trainval_io.SHUFFLE_TARGET_KEY: shuffle_target,
trainval_io.FIRST_STORM_TIME_KEY: first_training_time_unix_sec,
trainval_io.LAST_STORM_TIME_KEY: last_training_time_unix_sec,
trainval_io.NUM_EXAMPLES_PER_BATCH_KEY: num_examples_per_train_batch,
trainval_io.SOUNDING_FIELDS_KEY: sounding_field_names,
trainval_io.SOUNDING_HEIGHTS_KEY: SOUNDING_HEIGHTS_M_AGL,
trainval_io.NUM_ROWS_KEY: num_grid_rows,
trainval_io.NUM_COLUMNS_KEY: num_grid_columns,
trainval_io.NORMALIZATION_TYPE_KEY: normalization_type_string,
trainval_io.NORMALIZATION_FILE_KEY: normalization_param_file_name,
trainval_io.MIN_NORMALIZED_VALUE_KEY: min_normalized_value,
trainval_io.MAX_NORMALIZED_VALUE_KEY: max_normalized_value,
trainval_io.BINARIZE_TARGET_KEY: False,
trainval_io.SAMPLING_FRACTIONS_KEY: class_to_sampling_fraction_dict,
trainval_io.LOOP_ONCE_KEY: False,
trainval_io.X_TRANSLATIONS_KEY: x_translations_pixels,
trainval_io.Y_TRANSLATIONS_KEY: y_translations_pixels,
trainval_io.ROTATION_ANGLES_KEY: ccw_rotation_angles_deg,
trainval_io.NOISE_STDEV_KEY: noise_standard_deviation,
trainval_io.NUM_NOISINGS_KEY: num_noisings,
trainval_io.FLIP_X_KEY: flip_in_x,
trainval_io.FLIP_Y_KEY: flip_in_y
}
print('Writing metadata to: "{0:s}"...'.format(model_metafile_name))
cnn.write_model_metadata(
pickle_file_name=model_metafile_name,
metadata_dict=metadata_dict,
training_option_dict=training_option_dict,
list_of_layer_operation_dicts=list_of_layer_operation_dicts)
cnn.train_cnn_gridrad_2d_reduced(
model_object=model_object,
model_file_name=output_model_file_name,
history_file_name=history_file_name,
tensorboard_dir_name=tensorboard_dir_name,
num_epochs=num_epochs,
num_training_batches_per_epoch=num_training_batches_per_epoch,
training_option_dict=training_option_dict,
list_of_layer_operation_dicts=list_of_layer_operation_dicts,
monitor_string=monitor_string,
weight_loss_function=weight_loss_function,
num_validation_batches_per_epoch=num_validation_batches_per_epoch,
validation_file_names=validation_file_names,
first_validn_time_unix_sec=first_validation_time_unix_sec,
last_validn_time_unix_sec=last_validation_time_unix_sec,
num_examples_per_validn_batch=num_examples_per_validn_batch)
