def apply_2d_or_3d_cnn(model_object,
radar_image_matrix,
sounding_matrix=None,
num_examples_per_batch=100,
verbose=False,
return_features=False,
feature_layer_name=None):
"""Applies CNN to either 2-D or 3-D radar images (and possibly soundings).
:param model_object: Trained instance of `keras.models.Model` or
`keras.models.Sequential`.
:param radar_image_matrix: E-by-M-by-N-by-C numpy array of storm-centered
radar images.
:param sounding_matrix: [may be None]
numpy array (E x H_s x F_s) of storm-centered sounding.
:param num_examples_per_batch: Number of examples per batch. Will apply CNN
to this many examples at once. If `num_examples_per_batch is None`, will
apply CNN to all examples at once.
:param verbose: Boolean flag. If True, will print progress messages.
:param return_features: Boolean flag. If True, this method will return
features (activations of an intermediate layer). If False, this method
will return probabilistic predictions.
:param feature_layer_name: [used only if return_features = True]
Name of layer for which features will be returned.
If return_features = True...
:return: feature_matrix: E-by-Z numpy array of features, where Z = number of
outputs from the given layer.
If return_features = False...
:return: class_probability_matrix: E-by-K numpy array of class
probabilities. class_probability_matrix[i, k] is the forecast
probability that the [i]th storm object belongs to the [k]th class.
Classes are mutually exclusive and collectively exhaustive, so the sum
across each row is 1.
"""
dl_utils.check_radar_images(radar_image_matrix=radar_image_matrix,
min_num_dimensions=4,
max_num_dimensions=5)
num_examples = radar_image_matrix.shape[0]
if sounding_matrix is not None:
dl_utils.check_soundings(sounding_matrix=sounding_matrix,
num_examples=num_examples)
if num_examples_per_batch is None:
num_examples_per_batch = num_examples + 0
else:
error_checking.assert_is_integer(num_examples_per_batch)
error_checking.assert_is_greater(num_examples_per_batch, 0)
num_examples_per_batch = min([num_examples_per_batch, num_examples])
error_checking.assert_is_boolean(verbose)
error_checking.assert_is_boolean(return_features)
if return_features:
model_object_to_use = model_to_feature_generator(
model_object=model_object, feature_layer_name=feature_layer_name)
else:
model_object_to_use = model_object
output_matrix = None
for i in range(0, num_examples, num_examples_per_batch):
this_first_index = i
this_last_index = min(
[i + num_examples_per_batch - 1, num_examples - 1])
these_indices = numpy.linspace(this_first_index,
this_last_index,
num=this_last_index - this_first_index +
1,
dtype=int)
if verbose:
print('Applying model to examples {0:d}-{1:d} of {2:d}...').format(
this_first_index + 1, this_last_index + 1, num_examples)
if sounding_matrix is None:
these_outputs = model_object_to_use.predict(
radar_image_matrix[these_indices, ...],
batch_size=len(these_indices))
else:
these_outputs = model_object_to_use.predict(
[
radar_image_matrix[these_indices, ...],
sounding_matrix[these_indices, ...]
],
batch_size=len(these_indices))
if output_matrix is None:
output_matrix = these_outputs + 0.
else:
output_matrix = numpy.concatenate((output_matrix, these_outputs),
axis=0)
if verbose:
print 'Have applied model to all {0:d} examples!'.format(num_examples)
if return_features:
return output_matrix
return _binary_probabilities_to_matrix(output_matrix)
def apply_2d3d_cnn(model_object,
reflectivity_matrix_dbz,
azimuthal_shear_matrix_s01,
sounding_matrix=None,
num_examples_per_batch=100,
verbose=False,
return_features=False,
feature_layer_name=None):
"""Applies CNN to both 2-D and 3-D radar images (and possibly soundings).
M = number of rows in each reflectivity image
N = number of columns in each reflectivity image
:param model_object: Trained instance of `keras.models.Model` or
`keras.models.Sequential`.
:param reflectivity_matrix_dbz: numpy array (E x M x N x H_r x 1) of
storm-centered reflectivity images.
:param azimuthal_shear_matrix_s01: numpy array (E x 2M x 2N x C) of
storm-centered azimuthal-shear images.
:param sounding_matrix: See doc for `apply_2d_or_3d_cnn`.
:param num_examples_per_batch: Same.
:param verbose: Same.
:param return_features: Same.
:param feature_layer_name: Same.
If return_features = True...
:return: feature_matrix: See doc for `apply_2d_or_3d_cnn`.
If return_features = False...
:return: class_probability_matrix: See doc for `apply_2d_or_3d_cnn`.
"""
dl_utils.check_radar_images(radar_image_matrix=reflectivity_matrix_dbz,
min_num_dimensions=5,
max_num_dimensions=5)
dl_utils.check_radar_images(radar_image_matrix=azimuthal_shear_matrix_s01,
min_num_dimensions=4,
max_num_dimensions=4)
if sounding_matrix is not None:
dl_utils.check_soundings(sounding_matrix=sounding_matrix,
num_examples=reflectivity_matrix_dbz.shape[0])
dl_utils.check_soundings(
sounding_matrix=sounding_matrix,
num_examples=azimuthal_shear_matrix_s01.shape[0])
num_examples = reflectivity_matrix_dbz.shape[0]
if num_examples_per_batch is None:
num_examples_per_batch = num_examples + 0
else:
error_checking.assert_is_integer(num_examples_per_batch)
error_checking.assert_is_greater(num_examples_per_batch, 0)
num_examples_per_batch = min([num_examples_per_batch, num_examples])
error_checking.assert_is_boolean(verbose)
error_checking.assert_is_boolean(return_features)
if return_features:
model_object_to_use = model_to_feature_generator(
model_object=model_object, feature_layer_name=feature_layer_name)
else:
model_object_to_use = model_object
output_matrix = None
for i in range(0, num_examples, num_examples_per_batch):
this_first_index = i
this_last_index = min(
[i + num_examples_per_batch - 1, num_examples - 1])
these_indices = numpy.linspace(this_first_index,
this_last_index,
num=this_last_index - this_first_index +
1,
dtype=int)
if verbose:
print('Applying model to examples {0:d}-{1:d} of {2:d}...').format(
this_first_index + 1, this_last_index + 1, num_examples)
if sounding_matrix is None:
these_outputs = model_object_to_use.predict(
[
reflectivity_matrix_dbz[these_indices, ...],
azimuthal_shear_matrix_s01[these_indices, ...]
],
batch_size=len(these_indices))
else:
these_outputs = model_object_to_use.predict(
[
reflectivity_matrix_dbz[these_indices, ...],
azimuthal_shear_matrix_s01[these_indices, ...],
sounding_matrix[these_indices, ...]
],
batch_size=len(these_indices))
if output_matrix is None:
output_matrix = these_outputs + 0.
else:
output_matrix = numpy.concatenate((output_matrix, these_outputs),
axis=0)
if verbose:
print 'Have applied model to all {0:d} examples!'.format(num_examples)
if return_features:
return output_matrix
return _binary_probabilities_to_matrix(output_matrix)
def apply_cnn_soundings_only(
model_object, sounding_matrix, num_examples_per_batch=100,
verbose=False, return_features=False, feature_layer_name=None):
"""Applies CNN to soundings only.
:param model_object: See doc for `apply_2d_or_3d_cnn`.
:param sounding_matrix: numpy array (E x H_s x F_s) of storm-centered
soundings.
:param num_examples_per_batch: See doc for `apply_2d_or_3d_cnn`.
:param verbose: Same.
:param return_features: Same.
:param feature_layer_name: Same.
If return_features = True...
:return: feature_matrix: See doc for `apply_2d_or_3d_cnn`.
If return_features = False...
:return: class_probability_matrix: See doc for `apply_2d_or_3d_cnn`.
"""
num_examples = sounding_matrix.shape[0]
if sounding_matrix is not None:
dl_utils.check_soundings(
sounding_matrix=sounding_matrix, num_examples=num_examples)
if num_examples_per_batch is None:
num_examples_per_batch = num_examples + 0
else:
error_checking.assert_is_integer(num_examples_per_batch)
error_checking.assert_is_greater(num_examples_per_batch, 0)
num_examples_per_batch = min([num_examples_per_batch, num_examples])
error_checking.assert_is_boolean(verbose)
error_checking.assert_is_boolean(return_features)
if return_features:
model_object_to_use = model_to_feature_generator(
model_object=model_object, feature_layer_name=feature_layer_name)
else:
model_object_to_use = model_object
output_matrix = None
for i in range(0, num_examples, num_examples_per_batch):
this_first_index = i
this_last_index = min(
[i + num_examples_per_batch - 1, num_examples - 1]
)
these_indices = numpy.linspace(
this_first_index, this_last_index,
num=this_last_index - this_first_index + 1, dtype=int)
if verbose:
print((
'Applying model to examples {0:d}-{1:d} of {2:d}...'
).format(
this_first_index + 1, this_last_index + 1, num_examples
))
these_outputs = model_object_to_use.predict(
sounding_matrix[these_indices, ...], batch_size=len(these_indices)
)
if output_matrix is None:
output_matrix = these_outputs + 0.
else:
output_matrix = numpy.concatenate(
(output_matrix, these_outputs), axis=0
)
if verbose:
print('Have applied model to all {0:d} examples!'.format(num_examples))
if return_features:
return output_matrix
if len(output_matrix.shape) == 2 and output_matrix.shape[1] == 1:
output_matrix = output_matrix[:, 0]
if len(output_matrix.shape) == 1:
output_matrix = dl_utils.event_probs_to_multiclass(output_matrix)
return output_matrix