diff --git a/modules/deeplearning/icing_dnn.py b/modules/deeplearning/icing_dnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa6d6f5d25a73de84dd91268b1cb1a29095095ff
--- /dev/null
+++ b/modules/deeplearning/icing_dnn.py
@@ -0,0 +1,1021 @@
+import tensorflow as tf
+from util.setup import logdir, modeldir, cachepath, now, ancillary_path, home_dir
+from util.util import EarlyStop, normalize
+from util.geos_nav import get_navigation
+from util.augment import augment_icing
+
+import os, datetime
+import numpy as np
+import pickle
+import h5py
+
+
+LOG_DEVICE_PLACEMENT = False
+
+EVAL_BATCH_SIZE = 8192
+PROC_BATCH_SIZE = 4096
+PROC_BATCH_BUFFER_SIZE = 50000
+
+NumClasses = 2
+if NumClasses == 2:
+ NumLogits = 1
+else:
+ NumLogits = NumClasses
+NumFlightLevels = 5
+
+BATCH_SIZE = 128
+NUM_EPOCHS = 60
+
+
+EARLY_STOP = True
+PATIENCE = 7
+
+USE_EMA = False
+EMA_OVERWRITE_FREQUENCY = 5
+EMA_MOMENTUM = 0.99
+BETA_1 = 0.9
+BETA_2 = 0.999
+
+TRIPLET = False
+CONV3D = False
+
+NOISE_TRAINING = True
+NOISE_STDDEV = 0.001
+DO_AUGMENT = True
+
+IMG_WIDTH = 16 # This is the X,Y dimension length during training
+
+mean_std_dct = {}
+mean_std_file = ancillary_path+'mean_std_lo_hi_l2.pkl'
+f = open(mean_std_file, 'rb')
+mean_std_dct_l2 = pickle.load(f)
+f.close()
+
+mean_std_file = ancillary_path+'mean_std_lo_hi_l1b.pkl'
+f = open(mean_std_file, 'rb')
+mean_std_dct_l1b = pickle.load(f)
+f.close()
+
+mean_std_dct.update(mean_std_dct_l1b)
+mean_std_dct.update(mean_std_dct_l2)
+
+# -- NIGHT L2 -----------------------------
+train_params_l2_night = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
+ 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_acha', 'cld_opd_acha']
+# train_params_l2_night = ['cld_temp_acha', 'supercooled_cloud_fraction', 'cld_emiss_acha', 'cld_reff_acha', 'cld_opd_acha']
+# -- DAY L2 --------------------------------
+train_params_l2_day = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
+ 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
+# 'cld_reff_dcomp_1', 'cld_opd_dcomp_1', 'cld_reff_dcomp_2', 'cld_opd_dcomp_2', 'cld_reff_dcomp_3', 'cld_opd_dcomp_3']
+# train_params_l2_day = ['cld_temp_acha', 'supercooled_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
+# -- DAY L1B --------------------------------
+train_params_l1b_day = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
+ 'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom',
+ 'refl_0_47um_nom', 'refl_0_65um_nom', 'refl_0_86um_nom', 'refl_1_38um_nom', 'refl_1_60um_nom']
+# train_params_l1b_day = ['refl_1_38um_nom', 'refl_1_60um_nom', 'temp_8_5um_nom', 'temp_11_0um_nom']
+# -- NIGHT L1B -------------------------------
+train_params_l1b_night = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
+ 'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom']
+# train_params_l1b_night = ['temp_8_5um_nom', 'temp_11_0um_nom']
+# -- DAY LUNAR ---------------------------------
+# train_params_l1b = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
+# 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
+# ---------------------------------------------
+
+# train_params = train_params_l1b_day + train_params_l2_day
+# -- Zero out params (Experimentation Only) ------------
+zero_out_params = ['cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
+DO_ZERO_OUT = False
+
+
+def build_residual_block(input, drop_rate, num_neurons, activation, block_name, doDropout=True, doBatchNorm=True):
+ with tf.name_scope(block_name):
+ if doDropout:
+ fc = tf.keras.layers.Dropout(drop_rate)(input)
+ fc = tf.keras.layers.Dense(num_neurons, activation=activation)(fc)
+ else:
+ fc = tf.keras.layers.Dense(num_neurons, activation=activation)(input)
+ if doBatchNorm:
+ fc = tf.keras.layers.BatchNormalization()(fc)
+ print(fc.shape)
+ fc_skip = fc
+
+ if doDropout:
+ fc = tf.keras.layers.Dropout(drop_rate)(fc)
+ fc = tf.keras.layers.Dense(num_neurons, activation=activation)(fc)
+ if doBatchNorm:
+ fc = tf.keras.layers.BatchNormalization()(fc)
+ print(fc.shape)
+
+ if doDropout:
+ fc = tf.keras.layers.Dropout(drop_rate)(fc)
+ fc = tf.keras.layers.Dense(num_neurons, activation=activation)(fc)
+ if doBatchNorm:
+ fc = tf.keras.layers.BatchNormalization()(fc)
+ print(fc.shape)
+
+ if doDropout:
+ fc = tf.keras.layers.Dropout(drop_rate)(fc)
+ fc = tf.keras.layers.Dense(num_neurons, activation=None)(fc)
+ if doBatchNorm:
+ fc = tf.keras.layers.BatchNormalization()(fc)
+
+ fc = fc + fc_skip
+ fc = tf.keras.layers.LeakyReLU()(fc)
+ print(fc.shape)
+
+ return fc
+
+
+class IcingIntensityDNN:
+
+ def __init__(self, y_dim_len=IMG_WIDTH, x_dim_len=IMG_WIDTH,
+ day_night='DAY', l1b_or_l2='both', use_flight_altitude=False, datapath=None):
+ print('day_night: ', day_night)
+ print('l1b_or_l2: ', l1b_or_l2)
+ print('use_flight_altitude: ', use_flight_altitude)
+
+ if day_night == 'DAY':
+ self.train_params_l1b = train_params_l1b_day
+ self.train_params_l2 = train_params_l2_day
+ if l1b_or_l2 == 'both':
+ self.train_params = train_params_l1b_day + train_params_l2_day
+ elif l1b_or_l2 == 'l1b':
+ self.train_params = train_params_l1b_day
+ elif l1b_or_l2 == 'l2':
+ self.train_params = train_params_l2_day
+ else:
+ self.train_params_l1b = train_params_l1b_night
+ self.train_params_l2 = train_params_l2_night
+ if l1b_or_l2 == 'both':
+ self.train_params = train_params_l1b_night + train_params_l2_night
+ elif l1b_or_l2 == 'l1b':
+ self.train_params = train_params_l1b_night
+ elif l1b_or_l2 == 'l2':
+ self.train_params = train_params_l2_night
+
+ self.train_data = None
+ self.train_label = None
+ self.test_data = None
+ self.test_label = None
+ self.test_data_denorm = None
+
+ self.train_dataset = None
+ self.inner_train_dataset = None
+ self.test_dataset = None
+ self.eval_dataset = None
+ self.X_img = None
+ self.X_prof = None
+ self.X_u = None
+ self.X_v = None
+ self.X_sfc = None
+ self.inputs = []
+ self.y = None
+ self.handle = None
+ self.inner_handle = None
+ self.in_mem_batch = None
+
+ self.h5f_l1b_trn = None
+ self.h5f_l1b_tst = None
+ self.h5f_l2_trn = None
+ self.h5f_l2_tst = None
+
+ self.logits = None
+
+ self.predict_data = None
+ self.predict_dataset = None
+ self.mean_list = None
+ self.std_list = None
+
+ self.training_op = None
+ self.correct = None
+ self.accuracy = None
+ self.loss = None
+ self.pred_class = None
+ self.variable_averages = None
+
+ self.global_step = None
+
+ self.writer_train = None
+ self.writer_valid = None
+ self.writer_train_valid_loss = None
+
+ self.OUT_OF_RANGE = False
+
+ self.abi = None
+ self.temp = None
+ self.wv = None
+ self.lbfp = None
+ self.sfc = None
+
+ self.in_mem_data_cache = {}
+ self.in_mem_data_cache_test = {}
+
+ self.model = None
+ self.optimizer = None
+ self.ema = None
+ self.train_loss = None
+ self.train_accuracy = None
+ self.test_loss = None
+ self.test_accuracy = None
+ self.test_auc = None
+ self.test_recall = None
+ self.test_precision = None
+ self.test_confusion_matrix = None
+ self.test_true_pos = None
+ self.test_true_neg = None
+ self.test_false_pos = None
+ self.test_false_neg = None
+
+ self.test_labels = []
+ self.test_preds = []
+ self.test_probs = None
+
+ self.learningRateSchedule = None
+ self.num_data_samples = 1
+ self.initial_learning_rate = None
+
+ self.data_dct = None
+ self.cth_max = None
+
+ self.Y_DIM_LEN = y_dim_len
+ self.X_DIM_LEN = x_dim_len
+
+ n_chans = len(self.train_params)
+ self.input = tf.keras.Input(shape=(n_chans))
+
+ self.inputs.append(self.input)
+
+ tf.debugging.set_log_device_placement(LOG_DEVICE_PLACEMENT)
+
+ self.ema_trainable_variables = None
+
+ def get_in_mem_data_batch(self, idxs, is_training):
+
+ # sort these to use as numpy indexing arrays
+ nd_idxs = np.array(idxs)
+ nd_idxs = np.sort(nd_idxs)
+
+ data = []
+ for param in self.train_params:
+ nda = self.get_parameter_data(param, nd_idxs, is_training)
+ nda = normalize(nda, param, mean_std_dct)
+ if DO_ZERO_OUT and is_training:
+ try:
+ zero_out_params.index(param)
+ nda[:,] = 0.0
+ except ValueError:
+ pass
+ data.append(nda)
+ data = np.stack(data)
+ data = np.transpose(data, axes=(1, 0))
+ data = data.astype(np.float32)
+
+ label = self.get_label_data(nd_idxs, is_training)
+ label = np.where(label == -1, 0, label)
+
+ # binary, two class
+ if NumClasses == 2:
+ label = np.where(label != 0, 1, label)
+ label = label.reshape((label.shape[0], 1))
+ elif NumClasses == 3:
+ label = np.where(np.logical_or(label == 1, label == 2), 1, label)
+ label = np.where(np.invert(np.logical_or(label == 0, label == 1)), 2, label)
+ label = label.reshape((label.shape[0], 1))
+
+ return data, label
+
+ def get_parameter_data(self, param, nd_idxs, is_training):
+ if is_training:
+ if param in self.train_params_l1b:
+ h5f = self.h5f_l1b_trn
+ else:
+ h5f = self.h5f_l2_trn
+ else:
+ if param in self.train_params_l1b:
+ h5f = self.h5f_l1b_tst
+ else:
+ h5f = self.h5f_l2_tst
+
+ nda = h5f[param][nd_idxs,]
+ return nda
+
+ def get_label_data(self, nd_idxs, is_training):
+ # Note: labels will be same for nd_idxs across both L1B and L2
+ if is_training:
+ if self.h5f_l1b_trn is not None:
+ h5f = self.h5f_l1b_trn
+ else:
+ h5f = self.h5f_l2_trn
+ else:
+ if self.h5f_l1b_tst is not None:
+ h5f = self.h5f_l1b_tst
+ else:
+ h5f = self.h5f_l2_tst
+
+ label = h5f['icing_intensity'][nd_idxs]
+ label = label.astype(np.int32)
+ return label
+
+ def get_in_mem_data_batch_train(self, idxs):
+ return self.get_in_mem_data_batch(idxs, True)
+
+ def get_in_mem_data_batch_test(self, idxs):
+ return self.get_in_mem_data_batch(idxs, False)
+
+ def get_in_mem_data_batch_eval(self, idxs):
+ # sort these to use as numpy indexing arrays
+ nd_idxs = np.array(idxs)
+ nd_idxs = np.sort(nd_idxs)
+
+ data = []
+ for param in self.train_params:
+ nda = self.data_dct[param][nd_idxs, ]
+ nda = normalize(nda, param, mean_std_dct)
+ data.append(nda)
+ data = np.stack(data)
+ data = data.astype(np.float32)
+
+ return data
+
+ @tf.function(input_signature=[tf.TensorSpec(None, tf.int32)])
+ def data_function(self, indexes):
+ out = tf.numpy_function(self.get_in_mem_data_batch_train, [indexes], [tf.float32, tf.int32])
+ return out
+
+ @tf.function(input_signature=[tf.TensorSpec(None, tf.int32)])
+ def data_function_test(self, indexes):
+ out = tf.numpy_function(self.get_in_mem_data_batch_test, [indexes], [tf.float32, tf.int32])
+ return out
+
+ @tf.function(input_signature=[tf.TensorSpec(None, tf.int32)])
+ def data_function_evaluate(self, indexes):
+ # TODO: modify for user specified altitude
+ out = tf.numpy_function(self.get_in_mem_data_batch_eval, [indexes], [tf.float32])
+ return out
+
+ def get_train_dataset(self, indexes):
+ indexes = list(indexes)
+
+ dataset = tf.data.Dataset.from_tensor_slices(indexes)
+ dataset = dataset.batch(PROC_BATCH_SIZE)
+ dataset = dataset.map(self.data_function, num_parallel_calls=8)
+ dataset = dataset.cache()
+ dataset = dataset.shuffle(PROC_BATCH_BUFFER_SIZE, reshuffle_each_iteration=True)
+ dataset = dataset.prefetch(buffer_size=1)
+ self.train_dataset = dataset
+
+ def get_test_dataset(self, indexes):
+ indexes = list(indexes)
+
+ dataset = tf.data.Dataset.from_tensor_slices(indexes)
+ dataset = dataset.batch(PROC_BATCH_SIZE)
+ dataset = dataset.map(self.data_function_test, num_parallel_calls=8)
+ dataset = dataset.cache()
+ self.test_dataset = dataset
+
+ def get_evaluate_dataset(self, indexes):
+ indexes = list(indexes)
+
+ dataset = tf.data.Dataset.from_tensor_slices(indexes)
+ dataset = dataset.batch(EVAL_BATCH_SIZE)
+ dataset = dataset.map(self.data_function_evaluate, num_parallel_calls=8)
+ dataset = dataset.cache()
+ self.eval_dataset = dataset
+
+ def setup_pipeline(self, filename_l1b_trn, filename_l1b_tst, filename_l2_trn, filename_l2_tst, trn_idxs=None, tst_idxs=None, seed=None):
+ if filename_l1b_trn is not None:
+ self.h5f_l1b_trn = h5py.File(filename_l1b_trn, 'r')
+ if filename_l1b_tst is not None:
+ self.h5f_l1b_tst = h5py.File(filename_l1b_tst, 'r')
+ if filename_l2_trn is not None:
+ self.h5f_l2_trn = h5py.File(filename_l2_trn, 'r')
+ if filename_l2_tst is not None:
+ self.h5f_l2_tst = h5py.File(filename_l2_tst, 'r')
+
+ if trn_idxs is None:
+ # Note: time is same across both L1B and L2 for idxs
+ if self.h5f_l1b_trn is not None:
+ h5f = self.h5f_l1b_trn
+ else:
+ h5f = self.h5f_l2_trn
+ time = h5f['time']
+ # trn_idxs = np.arange(time.shape[0])
+ trn_idxs = np.arange(50000)
+ if seed is not None:
+ np.random.seed(seed)
+ np.random.shuffle(trn_idxs)
+
+ if self.h5f_l1b_tst is not None:
+ h5f = self.h5f_l1b_tst
+ else:
+ h5f = self.h5f_l2_tst
+ time = h5f['time']
+ # tst_idxs = np.arange(time.shape[0])
+ tst_idxs = np.arange(5000)
+ if seed is not None:
+ np.random.seed(seed)
+ np.random.shuffle(tst_idxs)
+
+ self.num_data_samples = trn_idxs.shape[0]
+
+ self.get_train_dataset(trn_idxs)
+ self.get_test_dataset(tst_idxs)
+
+ print('datetime: ', now)
+ print('training and test data: ')
+ print(filename_l1b_trn)
+ print(filename_l1b_tst)
+ print(filename_l2_trn)
+ print(filename_l2_tst)
+ print('---------------------------')
+ print('num train samples: ', self.num_data_samples)
+ print('BATCH SIZE: ', BATCH_SIZE)
+ print('num test samples: ', tst_idxs.shape[0])
+ print('setup_pipeline: Done')
+
+ def setup_test_pipeline(self, filename_l1b, filename_l2):
+
+ if filename_l1b is not None:
+ self.h5f_l1b_tst = h5py.File(filename_l1b, 'r')
+ if filename_l2 is not None:
+ self.h5f_l2_tst = h5py.File(filename_l2, 'r')
+
+ if self.h5f_l1b_tst is not None:
+ h5f = self.h5f_l1b_tst
+ else:
+ h5f = self.h5f_l2_tst
+
+ time = h5f['time']
+ flt_alt = h5f['flight_altitude'][:]
+ tst_idxs = np.arange(time.shape[0])
+
+ self.num_data_samples = len(tst_idxs)
+
+ self.get_test_dataset(tst_idxs)
+
+ print('num test samples: ', tst_idxs.shape[0])
+ print('setup_test_pipeline: Done')
+
+ def setup_eval_pipeline(self, data_dct, num_tiles=1):
+ self.data_dct = data_dct
+ # self.cth_max = data_dct.get('cth_high_avg', None)
+ idxs = np.arange(num_tiles)
+ self.num_data_samples = idxs.shape[0]
+
+ self.get_evaluate_dataset(idxs)
+
+ def build_dnn(self, input_layer=None):
+ print('build fully connected layer')
+ drop_rate = 0.5
+
+ # activation = tf.nn.relu
+ # activation = tf.nn.elu
+ activation = tf.nn.leaky_relu
+ momentum = 0.99
+
+ if input_layer is not None:
+ flat = input_layer
+ n_hidden = input_layer.shape[1]
+ else:
+ flat = self.input
+ n_hidden = self.input.shape[1]
+
+ fac = 2
+
+ fc = build_residual_block(flat, drop_rate, fac * n_hidden, activation, 'Residual_Block_1', doDropout=True,
+ doBatchNorm=True)
+
+ fc = build_residual_block(fc, drop_rate, fac * n_hidden, activation, 'Residual_Block_2', doDropout=True,
+ doBatchNorm=True)
+
+ fc = build_residual_block(fc, drop_rate, fac * n_hidden, activation, 'Residual_Block_3', doDropout=True,
+ doBatchNorm=True)
+
+ fc = build_residual_block(fc, drop_rate, fac * n_hidden, activation, 'Residual_Block_4', doDropout=True,
+ doBatchNorm=True)
+
+ fc = build_residual_block(fc, drop_rate, fac * n_hidden, activation, 'Residual_Block_5', doDropout=True,
+ doBatchNorm=True)
+
+ # fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_6', doDropout=True, doBatchNorm=True)
+ #
+ # fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_7', doDropout=True, doBatchNorm=True)
+ #
+ # fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_8', doDropout=True, doBatchNorm=True)
+
+ fc = tf.keras.layers.Dense(n_hidden, activation=activation)(fc)
+ fc = tf.keras.layers.BatchNormalization()(fc)
+
+ if NumClasses == 2:
+ activation = tf.nn.sigmoid # For binary
+ else:
+ activation = tf.nn.softmax # For multi-class
+
+ # Called logits, but these are actually probabilities, see activation
+ logits = tf.keras.layers.Dense(NumLogits, activation=activation)(fc)
+ print(logits.shape)
+
+ self.logits = logits
+
+ def build_training(self):
+ if NumClasses == 2:
+ self.loss = tf.keras.losses.BinaryCrossentropy(from_logits=False) # for two-class only
+ else:
+ self.loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False) # For multi-class
+
+ # decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
+ initial_learning_rate = 0.001
+ decay_rate = 0.95
+ steps_per_epoch = int(self.num_data_samples/BATCH_SIZE) # one epoch
+ decay_steps = int(steps_per_epoch)
+ print('initial rate, decay rate, steps/epoch, decay steps: ', initial_learning_rate, decay_rate, steps_per_epoch, decay_steps)
+
+ self.learningRateSchedule = tf.keras.optimizers.schedules.ExponentialDecay(initial_learning_rate, decay_steps, decay_rate)
+
+ optimizer = tf.keras.optimizers.Adam(learning_rate=self.learningRateSchedule,
+ beta_1=BETA_1, beta_2=BETA_2)
+ # use_ema=USE_EMA,
+ # ema_momentum=EMA_MOMENTUM,
+ # ema_overwrite_frequency=EMA_OVERWRITE_FREQUENCY)
+
+ self.optimizer = optimizer
+ self.initial_learning_rate = initial_learning_rate
+
+ def build_evaluation(self):
+ self.train_loss = tf.keras.metrics.Mean(name='train_loss')
+ self.test_loss = tf.keras.metrics.Mean(name='test_loss')
+
+ if NumClasses == 2:
+ self.train_accuracy = tf.keras.metrics.BinaryAccuracy(name='train_accuracy')
+ self.test_accuracy = tf.keras.metrics.BinaryAccuracy(name='test_accuracy')
+ self.test_auc = tf.keras.metrics.AUC(name='test_auc')
+ self.test_recall = tf.keras.metrics.Recall(name='test_recall')
+ self.test_precision = tf.keras.metrics.Precision(name='test_precision')
+ self.test_true_neg = tf.keras.metrics.TrueNegatives(name='test_true_neg')
+ self.test_true_pos = tf.keras.metrics.TruePositives(name='test_true_pos')
+ self.test_false_neg = tf.keras.metrics.FalseNegatives(name='test_false_neg')
+ self.test_false_pos = tf.keras.metrics.FalsePositives(name='test_false_pos')
+ else:
+ self.train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
+ self.test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
+
+ @tf.function
+ def train_step(self, mini_batch):
+ inputs = [mini_batch[0]]
+ labels = mini_batch[1]
+ with tf.GradientTape() as tape:
+ pred = self.model(inputs, training=True)
+ pred = tf.reshape(pred, (pred.shape[0], NumLogits))
+ loss = self.loss(labels, pred)
+ total_loss = loss
+ if len(self.model.losses) > 0:
+ reg_loss = tf.math.add_n(self.model.losses)
+ total_loss = loss + reg_loss
+ gradients = tape.gradient(total_loss, self.model.trainable_variables)
+ self.optimizer.apply_gradients(zip(gradients, self.model.trainable_variables))
+
+ self.train_loss(loss)
+ self.train_accuracy(labels, pred)
+
+ return loss
+
+ @tf.function
+ def test_step(self, mini_batch):
+ inputs = [mini_batch[0]]
+ labels = mini_batch[1]
+ pred = self.model(inputs, training=False)
+ pred = tf.reshape(pred, (pred.shape[0], NumLogits))
+ t_loss = self.loss(labels, pred)
+
+ self.test_loss(t_loss)
+ self.test_accuracy(labels, pred)
+ if NumClasses == 2:
+ self.test_auc(labels, pred)
+ self.test_recall(labels, pred)
+ self.test_precision(labels, pred)
+ self.test_true_neg(labels, pred)
+ self.test_true_pos(labels, pred)
+ self.test_false_neg(labels, pred)
+ self.test_false_pos(labels, pred)
+
+ def predict(self, mini_batch):
+ inputs = [mini_batch[0], mini_batch[1]]
+ labels = mini_batch[2]
+ pred = self.model(inputs, training=False)
+ pred = tf.reshape(pred, (pred.shape[0], NumLogits))
+ t_loss = self.loss(labels, pred)
+
+ self.test_labels.append(labels)
+ self.test_preds.append(pred.numpy())
+
+ self.test_loss(t_loss)
+ self.test_accuracy(labels, pred)
+ if NumClasses == 2:
+ self.test_auc(labels, pred)
+ self.test_recall(labels, pred)
+ self.test_precision(labels, pred)
+ self.test_true_neg(labels, pred)
+ self.test_true_pos(labels, pred)
+ self.test_false_neg(labels, pred)
+ self.test_false_pos(labels, pred)
+
+ def reset_test_metrics(self):
+ self.test_loss.reset_states()
+ self.test_accuracy.reset_states()
+ if NumClasses == 2:
+ self.test_auc.reset_states()
+ self.test_recall.reset_states()
+ self.test_precision.reset_states()
+ self.test_true_neg.reset_states()
+ self.test_true_pos.reset_states()
+ self.test_false_neg.reset_states()
+ self.test_false_pos.reset_states()
+
+ def get_metrics(self):
+ recall = self.test_recall.result()
+ precsn = self.test_precision.result()
+ f1 = 2 * (precsn * recall) / (precsn + recall)
+
+ tn = self.test_true_neg.result()
+ tp = self.test_true_pos.result()
+ fn = self.test_false_neg.result()
+ fp = self.test_false_pos.result()
+
+ mcc = ((tp * tn) - (fp * fn)) / np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
+ return f1, mcc
+
+ def do_training(self, ckpt_dir=None):
+
+ if ckpt_dir is None:
+ if not os.path.exists(modeldir):
+ os.mkdir(modeldir)
+ ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=self.model)
+ ckpt_manager = tf.train.CheckpointManager(ckpt, modeldir, max_to_keep=3)
+ else:
+ ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=self.model)
+ ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
+ ckpt.restore(ckpt_manager.latest_checkpoint)
+
+ self.writer_train = tf.summary.create_file_writer(os.path.join(logdir, 'plot_train'))
+ self.writer_valid = tf.summary.create_file_writer(os.path.join(logdir, 'plot_valid'))
+ self.writer_train_valid_loss = tf.summary.create_file_writer(os.path.join(logdir, 'plot_train_valid_loss'))
+
+ step = 0
+ total_time = 0
+ best_test_loss = np.finfo(dtype=np.float32).max
+ best_test_acc = 0
+ best_test_recall = 0
+ best_test_precision = 0
+ best_test_auc = 0
+ best_test_f1 = 0
+ best_test_mcc = 0
+
+ if EARLY_STOP:
+ es = EarlyStop(patience=PATIENCE)
+
+ for epoch in range(NUM_EPOCHS):
+ self.train_loss.reset_states()
+ self.train_accuracy.reset_states()
+
+ t0 = datetime.datetime.now().timestamp()
+
+ proc_batch_cnt = 0
+ n_samples = 0
+
+ for data0, label in self.train_dataset:
+ trn_ds = tf.data.Dataset.from_tensor_slices((data0, label))
+ trn_ds = trn_ds.batch(BATCH_SIZE)
+ for mini_batch in trn_ds:
+ if self.learningRateSchedule is not None:
+ loss = self.train_step(mini_batch)
+
+ if (step % 100) == 0:
+
+ with self.writer_train.as_default():
+ tf.summary.scalar('loss_trn', loss.numpy(), step=step)
+ # tf.summary.scalar('learning_rate', self.optimizer.lr.numpy(), step=step)
+ tf.summary.scalar('num_train_steps', step, step=step)
+ tf.summary.scalar('num_epochs', epoch, step=step)
+
+ self.reset_test_metrics()
+ for data0_tst, label_tst in self.test_dataset:
+ tst_ds = tf.data.Dataset.from_tensor_slices((data0_tst, label_tst))
+ tst_ds = tst_ds.batch(BATCH_SIZE)
+ for mini_batch_test in tst_ds:
+ self.test_step(mini_batch_test)
+
+ if NumClasses == 2:
+ f1, mcc = self.get_metrics()
+
+ with self.writer_valid.as_default():
+ tf.summary.scalar('loss_val', self.test_loss.result(), step=step)
+ tf.summary.scalar('acc_val', self.test_accuracy.result(), step=step)
+ if NumClasses == 2:
+ tf.summary.scalar('auc_val', self.test_auc.result(), step=step)
+ tf.summary.scalar('recall_val', self.test_recall.result(), step=step)
+ tf.summary.scalar('prec_val', self.test_precision.result(), step=step)
+ tf.summary.scalar('f1_val', f1, step=step)
+ tf.summary.scalar('mcc_val', mcc, step=step)
+ tf.summary.scalar('num_train_steps', step, step=step)
+ tf.summary.scalar('num_epochs', epoch, step=step)
+
+ with self.writer_train_valid_loss.as_default():
+ tf.summary.scalar('loss_trn', loss.numpy(), step=step)
+ tf.summary.scalar('loss_val', self.test_loss.result(), step=step)
+
+ print('****** test loss, acc, lr: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy())
+ # self.optimizer.lr.numpy())
+
+ step += 1
+ print('train loss: ', loss.numpy())
+
+ proc_batch_cnt += 1
+ n_samples += data0.shape[0]
+ print('proc_batch_cnt: ', proc_batch_cnt, n_samples)
+
+ t1 = datetime.datetime.now().timestamp()
+ print('End of Epoch: ', epoch+1, 'elapsed time: ', (t1-t0))
+ total_time += (t1-t0)
+
+ self.reset_test_metrics()
+ for data0, label in self.test_dataset:
+ ds = tf.data.Dataset.from_tensor_slices((data0, label))
+ ds = ds.batch(BATCH_SIZE)
+ for mini_batch in ds:
+ self.test_step(mini_batch)
+
+ if NumClasses == 2:
+ f1, mcc = self.get_metrics()
+ print('loss, acc, recall, precision, auc, f1, mcc: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy(),
+ self.test_recall.result().numpy(), self.test_precision.result().numpy(), self.test_auc.result().numpy(), f1.numpy(), mcc.numpy())
+ else:
+ print('loss, acc: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy())
+ print('------------------------------------------------------')
+
+ tst_loss = self.test_loss.result().numpy()
+ if tst_loss < best_test_loss:
+ best_test_loss = tst_loss
+ if NumClasses == 2:
+ best_test_acc = self.test_accuracy.result().numpy()
+ best_test_recall = self.test_recall.result().numpy()
+ best_test_precision = self.test_precision.result().numpy()
+ best_test_auc = self.test_auc.result().numpy()
+ best_test_f1 = f1.numpy()
+ best_test_mcc = mcc.numpy()
+
+ ckpt_manager.save()
+
+ if EARLY_STOP and es.check_stop(tst_loss):
+ break
+
+ print('total time: ', total_time)
+ self.writer_train.close()
+ self.writer_valid.close()
+ self.writer_train_valid_loss.close()
+
+ if self.h5f_l1b_trn is not None:
+ self.h5f_l1b_trn.close()
+ if self.h5f_l1b_tst is not None:
+ self.h5f_l1b_tst.close()
+ if self.h5f_l2_trn is not None:
+ self.h5f_l2_trn.close()
+ if self.h5f_l2_tst is not None:
+ self.h5f_l2_tst.close()
+
+ print('best stats: ', best_test_loss, best_test_acc, best_test_recall, best_test_precision, best_test_auc, best_test_f1, best_test_mcc)
+
+ f = open(home_dir+'/best_stats_'+now+'.pkl', 'wb')
+ pickle.dump((best_test_loss, best_test_acc, best_test_recall, best_test_precision, best_test_auc, best_test_f1, best_test_mcc), f)
+ f.close()
+
+ def build_model(self):
+ self.build_dnn()
+ self.model = tf.keras.Model(self.inputs, self.logits)
+
+ def restore(self, ckpt_dir):
+
+ ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=self.model)
+
+ ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
+
+ ckpt.restore(ckpt_manager.latest_checkpoint)
+
+ self.reset_test_metrics()
+
+ for data0, data1, label in self.test_dataset:
+ ds = tf.data.Dataset.from_tensor_slices((data0, data1, label))
+ ds = ds.batch(BATCH_SIZE)
+ for mini_batch_test in ds:
+ self.predict(mini_batch_test)
+ f1, mcc = self.get_metrics()
+ print('loss, acc: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy(), self.test_recall.result().numpy(),
+ self.test_precision.result().numpy(), self.test_auc.result().numpy(), f1.numpy(), mcc.numpy())
+
+ labels = np.concatenate(self.test_labels)
+ self.test_labels = labels
+
+ preds = np.concatenate(self.test_preds)
+ self.test_probs = preds
+
+ if NumClasses == 2:
+ preds = np.where(preds > 0.5, 1, 0)
+ else:
+ preds = np.argmax(preds, axis=1)
+
+ self.test_preds = preds
+
+ def do_evaluate(self, prob_thresh=0.5):
+
+ self.reset_test_metrics()
+
+ pred_s = []
+
+ for data in self.eval_dataset:
+ pred = self.model([data], training=False)
+ pred_s.append(pred)
+
+ preds = np.concatenate(pred_s)
+ preds = np.squeeze(preds)
+ self.test_probs = preds
+
+ if NumClasses == 2:
+ preds = np.where(preds > prob_thresh, 1, 0)
+ else:
+ preds = np.argmax(preds, axis=1)
+ self.test_preds = preds
+
+ def run(self, filename_l1b_trn, filename_l1b_tst, filename_l2_trn, filename_l2_tst):
+ self.setup_pipeline(filename_l1b_trn, filename_l1b_tst, filename_l2_trn, filename_l2_tst)
+ self.build_model()
+ self.build_training()
+ self.build_evaluation()
+ self.do_training()
+
+ def run_restore(self, filename_l1b, filename_l2, ckpt_dir):
+ self.setup_test_pipeline(filename_l1b, filename_l2)
+ self.build_model()
+ self.build_training()
+ self.build_evaluation()
+ self.restore(ckpt_dir)
+
+ if self.h5f_l1b_tst is not None:
+ self.h5f_l1b_tst.close()
+ if self.h5f_l2_tst is not None:
+ self.h5f_l2_tst.close()
+
+ # def run_evaluate(self, filename, ckpt_dir):
+ # data_dct, ll, cc = make_for_full_domain_predict(filename, name_list=self.train_params)
+ # self.setup_eval_pipeline(data_dct, len(ll))
+ # self.build_model()
+ # self.build_training()
+ # self.build_evaluation()
+ # self.do_evaluate(ckpt_dir)
+
+
+def run_restore_static(filename_l1b, filename_l2, ckpt_dir_s_path, day_night='DAY', l1b_or_l2='both',
+ use_flight_altitude=False, out_file=None):
+ ckpt_dir_s = os.listdir(ckpt_dir_s_path)
+ cm_s = []
+ prob_s = []
+ labels = None
+
+ for ckpt in ckpt_dir_s:
+ ckpt_dir = ckpt_dir_s_path + ckpt
+ if not os.path.isdir(ckpt_dir):
+ continue
+ nn = IcingIntensityDNN(day_night=day_night, l1b_or_l2=l1b_or_l2, use_flight_altitude=use_flight_altitude)
+ nn.run_restore(filename_l1b, filename_l2, ckpt_dir)
+ cm_s.append(tf.math.confusion_matrix(nn.test_labels.flatten(), nn.test_preds.flatten()))
+ prob_s.append(nn.test_probs.flatten())
+ if labels is None: # These should be the same
+ labels = nn.test_labels.flatten()
+
+ num = len(cm_s)
+ cm_avg = cm_s[0]
+ prob_avg = prob_s[0]
+ for k in range(num-1):
+ cm_avg += cm_s[k+1]
+ prob_avg += prob_s[k+1]
+ cm_avg /= num
+ prob_avg /= num
+
+ print(cm_avg)
+
+ if out_file is not None:
+ np.save(out_file, [labels, prob_avg])
+ else:
+ return labels, prob_avg, cm_avg
+
+
+def run_evaluate_static(data_dct, num_tiles, ckpt_dir_s_path, day_night='DAY', l1b_or_l2='both', satellite='GOES16',
+ prob_thresh=0.5, flight_levels=[0, 1, 2, 3, 4], use_flight_altitude=False):
+
+ ckpt_dir_s = os.listdir(ckpt_dir_s_path)
+ ckpt_dir = ckpt_dir_s_path + ckpt_dir_s[0]
+
+ if not use_flight_altitude:
+ flight_levels = [0]
+
+ probs_dct = {flvl: None for flvl in flight_levels}
+ preds_dct = {flvl: None for flvl in flight_levels}
+
+ nn = IcingIntensityDNN(day_night=day_night, l1b_or_l2=l1b_or_l2, satellite=satellite, use_flight_altitude=use_flight_altitude)
+ nn.num_data_samples = num_tiles
+ nn.build_model()
+ nn.build_training()
+ nn.build_evaluation()
+
+ ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=nn.model)
+ ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
+ ckpt.restore(ckpt_manager.latest_checkpoint)
+
+ for flvl in flight_levels:
+ nn.flight_level = flvl
+ nn.setup_eval_pipeline(data_dct, num_tiles)
+ nn.do_evaluate(prob_thresh=prob_thresh)
+
+ probs_dct[flvl] = nn.test_probs.flatten()
+ preds_dct[flvl] = nn.test_preds.flatten()
+
+ return preds_dct, probs_dct
+
+
+def run_evaluate_static_2(model, data_dct, num_tiles, prob_thresh=0.5, flight_levels=[0, 1, 2, 3, 4]):
+
+ probs_dct = {flvl: None for flvl in flight_levels}
+ preds_dct = {flvl: None for flvl in flight_levels}
+
+ for flvl in flight_levels:
+ model.flight_level = flvl
+ model.setup_eval_pipeline(data_dct, num_tiles)
+ model.do_evaluate(prob_thresh=prob_thresh)
+
+ probs_dct[flvl] = model.test_probs.flatten()
+ preds_dct[flvl] = model.test_preds.flatten()
+
+ return preds_dct, probs_dct
+
+
+# This probable just won't work, maybe not even a good idea?
+# Keep for example of accessing model weights --------------------------------
+def run_average_models(ckpt_dir_s_path, day_night='NIGHT', l1b_andor_l2='BOTH', use_flight_altitude=False):
+
+ ckpt_dir_s = os.listdir(ckpt_dir_s_path)
+ model_weight_s = []
+ for ckpt in ckpt_dir_s:
+ ckpt_dir = ckpt_dir_s_path + ckpt
+ if not os.path.isdir(ckpt_dir):
+ continue
+ model = load_model(ckpt_dir, day_night=day_night, l1b_andor_l2=l1b_andor_l2, use_flight_altitude=use_flight_altitude)
+ k_model = model.model
+ model_weight_s.append(k_model.get_weights())
+ print('done loading models ******************************************')
+
+ num_model_lyrs = len(model_weight_s[0])
+ model_lyrs = [[] for k in range(num_model_lyrs)]
+
+ avg_model_weights = []
+ for m in model_weight_s:
+ for k, w in enumerate(m):
+ model_lyrs[k].append(w)
+ for lyr in model_lyrs:
+ avg_model_weights.append(np.mean(np.stack(lyr, axis=-1), axis=-1))
+
+ # -- Make a new model for the averaged weights
+ new_model = IcingIntensityDNN(day_night=day_night, l1b_or_l2=l1b_andor_l2, use_flight_altitude=use_flight_altitude)
+ new_model.build_model()
+ new_model.build_training()
+ new_model.build_evaluation()
+
+ # -- Save the averaged weights to a new the model
+ if not os.path.exists(modeldir):
+ os.mkdir(modeldir)
+ ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=new_model.model)
+ ckpt_manager = tf.train.CheckpointManager(ckpt, modeldir, max_to_keep=3)
+
+ new_model.model.set_weights(avg_model_weights)
+ ckpt_manager.save()
+
+ return
+
+
+def load_model(model_path, day_night='NIGHT', l1b_andor_l2='BOTH', use_flight_altitude=False):
+ ckpt_dir_s = os.listdir(model_path)
+ ckpt_dir = model_path + ckpt_dir_s[0]
+
+ model = IcingIntensityDNN(day_night=day_night, l1b_or_l2=l1b_andor_l2, use_flight_altitude=use_flight_altitude)
+ model.build_model()
+ model.build_training()
+ model.build_evaluation()
+
+ ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=model.model)
+ ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
+ ckpt.restore(ckpt_manager.latest_checkpoint)
+
+ return model
+
+
+if __name__ == "__main__":
+ nn = IcingIntensityDNN()
+ nn.run('matchup_filename')