icing_dnn.py 37.59 KiB
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='l2', 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(1, 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])
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])
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 = 4
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_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')