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cloudheight_mgpus.py
cloudheight_mgpus.py 31.93 KiB
import tensorflow as tf
from util.setup import logdir, modeldir, cachepath
import subprocess
import os, datetime
import numpy as np
import xarray as xr
import pickle
from deeplearning.amv_raob import get_bounding_gfs_files, convert_file, get_images, get_interpolated_profile, \
split_matchup, shuffle_dict, get_interpolated_scalar, get_num_samples, get_time_tuple_utc, get_profile
LOG_DEVICE_PLACEMENT = False
CACHE_DATA_IN_MEM = True
CACHE_GFS = True
DISK_CACHE = True
NumLabels = 1
NUM_EPOCHS = 200
PROC_BATCH_SIZE = 60
BATCH_SIZE = 256 # Per replica
BATCH_SIZE = BATCH_SIZE * 3
PROC_BATCH_BUFFER_SIZE = 50000
TRACK_MOVING_AVERAGE = False
DAY_NIGHT = 'ANY'
TRIPLET = False
CONV3D = False
abi_2km_channels = ['14', '08', '11', '13', '15', '16']
# abi_2km_channels = ['08', '09', '10']
abi_hkm_channels = []
# abi_channels = abi_2km_channels + abi_hkm_channels
abi_channels = abi_2km_channels
abi_mean = {'08': 236.014, '14': 275.229, '02': 0.049, '11': 273.582, '13': 275.796, '15': 272.928, '16': 260.956, '09': 244.502, '10': 252.375}
abi_std = {'08': 7.598, '14': 20.443, '02': 0.082, '11': 19.539, '13': 20.431, '15': 20.104, '16': 15.720, '09': 9.827, '10': 11.765}
abi_valid_range = {'02': [0.001, 120], '08': [150, 350], '14': [150, 350], '11': [150, 350], '13': [150, 350], '15': [150, 350], '16': [150, 350], '09': [150, 350], '10': [150, 350]}
abi_half_width = {'08': 12, '14': 12, '02': 48, '11': 12, '13': 12, '15': 12, '16': 12, '09': 12, '10': 12}
#abi_half_width = {'08': 6, '14': 6, '02': 24, '11': 6, '13': 6, '15': 6, '16': 6, '09': 6, '10': 6}
#abi_half_width = {'08': 3, '14': 3, '02': 12, '11': 3, '13': 3, '15': 3, '16': 3, '09': 3, '10': 3}
abi_stride = {'08': 1, '14': 1, '02': 4, '11': 1, '13': 1, '15': 1, '16': 1, '09': 1, '10': 1}
img_width = 24
#img_width = 12
#img_width = 6
NUM_VERT_LEVELS = 26
NUM_VERT_PARAMS = 2
gfs_mean_temp = [225.481110,
218.950729,
215.830338,
212.063187,
209.348038,
208.787033,
213.728928,
218.298264,
223.061020,
229.190445,
236.095215,
242.589493,
248.333237,
253.357071,
257.768646,
261.599396,
264.793671,
267.667603,
270.408478,
272.841919,
274.929138,
276.826294,
277.786865,
278.834198,
279.980408,
281.308380]
gfs_mean_temp = np.array(gfs_mean_temp)
gfs_mean_temp = np.reshape(gfs_mean_temp, (1, gfs_mean_temp.shape[0]))
gfs_std_temp = [13.037852,
11.669035,
10.775956,
10.428216,
11.705231,
12.352798,
8.892235,
7.101064,
8.505628,
10.815929,
12.139559,
12.720000,
12.929382,
13.023590,
13.135534,
13.543551,
14.449997,
15.241049,
15.638563,
15.943666,
16.178715,
16.458992,
16.700863,
17.109579,
17.630177,
18.080544]
gfs_std_temp = np.array(gfs_std_temp)
gfs_std_temp = np.reshape(gfs_std_temp, (1, gfs_std_temp.shape[0]))
mean_std_dict = {'temperature': (gfs_mean_temp, gfs_std_temp), 'surface temperature': (279.35, 22.81),
'MSL pressure': (1010.64, 13.46), 'tropopause temperature': (208.17, 11.36), 'tropopause pressure': (219.62, 78.79)}
valid_range_dict = {'temperature': (150, 350), 'surface temperature': (150, 350), 'MSL pressure': (800, 1050),
'tropopause temperature': (150, 250), 'tropopause pressure': (100, 500)}
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 CloudHeightNN:
def __init__(self, gpu_device=0, datapath=None):
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.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.matchup_dict = 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.gpu_device = gpu_device
self.variable_averages = None
self.global_step = None
self.writer_train = None
self.writer_valid = 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.model = None
self.optimizer = None
self.train_loss = None
self.train_accuracy = None
self.test_loss = None
self.test_accuracy = None
self.accuracy_0 = None
self.accuracy_1 = None
self.accuracy_2 = None
self.accuracy_3 = None
self.accuracy_4 = None
self.accuracy_5 = None
self.num_0 = 0
self.num_1 = 0
self.num_2 = 0
self.num_3 = 0
self.num_4 = 0
self.num_5 = 0
self.learningRateSchedule = None
self.num_data_samples = None
self.initial_learning_rate = None
n_chans = len(abi_channels)
if TRIPLET:
n_chans *= 3
self.X_img = tf.keras.Input(shape=(img_width, img_width, n_chans))
self.X_prof = tf.keras.Input(shape=(NUM_VERT_LEVELS, NUM_VERT_PARAMS))
self.X_sfc = tf.keras.Input(shape=2)
self.inputs.append(self.X_img)
self.inputs.append(self.X_prof)
self.inputs.append(self.X_sfc)
if datapath is not None:
self.DISK_CACHE = False
f = open(datapath, 'rb')
self.in_mem_data_cache = pickle.load(f)
f.close()
tf.debugging.set_log_device_placement(LOG_DEVICE_PLACEMENT)
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
self.strategy = tf.distribute.MirroredStrategy()
def get_in_mem_data_batch(self, time_keys):
images = []
vprof = []
label = []
sfc = []
for key in time_keys:
if CACHE_DATA_IN_MEM:
tup = self.in_mem_data_cache.get(key)
if tup is not None:
images.append(tup[0])
vprof.append(tup[1])
label.append(tup[2])
sfc.append(tup[3])
continue
obs = self.matchup_dict.get(key)
if obs is None:
print('no entry for: ', key)
timestamp = obs[0][0]
print('not found in cache, processing key: ', key, get_time_tuple_utc(timestamp)[0])
gfs_0, time_0, gfs_1, time_1 = get_bounding_gfs_files(timestamp)
if (gfs_0 is None) and (gfs_1 is None):
print('no GFS for: ', get_time_tuple_utc(timestamp)[0])
continue
try:
gfs_0 = convert_file(gfs_0)
if gfs_1 is not None:
gfs_1 = convert_file(gfs_1)
except Exception as exc:
print(get_time_tuple_utc(timestamp)[0])
print(exc)
continue
ds_1 = None
try:
ds_0 = xr.open_dataset(gfs_0)
if gfs_1 is not None:
ds_1 = xr.open_dataset(gfs_1)
except Exception as exc:
print(exc)
continue
lons = obs[:, 2]
lats = obs[:, 1]
half_width = [abi_half_width.get(ch) for ch in abi_2km_channels]
strides = [abi_stride.get(ch) for ch in abi_2km_channels]
img_a_s, img_a_s_l, img_a_s_r, idxs_a = get_images(lons, lats, timestamp, abi_2km_channels, half_width, strides, do_norm=True, daynight=DAY_NIGHT)
if idxs_a.size == 0:
print('no images for: ', timestamp)
continue
idxs_b = None
if len(abi_hkm_channels) > 0:
half_width = [abi_half_width.get(ch) for ch in abi_hkm_channels]
strides = [abi_stride.get(ch) for ch in abi_hkm_channels]
img_b_s, img_b_s_l, img_b_s_r, idxs_b = get_images(lons, lats, timestamp, abi_hkm_channels, half_width, strides, do_norm=True, daynight=DAY_NIGHT)
if idxs_b.size == 0:
print('no hkm images for: ', timestamp)
continue
if idxs_b is None:
common_idxs = idxs_a
img_a_s = img_a_s[:, common_idxs, :, :]
img_s = img_a_s
if TRIPLET:
img_a_s_l = img_a_s_l[:, common_idxs, :, :]
img_a_s_r = img_a_s_r[:, common_idxs, :, :]
img_s_l = img_a_s_l
img_s_r = img_a_s_r
else:
common_idxs = np.intersect1d(idxs_a, idxs_b)
img_a_s = img_a_s[:, common_idxs, :, :]
img_b_s = img_b_s[:, common_idxs, :, :]
img_s = np.vstack([img_a_s, img_b_s])
# TODO: Triplet support
lons = lons[common_idxs]
lats = lats[common_idxs]
if ds_1 is not None:
ndb = get_interpolated_profile(ds_0, ds_1, time_0, time_1, 'temperature', timestamp, lons, lats, do_norm=True)
else:
ndb = get_profile(ds_0, 'temperature', lons, lats, do_norm=True)
if ndb is None:
continue
if ds_1 is not None:
ndf = get_interpolated_profile(ds_0, ds_1, time_0, time_1, 'rh', timestamp, lons, lats, do_norm=False)
else:
ndf = get_profile(ds_0, 'rh', lons, lats, do_norm=False)
if ndf is None:
continue
ndf /= 100.0
ndb = np.stack((ndb, ndf), axis=2)
#ndd = get_interpolated_scalar(ds_0, ds_1, time_0, time_1, 'MSL pressure', timestamp, lons, lats, do_norm=False)
#ndd /= 1000.0
#nde = get_interpolated_scalar(ds_0, ds_1, time_0, time_1, 'surface temperature', timestamp, lons, lats, do_norm=True)
# label/truth
# Level of best fit (LBF)
ndc = obs[common_idxs, 3]
# AMV Predicted
# ndc = obs[common_idxs, 4]
ndc /= 1000.0
nda = np.transpose(img_s, axes=[1, 2, 3, 0])
if TRIPLET or CONV3D:
nda_l = np.transpose(img_s_l, axes=[1, 2, 3, 0])
nda_r = np.transpose(img_s_r, axes=[1, 2, 3, 0])
if CONV3D:
nda = np.stack((nda_l, nda, nda_r), axis=4)
nda = np.transpose(nda, axes=[0, 1, 2, 4, 3])
else:
nda = np.concatenate([nda, nda_l, nda_r], axis=3)
images.append(nda)
vprof.append(ndb)
label.append(ndc)
# nds = np.stack([ndd, nde], axis=1)
nds = np.zeros((len(lons), 2))
sfc.append(nds)
if not CACHE_GFS:
subprocess.call(['rm', gfs_0, gfs_1])
if CACHE_DATA_IN_MEM:
self.in_mem_data_cache[key] = (nda, ndb, ndc, nds)
ds_0.close()
if ds_1 is not None:
ds_1.close()
images = np.concatenate(images)
label = np.concatenate(label)
label = np.reshape(label, (label.shape[0], 1))
vprof = np.concatenate(vprof)
sfc = np.concatenate(sfc)
return images, vprof, label, sfc
@tf.function(input_signature=[tf.TensorSpec(None, tf.int32)])
def data_function(self, input):
out = tf.numpy_function(self.get_in_mem_data_batch, [input], [tf.float32, tf.float64, tf.float64, tf.float64])
return out
def get_train_dataset(self, time_keys):
time_keys = list(time_keys)
dataset = tf.data.Dataset.from_tensor_slices(time_keys)
dataset = dataset.batch(PROC_BATCH_SIZE)
dataset = dataset.map(self.data_function, num_parallel_calls=8)
dataset = dataset.shuffle(PROC_BATCH_BUFFER_SIZE)
dataset = dataset.prefetch(buffer_size=1)
self.train_dataset = dataset
def get_test_dataset(self, time_keys):
time_keys = list(time_keys)
dataset = tf.data.Dataset.from_tensor_slices(time_keys)
dataset = dataset.batch(PROC_BATCH_SIZE)
dataset = dataset.map(self.data_function, num_parallel_calls=8)
self.test_dataset = dataset
def setup_pipeline(self, matchup_dict, train_dict=None, valid_test_dict=None):
self.matchup_dict = matchup_dict
if train_dict is None:
if valid_test_dict is not None:
self.matchup_dict = valid_test_dict
valid_keys = list(valid_test_dict.keys())
self.get_test_dataset(valid_keys)
self.num_data_samples = get_num_samples(valid_test_dict, valid_keys)
print('num test samples: ', self.num_data_samples)
print('setup_pipeline: Done')
return
train_dict, valid_test_dict = split_matchup(matchup_dict, perc=0.10)
train_dict = shuffle_dict(train_dict)
train_keys = list(train_dict.keys())
self.get_train_dataset(train_keys)
self.num_data_samples = get_num_samples(train_dict, train_keys)
print('num data samples: ', self.num_data_samples)
print('BATCH SIZE: ', BATCH_SIZE)
valid_keys = list(valid_test_dict.keys())
self.get_test_dataset(valid_keys)
print('num test samples: ', get_num_samples(valid_test_dict, valid_keys))
print('setup_pipeline: Done')
def build_1d_cnn(self):
print('build_1d_cnn')
# padding = 'VALID'
padding = 'SAME'
# activation = tf.nn.relu
# activation = tf.nn.elu
activation = tf.nn.leaky_relu
num_filters = 6
conv = tf.keras.layers.Conv1D(num_filters, 5, strides=1, padding=padding)(self.inputs[1])
conv = tf.keras.layers.MaxPool1D(padding=padding)(conv)
print(conv)
num_filters *= 2
conv = tf.keras.layers.Conv1D(num_filters, 3, strides=1, padding=padding)(conv)
conv = tf.keras.layers.MaxPool1D(padding=padding)(conv)
print(conv)
num_filters *= 2
conv = tf.keras.layers.Conv1D(num_filters, 3, strides=1, padding=padding)(conv)
conv = tf.keras.layers.MaxPool1D(padding=padding)(conv)
print(conv)
num_filters *= 2
conv = tf.keras.layers.Conv1D(num_filters, 3, strides=1, padding=padding)(conv)
conv = tf.keras.layers.MaxPool1D(padding=padding)(conv)
print(conv)
flat = tf.keras.layers.Flatten()(conv)
print(flat)
return flat
def build_cnn(self):
print('build_cnn')
# padding = "VALID"
padding = "SAME"
# activation = tf.nn.relu
# activation = tf.nn.elu
activation = tf.nn.leaky_relu
momentum = 0.99
num_filters = 8
conv = tf.keras.layers.Conv2D(num_filters, 5, strides=[1, 1], padding=padding, activation=activation)(self.inputs[0])
conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
conv = tf.keras.layers.BatchNormalization()(conv)
print(conv.shape)
num_filters *= 2
conv = tf.keras.layers.Conv2D(num_filters, 3, strides=[1, 1], padding=padding, activation=activation)(conv)
conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
conv = tf.keras.layers.BatchNormalization()(conv)
print(conv.shape)
num_filters *= 2
conv = tf.keras.layers.Conv2D(num_filters, 3, strides=[1, 1], padding=padding, activation=activation)(conv)
conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
conv = tf.keras.layers.BatchNormalization()(conv)
print(conv.shape)
num_filters *= 2
conv = tf.keras.layers.Conv2D(num_filters, 3, strides=[1, 1], padding=padding, activation=activation)(conv)
conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
conv = tf.keras.layers.BatchNormalization()(conv)
print(conv.shape)
num_filters *= 2
conv = tf.keras.layers.Conv2D(num_filters, 3, strides=[1, 1], padding=padding, activation=activation)(conv)
conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
conv = tf.keras.layers.BatchNormalization()(conv)
print(conv.shape)
flat = tf.keras.layers.Flatten()(conv)
return flat
def build_anc_dnn(self):
print('build_anc_dnn')
drop_rate = 0.5
# activation = tf.nn.relu
# activation = tf.nn.elu
activation = tf.nn.leaky_relu
momentum = 0.99
n_hidden = self.X_sfc.shape[1]
with tf.name_scope("Residual_Block_6"):
fc = tf.keras.layers.Dropout(drop_rate)(self.inputs[2])
fc = tf.keras.layers.Dense(4*n_hidden, activation=activation)(fc)
fc = tf.keras.layers.BatchNormalization()(fc)
print(fc.shape)
fc_skip = fc
fc = tf.keras.layers.Dropout(drop_rate)(fc)
fc = tf.keras.layers.Dense(4*n_hidden, activation=activation)(fc)
fc = tf.keras.layers.BatchNormalization()(fc)
print(fc.shape)
fc = tf.keras.layers.Dropout(drop_rate)(fc)
fc = tf.keras.layers.Dense(4*n_hidden, activation=None)(fc)
fc = tf.keras.layers.BatchNormalization()(fc)
fc = fc + fc_skip
fc = tf.keras.layers.LeakyReLU()(fc)
print(fc.shape)
return fc
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.X_img
n_hidden = self.X_img.shape[1]
fac = 1
fc = build_residual_block(flat, drop_rate, fac*n_hidden, activation, 'Residual_Block_1')
fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_2')
fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_3')
fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_4')
fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_5')
fc = tf.keras.layers.Dense(n_hidden, activation=activation)(fc)
fc = tf.keras.layers.BatchNormalization()(fc)
print(fc.shape)
logits = tf.keras.layers.Dense(NumLabels)(fc)
print(logits.shape)
self.logits = logits
def build_training(self):
self.loss = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE)
# decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
initial_learning_rate = 0.0016
decay_rate = 0.95
steps_per_epoch = int(self.num_data_samples/BATCH_SIZE) # one epoch
# decay_steps = int(steps_per_epoch / 2)
decay_steps = 2 * 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)
if TRACK_MOVING_AVERAGE:
ema = tf.train.ExponentialMovingAverage(decay=0.999)
with tf.control_dependencies([optimizer]):
optimizer = ema.apply(self.model.trainable_variables)
self.optimizer = optimizer
self.initial_learning_rate = initial_learning_rate
def build_evaluation(self):
self.train_accuracy = tf.keras.metrics.MeanAbsoluteError(name='train_accuracy')
self.test_accuracy = tf.keras.metrics.MeanAbsoluteError(name='test_accuracy')
self.train_loss = tf.keras.metrics.Mean(name='train_loss')
self.test_loss = tf.keras.metrics.Mean(name='test_loss')
self.accuracy_0 = tf.keras.metrics.MeanAbsoluteError(name='acc_0')
self.accuracy_1 = tf.keras.metrics.MeanAbsoluteError(name='acc_1')
self.accuracy_2 = tf.keras.metrics.MeanAbsoluteError(name='acc_2')
self.accuracy_3 = tf.keras.metrics.MeanAbsoluteError(name='acc_3')
self.accuracy_4 = tf.keras.metrics.MeanAbsoluteError(name='acc_4')
self.accuracy_5 = tf.keras.metrics.MeanAbsoluteError(name='acc_5')
def build_predict(self):
_, pred = tf.nn.top_k(self.logits)
self.pred_class = pred
if TRACK_MOVING_AVERAGE:
self.variable_averages = tf.train.ExponentialMovingAverage(0.999, self.global_step)
self.variable_averages.apply(self.model.trainable_variables)
@tf.function
def train_step(self, mini_batch):
inputs = [mini_batch[0], mini_batch[1], mini_batch[3]]
labels = mini_batch[2]
with tf.GradientTape() as tape:
pred = self.model(inputs, training=True)
loss = self.loss(labels, pred)
loss = tf.nn.compute_average_loss(loss, global_batch_size=BATCH_SIZE)
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], mini_batch[1], mini_batch[3]]
labels = mini_batch[2]
pred = self.model(inputs, training=False)
t_loss = self.loss(labels, pred)
self.test_loss(t_loss)
self.test_accuracy(labels, pred)
return t_loss
@tf.function
def distributed_train_step(self, dataset_inputs):
per_replica_losses = self.strategy.run(self.train_step, args=(dataset_inputs,))
return self.strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None)
@tf.function
def distributed_test_step(self, dataset_inputs):
return self.strategy.run(self.test_step, args=(dataset_inputs,))
def predict(self, mini_batch):
inputs = [mini_batch[0], mini_batch[1], mini_batch[3]]
labels = mini_batch[2]
pred = self.model(inputs, training=False)
t_loss = self.loss(labels, pred)
self.test_loss(t_loss)
self.test_accuracy(labels, pred)
m = np.logical_and(labels >= 0.01, labels < 0.2)
self.num_0 += np.sum(m)
self.accuracy_0(labels[m], pred[m])
m = np.logical_and(labels >= 0.2, labels < 0.4)
self.num_1 += np.sum(m)
self.accuracy_1(labels[m], pred[m])
m = np.logical_and(labels >= 0.4, labels < 0.6)
self.num_2 += np.sum(m)
self.accuracy_2(labels[m], pred[m])
m = np.logical_and(labels >= 0.6, labels < 0.8)
self.num_3 += np.sum(m)
self.accuracy_3(labels[m], pred[m])
m = np.logical_and(labels >= 0.8, labels < 1.15)
self.num_4 += np.sum(m)
self.accuracy_4(labels[m], pred[m])
m = np.logical_and(labels >= 0.01, labels < 0.5)
self.num_5 += np.sum(m)
self.accuracy_5(labels[m], pred[m])
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)
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'))
step = 0
total_time = 0
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 abi, temp, lbfp, sfc in self.train_dataset:
trn_ds = tf.data.Dataset.from_tensor_slices((abi, temp, lbfp, sfc))
trn_ds = trn_ds.batch(BATCH_SIZE)
trn_dist_ds = self.strategy.experimental_distribute_dataset(trn_ds)
for mini_batch in trn_dist_ds:
if self.learningRateSchedule is not None:
loss = self.distributed_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('num_train_steps', step, step=step)
tf.summary.scalar('num_epochs', epoch, step=step)
self.test_loss.reset_states()
self.test_accuracy.reset_states()
for abi_tst, temp_tst, lbfp_tst, sfc_tst in self.test_dataset:
tst_ds = tf.data.Dataset.from_tensor_slices((abi_tst, temp_tst, lbfp_tst, sfc_tst))
tst_ds = tst_ds.batch(BATCH_SIZE)
tst_dist_ds = self.strategy.experimental_distribute_dataset(tst_ds)
for mini_batch_test in tst_dist_ds:
self.distributed_test_step(mini_batch_test)
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)
tf.summary.scalar('num_train_steps', step, step=step)
tf.summary.scalar('num_epochs', epoch, step=step)
print('****** test loss, acc: ', self.test_loss.result(), self.test_accuracy.result())
step += 1
print('train loss: ', loss.numpy())
proc_batch_cnt += 1
n_samples += abi.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.test_loss.reset_states()
self.test_accuracy.reset_states()
for abi, temp, lbfp, sfc in self.test_dataset:
ds = tf.data.Dataset.from_tensor_slices((abi, temp, lbfp, sfc))
ds = ds.batch(BATCH_SIZE)
tst_dist_ds = self.strategy.experimental_distribute_dataset(ds)
for mini_batch_test in tst_dist_ds:
self.distributed_test_step(mini_batch_test)
print('loss, acc: ', self.test_loss.result(), self.test_accuracy.result())
ckpt_manager.save()
if self.DISK_CACHE and epoch == 0:
f = open(cachepath, 'wb')
pickle.dump(self.in_mem_data_cache, f)
f.close()
print('total time: ', total_time)
self.writer_train.close()
self.writer_valid.close()
def build_model(self):
flat = self.build_cnn()
flat_1d = self.build_1d_cnn()
# flat_anc = self.build_anc_dnn()
# flat = tf.keras.layers.concatenate([flat, flat_1d, flat_anc])
flat = tf.keras.layers.concatenate([flat, flat_1d])
self.build_dnn(flat)
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.test_loss.reset_states()
self.test_accuracy.reset_states()
for abi_tst, temp_tst, lbfp_tst, sfc_tst in self.test_dataset:
ds = tf.data.Dataset.from_tensor_slices((abi_tst, temp_tst, lbfp_tst, sfc_tst))
ds = ds.batch(BATCH_SIZE)
for mini_batch_test in ds:
self.predict(mini_batch_test)
print('loss, acc: ', self.test_loss.result(), self.test_accuracy.result())
print('acc_0', self.num_0, self.accuracy_0.result())
print('acc_1', self.num_1, self.accuracy_1.result())
print('acc_2', self.num_2, self.accuracy_2.result())
print('acc_3', self.num_3, self.accuracy_3.result())
print('acc_4', self.num_4, self.accuracy_4.result())
print('acc_5', self.num_5, self.accuracy_5.result())
def run(self, matchup_dict, train_dict=None, valid_dict=None):
with self.strategy.scope():
self.setup_pipeline(matchup_dict, train_dict=train_dict, valid_test_dict=valid_dict)
self.build_model()
self.build_training()
self.build_evaluation()
self.do_training()
def run_restore(self, matchup_dict, ckpt_dir):
self.setup_pipeline(None, None, matchup_dict)
self.build_model()
self.build_training()
self.build_evaluation()
self.restore(ckpt_dir)
if __name__ == "__main__":
nn = CloudHeightNN()
nn.run('matchup_filename')