diff --git a/modules/deeplearning/icing.py b/modules/deeplearning/icing.py
new file mode 100644
index 0000000000000000000000000000000000000000..98562ab7b4531acea81cc53e7ddc903d4b61fa1f
--- /dev/null
+++ b/modules/deeplearning/icing.py
@@ -0,0 +1,778 @@
+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
+
+PROC_BATCH_SIZE = 60
+PROC_BATCH_BUFFER_SIZE = 50000
+NumLabels = 1
+BATCH_SIZE = 256
+NUM_EPOCHS = 200
+
+
+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 IcingIntensityNN:
+
+ 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.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)
+
+ self.DISK_CACHE = True
+
+ 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)
+
+ 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_dnn(self, input_layer=None):
+ print('build fully connected layer')
+ drop_rate = 0.5
+
+ # activation = tf.nn.softmax
+ activation = tf.nn.sigmoid # For binary
+ 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.BinaryCrossentropy # for two-class only
+ #self.loss = tf.keras.losses.SparseCategoricalCrossentropy() # For multi-class
+
+ # 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')
+
+ 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)
+ 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)
+
+ 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)
+
+ 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)
+ 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('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)
+ for mini_batch_test in tst_ds:
+ self.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)
+ for mini_batch in ds:
+ self.test_step(mini_batch)
+
+ 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 = 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())
+
+ def run(self, matchup_dict, train_dict=None, valid_dict=None):
+ with tf.device('/device:GPU:'+str(self.gpu_device)):
+ 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 = IcingIntensityNN()
+ nn.run('matchup_filename')