Skip to content
Snippets Groups Projects
Commit 72f332d5 authored by tomrink's avatar tomrink
Browse files

snapshot...

parent ecc3efad
Branches
No related tags found
No related merge requests found
......@@ -4,14 +4,12 @@ import subprocess
import os, datetime
import numpy as np
import xarray as xr
import pickle
import h5py
from deeplearning.amv_raob import get_bounding_gfs_files, convert_file, get_images, get_interpolated_profile, get_time_tuple_utc, get_profile
from icing.pirep_goes import split_data
from icing.pirep_goes import train_params_day
from icing.pirep_goes import split_data, normalize
LOG_DEVICE_PLACEMENT = False
......@@ -49,8 +47,10 @@ img_width = 16
#img_width = 12
#img_width = 6
NUM_VERT_LEVELS = 26
NUM_VERT_PARAMS = 2
train_params_day = ['cld_height_acha', 'cld_geo_thick', 'supercooled_cloud_fraction', 'cld_temp_acha', 'cld_press_acha',
'cld_reff_dcomp', 'cld_opd_dcomp', 'cld_cwp_dcomp', 'iwc_dcomp', 'lwc_dcomp']
#'cloud_phase']
def build_residual_block(input, drop_rate, num_neurons, activation, block_name, doDropout=True, doBatchNorm=True):
......@@ -116,6 +116,7 @@ class IcingIntensityNN:
self.in_mem_batch = None
self.filename = None
self.h5f = None
self.h5f_l1b = None
self.logits = None
......@@ -164,7 +165,7 @@ class IcingIntensityNN:
n_chans *= 3
self.X_img = tf.keras.Input(shape=(img_width, img_width, n_chans))
#self.X_img = tf.keras.Input(shape=NUM_PARAMS)
self.X_prof = tf.keras.Input(shape=(NUM_VERT_LEVELS, NUM_VERT_PARAMS))
#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)
......@@ -201,7 +202,7 @@ class IcingIntensityNN:
data = []
for param in train_params_day:
nda = self.h5f[param][nd_keys, ]
# nda = do_normalize(nda)
# nda = normalize(nda, param)
data.append(nda)
data = np.stack(data)
data = data.astype(np.float32)
......@@ -224,7 +225,6 @@ class IcingIntensityNN:
# label.append(tup[2])
# continue
#
#
# if CACHE_DATA_IN_MEM:
# self.in_mem_data_cache[key] = (nda, ndb, ndc)
......@@ -576,7 +576,7 @@ class IcingIntensityNN:
self.predict(mini_batch_test)
print('loss, acc: ', self.test_loss.result(), self.test_accuracy.result())
def run(self, filename, train_dict=None, valid_dict=None):
def run(self, filename, filename_l1b=None, train_dict=None, valid_dict=None):
with tf.device('/device:GPU:'+str(self.gpu_device)):
self.setup_pipeline(filename, train_idxs=train_dict, test_idxs=valid_dict)
self.build_model()
......
0% Loading or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment