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Conv_net_train.py
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# libraries
import numpy as np
# classes
from DLD_Utils import DLD_Utils as utl
from Conv_Base import DLD_Net
# hyperparameters
test_frac = 0.2
epoch = 100
N_EPOCH = 5
batch_size = 64
lr = 0.0002
summary = False
train = False
dev = False
test = False
### loading data ###
dataset_name = "dataset2288"
dataset = utl.load_data(dataset_name)
### Normilizing data and saving the Normilized value ###
maxu = np.max(np.abs(dataset[0]), axis=(1, 2), keepdims=True)
maxv = np.max(np.abs(dataset[1]), axis=(1, 2), keepdims=True)
# Local MAX
max_vel = np.max((maxu, maxv), axis=0)
max_label = np.max(dataset[2], axis=0)
MAX = []
MAX.append(max_vel)
MAX.append(max_label)
dataset_norm = []
dataset_norm.append(dataset[0]/ MAX[0])
dataset_norm.append(dataset[1]/ MAX[0])
dataset_norm.append(dataset[2]/ MAX[1])
### Spiliting data to train test sections ###
train_ix = np.random.choice(len(dataset_norm[0]), size=int(
(1-test_frac)*len(dataset_norm[0])), replace=False)
test_ix = np.setdiff1d(np.arange(len(dataset_norm[0])), train_ix)
u_train, v_train, label_train = np.nan_to_num(
dataset_norm[0][train_ix]),np.nan_to_num(
dataset_norm[1][train_ix]), np.nan_to_num(
dataset_norm[2][train_ix])
u_test, v_test, label_test = np.nan_to_num(
dataset_norm[0][test_ix]),np.nan_to_num(
dataset_norm[1][test_ix]), np.nan_to_num(
dataset_norm[2][test_ix])
### load network class ###
NN = DLD_Net()
NN.analyse_data(dataset[0], dataset_norm[0], 3)
label_shape = label_train[0].shape
NN.create_model(label_shape, summary)
if train:
NN.train(u_train, v_train, label_train, u_test, v_test, label_test, epoch, N_EPOCH, batch_size, lr)
else:
NN.DLDNN.load_weights(NN.checkpoint_filepath)
if dev:
NN.DLDNN.load_weights(NN.checkpoint_filepath)
dataset_norm_test = []
dataset_norm_test.append(dataset_norm[0][test_ix])
dataset_norm_test.append(dataset_norm[1][test_ix])
dataset_norm_test.append(dataset_norm[2][test_ix])
eval_data = NN.network_evaluation(1, dataset_norm_test, MAX)
import csv
with open('eval_data.csv', 'w') as file:
writer = csv.writer(file)
writer.writerows(eval_data)
# label_number = 2227
# f, _, _ = NN.dataset[2][label_number]
# dp = 0.1
# periods = 1
# start_point = (0, f/2+dp*(1-f)/2)
# NN.strmline_comparison(label_number, dp, periods, start_point)
# f = np.round(np.linspace(0.25, 0.75, 10), 2).tolist()
# N = [3.5, 4.5, 5.5, 6.5]
# RE = [0.05, 3, 6, 8, 12, 18]
if test:
# loading data
dataset_name = "dataset_test_int"
dataset = utl.load_data(dataset_name)
# Normilizing data and saving the Normilized value
maxu = np.max(np.abs(dataset[0]), axis=(1, 2), keepdims=True)
maxv = np.max(np.abs(dataset[1]), axis=(1, 2), keepdims=True)
# Local MAX
max_vel = np.max((maxu, maxv), axis=0)
# use maximum from the training data
# max_label = np.max(dataset[2], axis=0)
MAX = []
MAX.append(max_vel)
MAX.append(max_label)
dataset_norm = []
dataset_norm.append(dataset[0]/ MAX[0])
dataset_norm.append(dataset[1]/ MAX[0])
dataset_norm.append(dataset[2]/ MAX[1])
NN.analyse_data(dataset[0], dataset_norm[0], 3)
eval_data = NN.network_evaluation(1, dataset_norm, MAX)
import csv
with open('eval_data_test_int.csv', 'w') as file:
writer = csv.writer(file)
writer.writerows(eval_data)
# label_number = 20
# f, _, _ = dataset[2][label_number]
# dp = 0.2
# periods = 1
# start_point = (0, f/2+dp*(1-f)/2)
# NN.strmline_comparison(dataset_norm, MAX, label_number, dp, periods, start_point)