def __init__(self, config):
self.socket = {}
self.rs = RawServer(Event(), 100, 1000)
self.nh = NetworkHandler(self)
self.shutdown = Event()
self.config = config
self.post_commit = []
for pattern, action in self.config.items('post-commit'):
try:
self.post_commit.append((re.compile(pattern, re.I), action))
except re.error, msg:
raise ServerError, 'Bad post-commit pattern \"%s\": %s' % \
(pattern, msg)
from network import NetworkHandler
from fingerprints import fingerprints
import numpy as np
import matplotlib.pyplot as plt
import pickle
train_params = {"learning_rate": 0.001, "optimizer": "rmsprop"}
fp = fingerprints(lmax=4, nmax=5, r_c=4.0)
N = NetworkHandler(fp,
nodes=[10, 10],
nNetworks=1,
train_params=train_params,
batch_size=5,
nEpochs=30,
activation="sigmoid",
name="debug")
# y = y[:,0]
#
f = open("./FP_data/Hcell2.0.pckl", 'rb')
dict = pickle.load(f)
f.close()
fps = dict["fingerprints"]
den = dict["density"]
print(len(den))
f = open("./Cell_data/Hcell2.0.pckl", 'rb')
dict = pickle.load(f)
f.close()
grid = dict["grid"]
idx = np.where(grid[1, 1] == grid[:, 1])[0]
idx = np.where(grid[1, 2] == grid[idx, 2])[0]
import tensorflow as tf
from network import NetworkHandler
from fingerprints import fingerprints
from castep_density import Castep_density
import matplotlib.pyplot as plt
import numpy as np
fp = fingerprints(lmax=4, nmax=5, r_c=4.0)
N = NetworkHandler(fp, nEpochs=50, train_dir='../CastepCalculations/practice/')
N.get_data()
N.load()
C = Castep_density(fp, N)
C.setupNetwork()
C.get_cell_data('Hcell1.41.pckl')
C.setCellDensities()
#N.load()
#for i in range(N.nNetworks):
# rmse = N.get_rmse(i,N.X_test,N.y_test)
# print(rmse)
def __init__(self, co):
self.co = co
self.socket = {}
self.rs = RawServer(Event(), 100, 1000)
self.nh = NetworkHandler(self)
