num_outputs = 18
other_param_dict = {'num_epochs' : 50,
'batch_size' : 200, 'normalize' :1 ,
'dropout' : 0, 'fp_depth': 4, 'activation' :relu,
#'fp_type' : 'morgan',
'fp_type' : 'neural',
'save_weights' : False,
'h1_size' : 100, 'conv_width': 20, 'num_outputs': num_outputs,
'init_bias': 0.85}
#learn rate, init scale,
# Neural 1, 100 runs, norm
# Parameters are : learn rate, init scale, and fingerpritn length
RE = rxn_estimator( -3.7708613280344383, -3.5680734313359697, 142, other_param_dict)
train_input_1 = pkl.load(open("../../data/200each/balanced_200each_train_inputs_1.dat"))
train_targets = pkl.load(open("../../data/200each/balanced_200each_train_targets.dat"))
print 'beginning training'
start = time.time()
RE.fit(train_input_1, train_targets)
print 'end of training'
print 'run time: ', time.time() - start
batch_nm = 'exam_200each'
method = other_param_dict['fp_type']+'1'
'h1_size': 100,
'conv_width': 20,
'num_outputs': num_outputs,
'init_bias': 0.85
}
#learn rate, init scale,
# Morgan 1:
# Change to correct value (I deleted unfortunately...)
#RE = rxn_estimator(-2.9980333058185202, -4.92986261217202, 153, other_param_dict)
# Morgan 2:
#RE = rxn_estimator(-3.7771145904698447, -4.3688416048512435, 752, other_param_dict)
# Morgan 3:
RE = rxn_estimator(-3.9815288594632183, -3.5896897128869263, 767,
other_param_dict)
# Neural 1:
#RE = rxn_estimator( -3.2721919214871984, -2.3649628009746966, 148, other_param_dict)
#Neural 2:
#RE = rxn_estimator(-3.6682766762789663, -3.1635758381500736, 156, other_param_dict)
train_input_1 = pkl.load(
open(
"/home/jennifer/Documents/DeepMolecules-master/reaction_learn/Classification_3_1/data/200each/balanced_200each_train_inputs_1.dat"
))
train_targets = pkl.load(
open(
"/home/jennifer/Documents/DeepMolecules-master/reaction_learn/Classification_3_1/data/200each/balanced_200each_train_targets.dat"
))
def hyperopt_train_test(params):
clf = rxn_estimator(np.float32(params[0]), np.float32(params[1]),
np.int(params[2]), other_param_dict)
return cross_val_score(clf, X, y, cv=3).mean()
'batch_size': 100,
'normalize': 1,
'dropout': 0,
'fp_depth': 4,
'activation': relu,
'fp_type': 'morgan',
#'fp_type' : 'neural',
'save_weights': False,
'h1_size': 100,
'conv_width': 20,
'num_outputs': num_outputs,
'init_bias': 0.85
}
# Parameters are : learn rate, init scale, and fingerpritn length
RE = rxn_estimator(-3.418366537208893, -3.435202814263662, 563,
other_param_dict)
train_input_1 = pkl.load(
open("../../data/200each/balanced_200each_train_inputs_1.dat"))
train_targets = pkl.load(
open("../../data/200each/balanced_200each_train_targets.dat"))
print 'beginning training'
start = time.time()
RE.fit(train_input_1, train_targets)
print 'end of training'
print 'run time: ', time.time() - start
print 'loading weights, begin prediction'