def main(args):
best_data = 1.0
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = 0.9)
(feature, target) = load_txt(args.file_path)
(test_fe, test_tg), (verif_fe, verif_tg), (train_fe, train_tg), test_index,train_index,verif_index = train_test_split(
feature = feature,
target = target
)
net = network(
axis = train_fe.shape[1],
lr = args.lr
)
saver = tf.train.Saver(max_to_keep = 1)
np.savetxt(str(number)+"_test_index.csv", test_index)
np.savetxt(str(number)+"_train_index.csv", train_index, delimiter = ",")
np.savetxt(str(number)+"_verif_index.csv", verif_index, delimiter = ",")
global sess_global
with tf.Session(
config=tf.ConfigProto(gpu_options = gpu_options)
) as sess:
print("session assigned..........")
saver.restore(sess, "check_point/model.ckpt")
for epoch in range(args.epochs):
train_loss = train(
sess = sess,
model = net,
feature = train_fe,
target = train_tg,
batch_size = args.batch_size
)
verif_loss = verif(
sess = sess,
model = net,
feature = verif_fe,
target = verif_tg,
batch_size = args.batch_size
)
print(
"epoch {}, train_r2={:.4f}, train_mae={:.4f}, train_rmse={:.4f}, verif_r2={:.4f}, verif_mae={:.4f}, verif_rmse={:.4f}"
.format(epoch, train_loss[1], train_loss[2], train_loss[3], verif_loss[1], verif_loss[2], verif_loss[3])
)
if verif_loss[2] < best_data:
best_data = verif_loss[2]
print("*"*10 + " save model " + "*"*10)
saver.save(sess, "check_point/model.ckpt")
def main(args):
best_data = 1.0
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = 0.9)
(feature, target) = load_txt(args.file_path)
(verif_fe, verif_tg), (train_fe, train_tg) = train_test_split(
feature = feature,
target = target,
percent = args.percent
)
net = network(
axis = train_fe.shape[1],
lr = args.lr
)
saver = tf.train.Saver(max_to_keep = 1)
with tf.compat.v1.Session(
config=tf.ConfigProto(gpu_options = gpu_options)
) as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(args.epochs):
train_loss = train(
sess = sess,
model = net,
feature = train_fe,
target = train_tg,
batch_size = args.batch_size
)
verif_loss = verif(
sess = sess,
model = net,
feature = verif_fe,
target = verif_tg,
batch_size = args.batch_size
)
print(
"epoch {}, train_r2={:.4f}, train_mae={:.4f}, train_rmse={:.4f}, verif_r2={:.4f}, verif_mae={:.4f}, verif_rmse={:.4f}"
.format(epoch, train_loss[1], train_loss[2], train_loss[3], verif_loss[1], verif_loss[2], verif_loss[3])
)
if verif_loss[2] < best_data:
best_data = verif_loss[2]
print("*"*10 + " save model " + "*"*10)
saver.save(sess, "check_point/model.ckpt")
warnings.warn = warn
number = 508
target_index = np.loadtxt(str(number) + "_test_index.csv").astype(np.float32)
feature_number = 132
data = np.loadtxt(str(number) + "_magpie_target.csv", dtype=str, delimiter=",")
target = data[:, 134:].astype(np.float32)
target_spectrum = target[int(target_index)]
elements = np.array(re.findall(r"\D+\D*", data[0, 0]))
elements = elements[elements != '.']
print(elements)
element_count = len(elements)
net = network(axis=feature_number, lr=1e-3)
saver = tf.train.Saver(max_to_keep=1)
def magpie_feature(formula):
data = [formula]
df = pd.DataFrame(data, columns=["formula"])
df["composition"] = df["formula"].transform(str_to_composition)
ep_feat = ElementProperty.from_preset(preset_name="magpie")
df = ep_feat.featurize_dataframe(df, col_id="composition")
df.drop(labels=["composition"], axis=1, inplace=True)
return df.iloc[0, 1:].to_numpy()
def target(x1, x2):
formula = ''
gene_length = 7
element_count = len(elements)
feature_number = 132
creator.create("FitnessMax", base.Fitness, weights=(1.0, ))
creator.create("Individual", numpy.ndarray, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register("attr_bool", random.randint, 0, 1)
toolbox.register("individual",
tools.initRepeat,
creator.Individual,
toolbox.attr_bool,
n=gene_length * element_count)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
net = network(axis=132, lr=1e-3)
saver = tf.train.Saver(max_to_keep=1)
def magpie_feature(formula):
data = [formula]
df = pd.DataFrame(data, columns=["formula"])
df["composition"] = df["formula"].transform(str_to_composition)
ep_feat = ElementProperty.from_preset(preset_name="magpie")
df = ep_feat.featurize_dataframe(df, col_id="composition")
df.drop(labels=["composition"], axis=1, inplace=True)
return df.iloc[0, 1:].to_numpy()
def evalresult(individual):
formula = ''
def train(args, test_image, test=False):
log_file = None
if not test:
log_file = open(args.log_file, 'w')
minibatchs_X, minibatchs_Y = make_minibatches(*load_data(args.datafile), args.minibatch_size)
print("Data loaded")
log_file.write("Data loaded\n")
tf.reset_default_graph()
X = tf.placeholder(tf.float32, shape=[None, 160, 320, 3], name='X')
Y = tf.placeholder(tf.float32, shape=[None, 4], name='Y')
pred = network(X)
with tf.name_scope('train'):
loss = tf.reduce_mean(tf.pow(Y - pred, 2))
tf.summary.scalar('loss', loss)
train_op = tf.train.AdamOptimizer().minimize(loss)
summary = tf.summary.merge_all()
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
if test:
restore_path = tf.train.latest_checkpoint('model')
saver.restore(sess, restore_path)
print('restored the model from' + str(restore_path))
pred_action = sess.run(pred, feed_dict={X: test_image})
return pred_action
if args.restore:
restore_path = tf.train.latest_checkpoint(args.save_dir)
saver.restore(sess, restore_path)
print('restored the model from' + str(restore_path))
log_file.write('restored the model from' + str(restore_path) + "\n")
else:
sess.run(init)
summary_writer = tf.summary.FileWriter(args.summary_dir, sess.graph)
train_start = time.time()
print("Training started")
log_file.write("Training started\n")
for epoch in range(args.num_epochs):
epoch_start = time.time()
num_minibatches = len(minibatchs_X)
losses = []
for minibatch in range(num_minibatches):
_loss, _, _summ = sess.run([loss, train_op, summary],
feed_dict={X: minibatchs_X[minibatch], Y: minibatchs_Y[minibatch]})
losses.append(_loss)
print("Epoch " + str(epoch + 1) + " - Minibatch " + str(minibatch + 1) +
" completed with loss = " + str(_loss))
log_file.write("Epoch " + str(epoch + 1) + " - Minibatch " + str(minibatch + 1) +
" completed with loss = " + str(_loss) + "\n")
summary_writer.add_summary(_summ)
print("EPOCH " + str(epoch + 1) +
" completed in " + str(time.time() - epoch_start)[:5] +
" secs with average loss = " + str(sum(losses)/len(losses)))
log_file.write("EPOCH " + str(epoch + 1) +
" completed in " + str(time.time() - epoch_start)[:5] +
" secs with average loss = " + str(sum(losses)/len(losses)) + "\n")
save_path = saver.save(sess, args.save_dir + 'model.ckpt')
print("Model saved in the dir " + str(save_path))
log_file.write("Model saved in the dir " + str(save_path) + "\n")
print("Training Finished in " + str(time.time() - train_start)[:5])
log_file.write("Training Finished in " + str(time.time() - train_start)[:5] + "\n")
log_file.close()