from neural_network import neural_network
import numpy as np
import tensorflow as tf
from neural_network.network_structure.Logger import Logger
import os
network = neural_network.neural_network()
logger = Logger(network.path)
train_data, train_labels, test_data, test_labels = network.data()
EPOCHS = 100
BATCH_SIZE = 32
def log_write(data):
with open(os.path.join(network.path, 'log.txt'), 'a') as file:
file.write(data + '\n')
print(data)
def train():
sess = tf.Session()
network.create_model()
network.session_init(sess)
for epoch in range(EPOCHS):
log_write('##########%4d##########' % (epoch))
train_loss, train_accuracy = train_one_epoch(train_data, train_labels,
epoch)
test_accuracy = test_one_epoch(test_data, test_labels)
logger.log_scalar(tag='Loss per Epoch', value=train_loss, step=epoch)
logger.log_scalar(tag='Train Accuracy per Epoch',
import tensorflow as tf
import os
import numpy as np
from neural_network.neural_network import neural_network
from neural_network.network_structure.Logger import Logger
from IPython import embed
import argparse
import sys
parser = argparse.ArgumentParser()
parser.add_argument('--mode',type=str,default='train',help='Mode of operation')
parser.add_argument('--weights',type=str,default='1.ckpt',help='Path of weights')
args = parser.parse_args()
# Create a Neural Network Class.
nn = neural_network()
# Save network structure in logs.
nn.save_network_structure()
L1,L2 = 1,1
def end_effector_pose(t1,t2):
return [L1*np.cos(t1)+L2*np.cos(t1+t2),L1*np.sin(t1)+L2*np.sin(t1+t2)]
# Create logger file for tensorboard.
# Get the path from neural network class.
logger = Logger(nn.path)
episodes = 5000
batch_size = 100
samples = 10000
