import torch
from neural_net import NeuralNet
from load_policy import *
import gym
import torch.nn as nn
import torch.optim as optim
network = NeuralNet(4,128,64,2)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(network.parameters(),0.01,0.9)
losses = []
iter = []
for i in range(20):
state,action = get_batch()
for j in range(state.size()[0]):
network.zero_grad()
output = network(state[j])
loss = criterion(output,action[j])
if i%10==0:
losses.append(loss)
iter.append(i)
loss.backward()
optimizer.step()
print(loss)
torch.save(network.state_dict(),"mymodel.pt")
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from neural_net import NeuralNet
number_of_epochs = 100
# CUDA for PyTorch
use_cuda = torch.cuda.is_available() #checking if gpu available
print(torch.cuda.get_device_name())
device = torch.device("cuda" if use_cuda else "cpu") #if no gpu then use cpu
neural_net = NeuralNet()
neural_net.train()
optimizer = optim.SGD(neural_net.parameters(), lr=0.01, momentum=0.5)
loss_function = nn.MSELoss()
fp = open("dataset.txt", "r")
for i in range(number_of_epochs):
for lines in fp:
data_per_episode = ast.literal_eval(lines.strip())
print("total number of steps {}".format(len(data_per_episode)))
for steps in data_per_episode:
data = steps[0]
data.append(steps[1])
gt_action = np.asarray(steps[2])
data = np.asarray(data)
optimizer.zero_grad()
assert image_size is not None, 'Image size can not be None'
assert number_classes is not None, 'Total number of classes can not be None'
num_input_channels = image_size[0]
print('> Constructing the network')
# construct the network
cnn = NeuralNet(num_conv_layers, num_full_layers, list_param_conv_layers,
list_param_full_layers, dropout_rate, activation,
image_size[1], number_classes, num_input_channels)
cnn.to(device)
try:
if optimizer_choice == 1:
optimizer = optim.SGD(cnn.parameters(),
lr=arg1,
momentum=arg2,
weight_decay=arg3,
dampening=arg4)
if optimizer_choice == 2:
optimizer = optim.Adam(cnn.parameters(),
lr=arg1,
betas=(arg2, arg3),
weight_decay=arg4)
if optimizer_choice == 3:
optimizer = optim.Adagrad(cnn.parameters(),
lr=arg1,
lr_decay=arg2,
weight_decay=arg4,
initial_accumulator_value=arg3)
