main.py

import numpy as np
import tensorflow as tf
import nengo
import nengo_dl

from config import get_config
#from data_loader import get_loader
from utils import prepare_dirs_and_logger
from models import GeneratorCNN, GeneratorRCNN, DiscriminatorCNN, get_conv_shape2

def main(config):
	prepare_dirs_and_logger(config)
	batch_size = config.batch_size
	z_num = config.z_num
	hidden_num = config.conv_hidden_num
	repeat_num = int(np.log2(config.input_scale_size)) - 2
	#data_loader=get_loader()
	data_format = config.data_format
	channel = 3 #get_conv_shape2(data_loader, data_format)
	neuron_type=nengo.LIF(tau_rc=0.02, tau_ref=0.002)#LIF neuron
		
		
		
		
    #do the rest of the importing... look at trainer.py in the __init__ and the code below for which vars

	z = np.random.uniform(size=(batch_size, z_num), low=-1.0, high=1.0)

	neuron_type = nengo.LIF(tau_rc=0.02, tau_ref=0.002)#, sigma=0.002)
	ens_params = dict(max_rates=nengo.dists.Choice([100]), intercepts=nengo.dists.Choice([0]))

	with nengo.Network() as net:
		inpt = nengo.Node(nengo.processes.PresentInput(z, 0.1))
		G, G_out = GeneratorCNN(inpt, hidden_num, channel, repeat_num, reuse=False, data_format=data_format, neuron_type=neuron_type, ens_params = ens_params)
		G_r, G_r_out = GeneratorRCNN(G_out, channel, z_num, repeat_num, hidden_num, data_format=data_format, neuron_type=neuron_type, ens_params = ens_params)
		G2, G2_out = GeneratorCNN(G_r_out, hidden_num, channel, repeat_num, reuse=True, data_format=data_format, neuron_type=neuron_type, ens_params = ens_params)
		D, D_out = DiscriminatorCNN(G_out, channel, z_num, repeat_num, hidden_num, data_format=data_format, neuron_type=neuron_type, ens_params = ens_params)


	with net:
		out_p = nengo.Probe(G_out)

	sim = nengo_dl.Simulator(net, minibatch_size=batch_size)
	sim.load_params("C:\\Users\\Cody\\Downloads\\TensorFlow(DSRI)\\trainoutput.zip")
	sim.close()

if __name__ == "__main__":
	config, unparsed = get_config()
	main(config)