data = tg.Target(nBits=nBits)
ckt = ct.Circuit(nHidden=nHidden, nBits=nBits)
gt = tf.placeholder(dtype=tf.float32)
sig = tf.placeholder(dtype=tf.float32)
hpred, acl = ckt.encode(sig)
enc_t = data.Eval(gt, hpred, acl)[0]
#########################################################################
# Start TF session
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sigs, bits = data.uSamp(100000)
bits = np.sum(bits * np.reshape(2**np.arange(nBits), [1, nBits]), 1)
mfiles = glob('wts/*.model.npz')
for m in mfiles:
if os.path.isfile(m + '.png'):
continue
ut.netload(ckt, m, sess)
enc = sess.run(enc_t, feed_dict={sig: sigs})
plt.Figure()
plt.hold(True)
plt.plot(sigs.flatten(), bits.flatten(), '-g', linewidth=2)
plt.plot(sigs.flatten(), enc.flatten(), '-r', linewidth=2)
plt.title(m)
plt.savefig(m + '.png', dpi=120)
plt.close()
#########################################################################
# Start TF session (respecting OMP_NUM_THREADS)
# Restore model if necessary
#gopt = tf.GPUOptions(per_process_gpu_memory_fraction=0.25)
gopt = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gopt))
try:
sess.run(tf.global_variables_initializer())
except:
sess.run(tf.initialize_all_variables())
if ockp.latest is not None:
mprint("Loading model")
ut.netload(ckt,wtdir+'/iter_%d.model.npz'%ockp.iter,sess)
mprint("Loading state")
ut.loadopt(opt,ckt.wts,[],wtdir+'/iter_%d.state.npz'%ockp.iter,sess)
mprint("Done!")
niter = ockp.iter
#########################################################################
# Main training loop
mprint("Starting from Iteration %d" % niter)
while niter < maxiter:
e1v_a, lv_a = 0., 0.
for i in range(bgroup):
tStep = opt.minimize(loss + 1e-3 * tf.reduce_sum(tf.abs(ckt.wts['w1'])))
#########################################################################
# Start TF session (respecting OMP_NUM_THREADS)
# Restore model if necessary
gopt = tf.GPUOptions(per_process_gpu_memory_fraction=0.25)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gopt))
try:
sess.run(tf.global_variables_initializer())
except:
sess.run(tf.initialize_all_variables())
if ockp.latest is not None:
mprint("Loading model")
ut.netload(ckt, 'wts/iter_%d.model.npz' % ockp.iter, sess)
mprint("Loading state")
ut.loadopt(opt, ckt.wts, [], 'wts/iter_%d.state.npz' % ockp.iter, sess)
mprint("Done!")
niter = ockp.iter
#########################################################################
# Main training loop
mprint("Starting from Iteration %d" % niter)
while niter < maxiter:
e1v_a, e2v_a, lv_a = 0., 0., 0.
for i in range(bgroup):
sigs, bits = data.rSamp(bsz)