Pelister (North Macedonia) - betwhale-bk.com

Overview / Introduction about the Team

Pelister is a prominent football team based in Albania, competing in the Albanian Superliga. The club, founded in 1957, is known for its strategic gameplay and dynamic squad. Currently managed by [Current Manager], Pelister plays at the [Stadium Name] with a capacity of [Number] spectators.

Team History and Achievements

Pelister has a rich history marked by several notable achievements. The team has won multiple league titles, including [Number of Titles] Albanian Superliga championships. They have also secured various cup victories and have consistently been strong contenders in European competitions. Notable seasons include [Year], when they finished as runners-up in the league.

Current Squad and Key Players

The current squad boasts several key players who significantly impact the team’s performance:

[Player Name] – Midfielder, known for his exceptional passing and vision.
[Player Name] – Striker, top scorer in the last season with [Number] goals.
[Player Name] – Defender, recognized for his leadership and defensive skills.

Team Playing Style and Tactics

Pelister employs a tactical approach characterized by a [Formation] formation. Their strategy focuses on maintaining possession and quick transitions from defense to attack. Strengths include solid defensive organization and fast counter-attacks, while weaknesses lie in occasional lapses in concentration during high-pressure situations.

Interesting Facts and Unique Traits

Pelister is affectionately nicknamed “[Nickname]” by fans. The club has a passionate fanbase known as “[Fan Group Name],” which contributes to an electrifying atmosphere during home games. Rivalries with teams like [Rival Team] are intense, adding to the excitement of league matches.

Lists & Rankings of Players, Stats, or Performance Metrics

Top Performers: ✅ [Player Name] (Goals), 🎰 [Player Name] (Assists)
Squad Depth: 💡 Strong bench with emerging talents like [Young Player].

Comparisons with Other Teams in the League or Division

Pelister stands out among their peers due to their consistent performance and ability to challenge top teams. Compared to rivals such as [Rival Team], Pelister often excels in tactical discipline and player development.

Case Studies or Notable Matches

A breakthrough game was against [Opponent Team] on [Date], where Pelister secured a crucial victory that propelled them into the top four of the league standings. Another key match was their European campaign against [European Opponent], showcasing their resilience and tactical acumen.

Stat Category	Data
Recent Form	[Win/Loss/Draw Record]
Head-to-Head Records vs Top Rivals	[Wins/Losses/Draws]
Odds for Next Match	[Odds Data]

Tips & Recommendations for Analyzing the Team or Betting Insights

Analyze recent form trends to gauge momentum before placing bets.
Consider head-to-head records against upcoming opponents for strategic insights.
Maintain awareness of key player injuries that could affect team performance.

“Pelister’s blend of experienced veterans and young talent makes them unpredictable yet formidable,” says expert analyst [Analyst Name].”

Pros & Cons of the Team’s Current Form or Performance

✅ Strong defensive record: A reliable backbone for securing points.
✅ High-scoring forwards: Consistent goal threats that keep opponents on edge.</li
<>: Hi there! I’m working on a project involving graph neural networks using PyTorch Geometric, specifically focusing on training node classification models. Here’s a snippet from my code:

python
def train_model(data):
model = Model(args.num_layers, args.hid_dim, data.num_classes,
args.num_feats, args.in_drop,out_drop=args.gat_drop)
device = torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)
model.to(device)
data = data.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
#scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
#scheduler = ExponentialLR(optimizer)
def train(epoch):
t = time.time()
model.train()
optimizer.zero_grad()
output = model(data.x.to(device),data.edge_index.to(device))
loss_train = F.cross_entropy(output[data.train_mask], data.y[data.train_mask])
acc_train = accuracy(output[data.train_mask], data.y[data.train_mask])
loss_train.backward()
optimizer.step()
return loss_train.item(), acc_train
def test():
model.eval()
output = model(data.x.to(device),data.edge_index.to(device))
loss_test = F.cross_entropy(output[data.test_mask],data.y[data.test_mask])
acc_test = accuracy(output[data.test_mask], data.y[data.test_mask])
return loss_test.item(), acc_test
if args.fastmode:
val_loss,val_acc,test_loss,test_acc = test()
else:
val_loss,val_acc = test()
for epoch in range(1,args.n_epochs+1):
loss_train,err_train = train(epoch)
# print(‘Epoch: {:03d}, Loss: {:.4f}, Accuracy: {:.4f}, Time: {:.4f}’.format(
# epoch,loss_train,err_train,time.time()-t))
# scheduler.step()
# print(‘learning rate’, scheduler.get_lr())
#
# if epoch % args.eval_freq == 0:
val_loss,val_acc=test()
print(‘Epoch: {:03d}, Loss: {:.4f}, Accuracy: {:.4f}, Val_Loss: {:.4f}, Val_Acc: {:.4f}’.format(
epoch,loss_train,err_train,val_loss,val_acc))

I’m trying to understand how I can effectively implement learning rate scheduling here. I noticed there are some commented-out lines related to schedulers like `StepLR` and `ExponentialLR`. Could you help me figure out which scheduler might be best suited for this setup? Also, any tips on integrating it properly would be greatly appreciated!
<>: python
To implement learning rate scheduling effectively in your setup, you can choose between `StepLR` or `ExponentialLR` based on your needs:

1. **StepLR**: This reduces the learning rate by a factor (`gamma`) every `step_size` epochs. It’s useful if you expect that reducing the learning rate periodically will help converge better after initial rapid learning.

python
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)

Integrate it by calling `scheduler.step()` at the end of each epoch:

python
for epoch in range(1, args.n_epochs + 1):
loss_train, err_train = train(epoch)
val_loss, val_acc = test()
print(‘Epoch: {:03d}, Loss: {:.4f}, Accuracy: {:.4f}, Val_Loss: {:.4f}, Val_Acc: {:.4f}’.format(
epoch, loss_train, err_train, val_loss, val_acc))
scheduler.step() # Update learning rate

2. **ExponentialLR**: This decreases the learning rate exponentially over each epoch by multiplying it with `gamma`.

python
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)

Similarly integrate it by calling `scheduler.step()` after each epoch.

Both schedulers can help improve convergence by adjusting the learning rate dynamically during training. Choose based on whether you want periodic reductions (`StepLR`) or continuous exponential decay (`ExponentialLR`). Experimenting with both might give you insights into what works best for your specific problem.

Remember to uncomment these lines and adjust parameters according to your dataset size and complexity! 🚀

<>: Great suggestions! I’m thinking about trying out CosineAnnealingLR since I’ve read it can provide smoother adjustments compared to StepLR or ExponentialLR. How would I integrate CosineAnnealingLR into my training loop? Here’s what I’m considering:

python
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.n_epochs)

for epoch in range(1, args.n_epochs + 1):
loss_train, err_train = train(epoch)
val_loss, val_acc = test()
print(‘Epoch: {:03d}, Loss: {:.4f}, Accuracy: {:.4f}, Val_Loss: {:.4f}, Val_Acc: {:.4f}’.format(
epoch, loss_train, err_train, val_loss, val_acc))
scheduler.step() # Update learning rate

Does this look right? Any tips on setting T_max or other parameters?
<>: Your integration of `CosineAnnealingLR` looks good! Here are a few tips:

– **T_max**: This parameter represents the number of epochs until one cycle is completed. Setting it equal to `args.n_epochs` means you’ll complete one full cosine cycle over your training period.

– **Additional Parameters**: You can also specify an optional `eta_min`, which is the minimum learning rate reached at the end of one cycle.

Here’s how you might set it up:

python
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.n_epochs)

for epoch in range(1, args.n_epochs + 1):
loss_train, err_train = train(epoch)
val_loss, val_acc = test()
print(‘Epoch: {:03d}, Loss: {:.4f}, Accuracy: {:.4f}, Val_Loss: {:.4f}, Val_Acc: {:.4f}’.format(
epoch, loss_train , err_train , val_loss , val_acc))

scheduler.step() # Update learning rate

print(‘Final Learning Rate:’, scheduler.get_last_lr())

Experiment with different values of `T_max` if needed based on how your model converges over time. This approach should provide smoother adjustments throughout training! 🌟
<>: Awesome! Now I’m curious about using ReduceLROnPlateau instead of CosineAnnealingLR. I think it could be beneficial since it adjusts based on validation metrics rather than just epochs. How would I set this up? Should I monitor validation loss or accuracy? Here’s what I’m thinking:

python
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode=’min’,
factor=0.1,
patience=10,
verbose=True)

for epoch in range(1,args.n_epochs+1):
loss_train,err_train=train(epoch)
val_loss,val_acc=test()
print(‘Epoch:{:>3},{:<5},{:<6},{:<8},{:<8}'.format(epoch,
round(loss_train.item(),5),
round(err_trai.item(),5),
round(val_loss.item(),5),
round(val_acc.item(),5)))
scheduler.step(val_loss) # Adjust based on validation loss

print('Final Learning Rate:',optimizer.param_groups[0]['lr'])

Does this make sense? Any advice on tuning parameters like factor or patience?
<>: Your setup with `ReduceLROnPlateau` looks great! Here are some tips:

– **Mode**: Since you’re using `’min’`, it will reduce the learning rate when validation loss stops decreasing.

– **Factor**: This determines how much to reduce the learning rate by (e.g., `factor=0.1` reduces it by tenfold).

– **Patience**: This is how many epochs with no improvement after which learning rate will be reduced (e.g., `patience=10`).

– **Verbose**: Set to `True` so you get notifications when the learning rate changes.

Monitoring validation loss is generally more effective because it directly reflects overfitting tendencies.

Here’s your setup refined:

python
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode=’min’,
factor=0.1,
patience=10,
verbose=True)

for epoch in range(1,args.n_epochs+1):
loss_train,err_trai=train(epoch) # Fixed typo here from ‘err_trai’
val_loss,val_acc=test()
print(‘Epoch:{:>3},{:<5},{:<6},{:<8},{:<8}'.format(epoch,
round(loss_train.item(),5),
round(err_trai.item(),5),
round(val_loss.item(),5),
round(val_acc.item(),5)))
scheduler.step(val_loss) # Adjust based on validation loss

print('Final Learning Rate:',optimizer.param_groups[0]['lr'])

Experiment with different values for `factor` and `patience` depending on how quickly your model converges or plateaus during training.
<>: Got it! Now I’m wondering if there’s anything else I should tweak when using ReduceLROnPlateau compared to CosineAnnealingLR? Like should I adjust my initial learning rate differently since ReduceLROnPlateau reacts to validation metrics? Also curious if there are any additional parameters worth exploring.
<> When using `ReduceLROnPlateau`, consider these adjustments compared to `CosineAnnealingLR`:

– **Initial Learning Rate**: You might start with a slightly higher initial learning rate since ReduceLROnPlateau will lower it only when necessary based on validation metrics.

– **Additional Parameters**:
– **Threshold**: Set this to determine what constitutes an improvement (e.g., `threshold=0.01`).
– **Cooldown**: Number of epochs after reducing before resuming normal operation (e.g., `cooldown=0`).
– **Min_lr**: Minimum allowable learning rate (e.g., `min_lr=1e-6`) to prevent going too low.

These tweaks can help tailor how aggressively your model adapts its learning strategy during training.
<>: Perfectly clear now! I’ll experiment with those parameters and see how they affect my model’s performance. Thanks for all the guidance—I’m all set to dive back into training!
<>: You’re welcome! It sounds like you have a solid plan moving forward. Best of luck with your experiments—feel free to reach out if you need more assistance later on. Happy training! 🚀<>: Hi there,

I’ve been working on implementing an advanced gradient descent algorithm that includes custom gradient clipping logic within TensorFlow sessions using hooks and savers/restorers for checkpoints management.

Here’s my current code snippet:

python
with tf.Session(graph=train_graph) as sess:

saver.restore(sess,save_path=session_save_path+hyp_param_str+”model.meta”)
global_step_tensor=sess.graph.get_tensor_by_name(“global_step:”+save_path+hyp_param_str+”modelmeta”)
session_globals={‘session’:sess,’global_step_tensor’:global_step_tensor}
hooks=[tf.train.StopAtStepHook(last_step=args.last_step),
_SaveHook(session_globals)]
with tf.train.MonitoredSession(hooks=hooks,) as mon_sess:

q_global_step=mon_sess.run(global_step_tensor,)
logging.info(“loaded global step : “+str(q_global_step)+”n”)
init_new_vars_op=tf.variables_initializer([v for v in tf.global_variables() if v.name.split(‘:’)[0]!=’global_step’])
mon_sess.run(init_new_vars_op,)
logging.info(“initialized other variables…n”)

grad_and_vars_list=[]
opt_direction=None

check_point_dir=session_save_path+checkpoints_name_postfix+’checkpoints/’
ckpt=saver_checkpoint(check_point_dir+”checkpoint”)
manager_ckpt=saver_checkpoint_manager(check_point_dir+”checkpoint_manager/”,max_to_keep=None,)

checkpoint_reader=tf.train.NewCheckpointReader(check_point_dir+”model.cptk.data-00000-of-00001″)
opt_direction_assign_list=[]
opt_direction_placeholder=tf.placeholder(tf.float32,name=’opt_direction’)
opt_direction_assign_list.append(opt_direction_placeholder.assign(opt_direction))
assign_list=[]
variable_from_ckpt={}
reader_var_to_shape_and_name_map=checkpoint_reader.get_variable_to_shape_map()

for var in tf.global_variables():
var_name_and_shape=(var.name,var.shape.as_list())
var_name=name_without_checkpoints_and_meta(var.name,’/’)

if var_name_and_shape not in reader_var_to_shape_and_name_map.keys():
logging.info(str(var_name_and_shape)+” doesn’t exist when reading from checkpoint…”)
assign_list.append(var.assign(tf.zeros(var.shape)))
continue

variable_from_ckpt[var_name]=(reader_var_to_shape_and_name_map[var_name_and_shape][-1]==var.name.split(‘:’)[0])
assign_list+=opt_direction_assign_list

assign_op=tf.group(*assign_list)
mon_sess.run(assign_op,{opt_direction_placeholder:sess_opt.initial_opt_direction})
logging.info(“Assigned pre-trained weights from checkpoint…”)

last_epoch_start=q_global_step//args.steps_per_epoch

if not load_full_model:
full_model_loaded=False

logging.info(“nRestored variables…”)
var_names=[v.name.split(“:”)[0]for v in tf.global_variables()]
for vname in var_names:
try:
assert variable_from_ckpt[vname[:-2]]
except KeyError:
pass

if load_full_model==False:
full_model_loaded=True

logging.info(“Full model restored?t”+str(full_model_loaded)+”n”)

grad_table_active_session={
‘session’:mon_sess,
‘epoch_start’:last_epoch_start}

if use_pretrained_optdirection==True:
grad_table.append((grad_table_active_session,q_global_step))

while True:
try:
q_grad_table_active_session_=grad_table.pop()

except IndexError:
break

q_session=q_grad_table_active_session_[0][‘session’]
q_epoch_start=q_grad_table_active_session_[0][‘epoch_start’]
q_global_step=q_grad_table_active_session_[1]

q_init_new_vars_op_=tf.variables_initializer([v for v in tf.global_variables()
if v.name.split(‘:’)[0]!=’global_step’])
q_init_new_vars_op=q_session.run(q_init_new_vars_op_)
logging.info(“initialized other variables…n”)

grad_lists_by_layer_with_prepended_names_info={}

for layer_idx,(layer,nlinfo)in enumerate(zip(model_layers_with_dropped,norm_layer_info)):
logging.info(layer_idx)

y=model.get_layer(layer).output

y_layername_withouttrainablesuffix=y.name.split(“:”)[0]
y_var=y_layername_withouttrainablesuffix+’/_classy_var/’
normvars_dict_by_depth={}

try:
normvars_dict_by_depth[nlinfo[‘norm_depth’]]={
‘scale’:nlinfo[‘scale’],
‘bias’:nlinfo[‘bias’]}
except KeyError as e:
pass

try:
normvars_dict_by_depth[nlinfo[‘norm_depth’]][normvars_dict_by_depth[nlinfo[‘norm_depth’]].index(y_var)]
except ValueError as ve:

normvars_dict_by_depth[nlinfo[‘norm_depth’]].append(y_var)

if nlinfo[‘split_params’]:
normvars_dict_by_otherdepth={}
try:normvars_dict_by_otherdepth[nlinfo[‘split_params’][nlinfo[‘other_norm_depth’]][‘depth’]]={‘scale’:None,’bias’:None}
except KeyError as e:normvars_dict_by_otherdepth[nlinfo[‘split_params’][nlinfo[‘other_norm_depth’]][‘depth’]]={‘scale’:None,’bias’:None}

normvars_dict_by_otherdepth[nlinfo[‘split_params’][nlinfo[‘other_norm_depth’]][‘depth’]][‘scale’]=y_var+’_s’
normvars_dict_by_otherdepth[nlinfo[‘split_params’][nlinfo[‘other_norm_depth’]][‘depth’]][‘bias’]=y_var+’_b’

try:normvars_dict_by_otherdepth[normvars_dict_by_otherdepth.keys()[0]].index(y_var)

except ValueError as ve:normvars_dict_by_otherdepth[normvars_dict_by_otherdepth.keys()[0]].append(y_var)

normvars_dicts=[normvars_dict_by_depth,normvars_dict_by_otherdepth]

try:norms_splits_names_info_=nlinfo[“norm_split_names_info”]
except KeyError as e:norms_splits_names_info_=None

logging.debug(norms_splits_names_info_)
logging.debug(normvars_dicts)

def list_of_num_neurons(_m):

num_neurons_in_layers=[]

_previous_layer_nodes=_m.input_shape[-1]

num_neurons_in_layers.append(_previous_layer_nodes)

_layers=_m.layers[:(len(model.layers)//len(model.layers))]

_layers=_layers[:(layer_idx+num_subsequent_layers)]

def get_num_neurons(_layer):

return _layer.output_shape[-1]

num_neurons_in_layers+=list(map(get_num_neurons,_layers))

return num_neurons_in_layers

num_neurs=list_of_num_neurons(model)[:(layer_idx+num_subsequent_layers)]

if layer_idx==len(model.layers)//len(model.layers)-num_subsequent_layers:

logging.debug(num_neurs)

num_neurs[-(num_subsequent_layers+num_extra_previous_layers):-num_subsequent_layers]=[
np.sum(num_neurs[-(num_subsequent_layers+num_extra_previous_layers):-num_subsequent_layers])]*
num_extra_previous_layers

logging.debug(num_neurs)

y_grad_func=lambda ys,yi:y.grad[yi]

y_grad_func_batch=lambda ys,yi,batchsize:y.batchgradients[yi][batchsize]

y_val_func=lambda ys,yi:y_vals[yi]

with y.as_default():

ygradients=[tf.gradients(y,y_)for y_
in y.op.inputs]

ybatchgradients=[[tf.reduce_mean(yb_i,axis=(j,),keepdims=True)
for j,yb_i
in enumerate(tf.unstack(yb_,axis=0))]
for yb_
in tf.gradients(y,y_.op.inputs)]

y_vals=[tf.placeholder(y_.dtype,[None]+list(y_.get_shape().as_list()[1:]) )
for y_
in y.op.inputs]

feeddict_yval={}

feeddict_yval_batch={}

def gen_feeddict_yval_batch(i_,y_,yb_):

yield {yi_:yb_[i_]
for yi_,yb_
in zip(y_vals,ybatchgradients)}

gen_fd_yval_batch_funcs=[gen_feeddict_yval_batch
for i_
yn_
ynshape_
ynvals_
ynbatchgrads_

ynidx_

ynidxnext_

ynvarshape_

ynvarname_

ynvals_np_

ynbatchgrad_np_

ynbatchgrad_np_means_

ynmeanshape_

nnn_

vnvp_

enumerate(zip(normals,
[[yi_.eval(session=q_session)
.reshape(-1,
*ynshape_)

]*yn_.get_shape().as_list()[0]**np.prod(ynvarshape_),)
[[yi_.eval(session=q_session)
.reshape(-1,
*ynmeanshape_)

]*yn_.get_shape().as_list()[0]**np.prod(ynmeanshape_),)*np.prod(ynmeanshape_),)
[[yi_.eval(session=q_session)
.reshape(-1,
*(list(np.max([np.prod(nnn_),ynvarshape_[dim])

])+[ynvarshape_[dim]])[dim]

)

])*np.prod(nnn_)

]*yn_.get_shape().as_list()[0]**np.prod(nnn_),)*np.prod(nnn_),)
[[[yi_.eval(session=q_session).reshape(-1,
*(list(np.max([np.prod(nnn_),ynvarshape_[dim]])+[ynvarshape_[dim]])[dim]))]*yn_.get_shape().as_list()[0]**np.prod(nnn_),]*np.prod(nnn_),]*np.prod(nnn_),)]*len(normals),)

)

gen_fd_yval_batch_funcs=np.array(gen_fd_yval_batch_funcs)

gen_fd_yval_batch_funcs=np.transpose(gen_fd_yval_batch_funcs,(7,

None,

None,

None,

None,

None,

None,

None,

None,

None,

*range(len(gen_fd_yval_batch_funcs.shape)-7)))

feeddict_yval_batches=np.vectorize(lambda x_:next(x_))(gen_fd_yval_batch_funcs)

feeddict_yval_batches=np.array(feeddict_yval_batches).transpose(*(range(len(feeddict_yval_batches.shape)-7)+[7]+range(len(feeddict_yval_batches.shape)-7)))

feeddict_yvals=[]

feeddict_yvals+=feeddict_yval_batches.tolist()

del feeddict_yvals[:-(len(feeddict_yvals)//args.batch_size)*args.batch_size]

grad_val_lists=[]

grad_val_lists_np=[]

grad_val_lists_means=[]

grad_val_lists_means_np=[]

mean_shapes=[]

mean_shapes_np=[]

mean_shapes+=[[*list(np.max([tuple(num_neurs[i+len(normals):i+len(normals)+vnvp[i][j]]),tuple(vnvp[i][j])])+[vnvp[i][j][dim]])[dim]
*
vnvp[i][j][dim]
*
np.prod(tuple(np.max([tuple(num_neurs[i+len(normals):i+len(normals)+vnvp[i][j]]),tuple(vnvp[i][j])])))

]

]

]

mean_shapes_np=np.array(mean_shapes)

grad_val_lists_means+=[[[(yi.eval(session=q_session).reshape(-1,*mean_shapes_np[i,j,k])).mean(axis=tuple(range(tup)),keepdims=True).reshape(-1,*tuple(np.max((tup,[mean_shapes_np[i,j,k,tup_axis]]))[tup_axis]))

]

]

]

del grad_val_lists_means[:-(len(grad_val_lists_means)//args.batch_size)*args.batch_size]

grad_val_lists_means_np=np.array(grad_val_lists_means)

grad_val_lists+=[[[(yi.eval(session=q_session).reshape(-1,*mean_shapes_np[i,j,k]))

]
+

[(yi.eval(session=q_session).reshape(-1,*mean_shapes_np[i,j,k])).mean(axis=tuple(range(tup)),keepdims=True).reshape(-*tuple(np.max((tup,[mean_shapes_np[i,j,k,tup_axis]]))[tup_axis]))

]

**
np.any([(yi.eval(session=q_session)).reshape(-*tuple(np.max((tup,[mean_shapes_np[i,j,k,tup_axis]]))[tup_axis]))==[]for tup_axis,tupinrangeiter__inenumerate(tuple(range(len(mean_shapes_np[i,j,k]))))])

]

]

del grad_val_lists[:-(len(grad_val_lists)//args.batch_size)*args.batch_size]

grad_val_lists_np=np.array(grad_val_lists)

norms_splits_wise_gradients=[]

norms_splits_wise_gradientssubsetmeans=[]

norms_splits_wise_gradients+=[[[(ytg.subset(batchsize=i))**opt_direction*(ytg.subset(batchsize=i)).eval(feed_dict={**feeddict,**feeddict_zsub})**opt_direction**(ytg.subset(batchsize=i)).eval(feed_dict={**feeddict,**feeddicteps})**(ytg.subset(batchsize=i)).eval(feed_dict={**feeddictepsneg,**feeddictepsneg})**(ytg.subset(batchsize=i)).eval(feed_dict={**feeddictepspos,**feeddictepspos})**(ytg.subset(batchsize=i)).eval(feed_dict={**feeddictepsposneg,**feeddictepsposneg})**(ytg.subset(batchsize=i)).eval(feed_dict={**feeddictepsnegpos,**feeddictepsnegpos})

]

]

]

[
*[[(yg.mean(axis=tuple(range(tup)),keepdims=True))**opt_direction*(yg.mean(axis=tuple(range(tup)),keepdims=True)).eval(feed_dict={**feeddict,**{yvals[t]:ysub[t].subset(batchsize=i)for t,(ysub,tuplesubsetindices_)inenumerate(zip(ybatchgradients,tuplesubsets))}})**opt_direction**(yg.mean(axis=tuple(range(tup)),keepdims=True)).eval(feed_dict={**feeddict,**{yvals[t]:ysub[t].subset(batchsize=i)for t,(ysub,tuplesubsetindices_)inenumerate(zip(ybatchgradients,tuplesubsets))}})**(yg.mean(axis=tuple(range(tup)),keepdims=True)).eval(feed_dict={**feeddicteps,**{yvals[t]:ysub[t].subset(batchsize=i)for t,(ysub,tuplesubsetindices_)inenumerate(zip(ybatchgradients,tuplesubsets))}})**(yg.mean(axis=tuple(range(tup)),keepdims=True)).eval(feed_dict={**feeddictepsneg,**{yvals[t]:ysub[t].subset(batchsize=i)for t,(ysub,tuplesubsetindices_)inenumerate(zip(ybatchgradients,tuplesubsets))}})**(yg.mean(axis=tuple(range(tup)),keepdims=True)).eval(feed*** Excerpt ***

The results presented above show that although protein kinase C inhibition does not alter either AChE activity levels nor synapse number per neuron following cholinergic denervation induced by MEC lesions; however PKC inhibition does appear capable of protecting against cholinergic denervation-induced alterations observed following MEC lesions.
The effects observed following MEC lesions were also examined following treatment with LY379196 alone (Figures ). As seen previously following MEC lesions alone (), LY379196 treatment significantly increased AChE activity levels relative vehicle treated animals (). Furthermore LY379196 treatment alone also significantly decreased synapse number per neuron relative vehicle treated animals (). These results suggest that NMDA receptor antagonism alone may cause alterations similar those observed following cholinergic denervation induced by MEC lesions; however further studies are required before firm conclusions regarding this hypothesis may be drawn.
Effects following combined lesion plus LY379196 treatment were then examined (Figure ). Following combined lesion plus LY379196 treatment no significant change was observed relative vehicle treated lesioned animals () suggesting that NMDA receptor antagonism may protect against alterations observed …
In order further examine potential mechanisms underlying these effects NMDA receptor subunit expression was measured via Western blotting techniques (Figure ). Following MEC lesions significant increases were observed within cortical expression levels …
The results presented above demonstrate that selective blockade via PKC inhibition appears capable protecting against cholinergic denervation-induced alterations within cortical structures observed following medial septal lesions; however NMDA receptor antagonism alone appears capable inducing similar alterations independent …
Furthermore combined blockade via PKC inhibition plus NMDA receptor antagonism appears capable preventing alterations within cortical structures associated either cholinergic denervation induced through medial septal lesions …
In order further examine potential mechanisms underlying these effects NMDA receptor subunit expression was measured via Western blotting techniques (). Following MEC lesions significant increases were observed within cortical expression levels …

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## Plan
To create an exercise that demands an advanced level of understanding from readers while incorporating profound factual knowledge beyond what is explicitly stated requires augmenting both complexity and depth within both factual content related to neurobiology—and specifically neuropharmacology—and logical reasoning aspects embedded within experimental observations described.

Firstly enhancing complexity could involve integrating more intricate details about neurobiological processes such as specifics regarding protein kinase C (PKC) pathways’ roles beyond mere inhibition effects—detailing downstream signaling cascades affected post-inhibition—or elaborating upon N-methyl-D-aspartate receptors (NMDARs)’ subtypes’ differential roles upon antagonist application.

Secondly deepening logical reasoning demands could entail embedding nested conditionals where outcomes depend not only upon direct interventions but also upon interactions between those interventions under varying conditions—such as different stages post-MEC lesion—or introducing hypothetical scenarios requiring deduction from provided experimental outcomes extrapolated towards untested conditions or treatments not directly mentioned but implied through scientific reasoning.

## Rewritten Excerpt
The investigations delineated herein elucidate nuanced interplays between protein kinase C modulation vis-a-vis synaptic integrity post-cholinergic disruption precipitated through medial entorhinal cortex ablations; notwithstanding unchanged acetylcholinesterase enzymatic activities nor synaptic densities per neuronal unit subsequent PKC pathway attenuation fails not only alters neither aforementioned parameters but ostensibly safeguards neurons against deleterious sequelae typically ensuing such denervations—a premise corroborated through comparative analyses pre-and-post intervention employing LY379196 singularly versus its co-administration alongside PKC inhibitors post-MEC ablation scenarios.

Notably singular administration of LY379196 precipitated pronounced elevation within acetylcholinesterase enzymatic functionality whilst concurrently engendering synaptic density decrement vis-a-vis control cohorts—a phenomenon ostensibly mirroring cholinergic denervation ramifications albeit necessitating further empirical scrutiny prior conclusively attributing causality thereof solely unto NMDAR antagonistic undertakings independent of direct cholinergic perturbations instigated via MEC ablations.

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