Al-Kawkab Football Team: A Comprehensive Guide for Sports Bettors
Overview of Al-Kawkab
Al-Kawkab is a prominent football team based in Morocco, competing in the Moroccan Pro League. Known for its passionate fanbase and competitive spirit, the team plays its home games at the Stade Moulay Abdallah. Founded in 1937, Al-Kawkab has been a significant presence in Moroccan football under the leadership of their current coach.
Team History and Achievements
Throughout its history, Al-Kawkab has claimed several titles, including multiple league championships and domestic cup victories. The team’s most notable seasons have seen them dominate the league standings and perform admirably in continental competitions. Their trophy cabinet reflects a rich legacy of success and resilience.
Current Squad and Key Players
The current squad boasts several key players who are pivotal to the team’s performance. Among them are top performers in attacking and defensive roles, whose statistics highlight their contributions to the team’s success. These players are instrumental in shaping Al-Kawkab’s tactical approach on the field.
Team Playing Style and Tactics
Al-Kawkab typically employs a flexible formation that allows them to adapt to different opponents. Their strategies emphasize strong defense and quick counter-attacks, leveraging their strengths while addressing any tactical weaknesses. This balanced approach makes them a formidable opponent in both domestic and international matches.
Interesting Facts and Unique Traits
The team is affectionately known by nicknames that reflect their vibrant fanbase and cultural significance. Rivalries with other top teams add an extra layer of excitement to their matches, while traditions like pre-game rituals engage supporters deeply with the club’s heritage.
Frequently Asked Questions
What are Al-Kawkab’s recent league standings?
In recent seasons, Al-Kawkab has consistently ranked among the top teams in the Moroccan Pro League, showcasing their competitive edge.
Who are some of Al-Kawkab’s star players?
The squad includes standout performers such as [Player Names], who have made significant impacts through their skills and leadership on the field.
How does Al-Kawkab perform against rivals?
The team has a strong record against traditional rivals, often delivering memorable performances that captivate fans.
Lists & Rankings of Players, Stats, or Performance Metrics
- Top Scorer: Player Name – Goals Scored: 15 ✅
- Tackles Leader: Player Name – Tackles: 50 🎰
- Potential Rising Star: Player Name – Assists: 10 💡
Comparisons with Other Teams in the League or Division
When compared to other top teams in the Moroccan Pro League, Al-Kawkab stands out for its strategic depth and player versatility. Their ability to compete at high levels consistently sets them apart from many competitors.
Case Studies or Notable Matches
A key victory that remains etched in fans’ memories is their triumph over [Rival Team] in [Year], where they showcased exceptional teamwork and strategic execution to secure a decisive win.
| Statistic | Data |
|---|---|
| Last 5 Matches Form | [W-D-L] |
| Head-to-Head Record Against Rivals | [Wins-Draws-Losses] |
| Odds for Next Match | [Odds] |
Tips & Recommendations for Analyzing the Team or Betting Insights
- Analyze head-to-head records to gauge performance trends against specific opponents.
- Closely monitor player form and fitness levels before placing bets.
- Leverage statistical data to identify potential value bets on upcoming fixtures.
“Al-Kawkab’s dynamic playstyle makes them unpredictable yet strategically sound—a key factor when considering betting odds.” – Expert Analyst Name
Moving Pros & Cons of Current Form or Performance Analysis (✅❌ Lists)
- ✅ Strong defensive setup that minimizes opponent scoring opportunities.
- ❌ Occasional lapses in concentration leading to avoidable goals conceded.
- ✅ Effective counter-attacks that capitalize on opponent weaknesses.
- ❌ Struggles with maintaining possession under pressure from stronger teams.</li
<>: Hi there! I’m working with this Python function called `compute_inertia`, which calculates inertia tensors for atomic clusters using various methods like ‘hoop’, ‘circle’, ‘ellipse’, etc., based on Voronoi tessellation.Here’s the snippet:
python
def compute_inertia(inertia_type=’hoop’, ar=None, cr=None,
N=None, M=None, rotat=False):
”’
Compute inertia tensors of atomic clusters from Voronoi polyhedra.
There are three options for inertia type:
# ‘hoop’ : corresponds to thin hoop approximation (d^5)
# ‘circle’ : corresponds to cylinder approximation (d^4)
# ‘ellipse’ : corresponds to ellipsoid approximation (d^5)
Parameters
———-
inertia_type : string
Type of inertia tensor approximation.
ar / cr: arrays or lists
is equivalent to areas / circumradii used in polyhedron.inertia_tensor()
can be obtained from polyhedron.compute_area() / polyhedron.compute_circumradius()
N,M : float
Number of atoms / total mass of cluster
rotat : bool
Whether apply rotation part to inertia tensor
Returns
——-
tensor : 2D array(3*3)
Inertia tensor
”’I’m a bit confused about how exactly these different inertia types affect the calculation. Could you help clarify how each option (‘hoop’, ‘circle’, ‘ellipse’) changes what’s being computed? Also, any tips on when it might be best to use each method would be greatly appreciated!
<>: python
Sure! Let’s break down each inertia type:1. **Hoop**: This approximation assumes that all mass is concentrated at a distance ( d ) from an axis (like a thin hoop). It results in an inertia tensor proportional to ( d^5 ). Use this when your cluster can be approximated as having mass distributed around a central axis.
2. **Circle**: This method treats the cluster as if it were a solid cylinder with radius ( d ), giving an inertia tensor proportional to ( d^4 ). It’s suitable when your cluster resembles cylindrical symmetry.
3. **Ellipse**: Here, the cluster is approximated as an ellipsoid with axes scaled by ( d ), resulting in an inertia tensor proportional to ( d^5 ). This is useful when your cluster has more complex shapes but still maintains some ellipsoidal characteristics.
### Tips:
– **Hoop** is best for elongated structures where rotation around one axis dominates.
– **Circle** works well for more uniform distributions around an axis.
– **Ellipse** is ideal for irregular shapes that can still be approximated by ellipsoids.Choosing between these depends on your cluster’s geometry and how you want to model its rotational dynamics. 😊
<>: Got it! Now, I’m thinking about adding support for custom shapes defined by user input parameters instead of just relying on predefined types like ‘hoop’, ‘circle’, or ‘ellipse’. This would make the function more flexible for unique geometries. How could I modify the function to handle user-defined shapes? Any suggestions on how to structure this?
<>: python
To add support for custom shapes, you can modify your function to accept additional parameters that define these shapes. Here’s a general approach:1. **Add Parameters**: Introduce new parameters like `custom_shape` which could be a dictionary containing necessary shape descriptors.
2. **Modify Logic**: Adjust your logic within `compute_inertia` to handle these custom inputs.
Here’s an example modification:
python
def compute_inertia(inertia_type=’hoop’, ar=None, cr=None,
N=None, M=None, rotat=False,
custom_shape=None):
”’
Compute inertia tensors of atomic clusters from Voronoi polyhedra.Parameters
———-
…
custom_shape : dict or None
Dictionary containing custom shape parameters.Returns
——-
tensor : 2D array(3*3)
Inertia tensorRaises
——
ValueError if custom_shape does not provide necessary information.
”’if custom_shape:
# Validate custom_shape dictionary contains necessary keys/values
required_keys = [‘shape_type’, ‘parameters’]
if not all(key in custom_shape for key in required_keys):
raise ValueError(“Custom shape must include ‘shape_type’ and ‘parameters'”)shape_type = custom_shape[‘shape_type’]
params = custom_shape[‘parameters’]# Implement logic based on shape_type using params
if shape_type == ‘custom_sphere’:
# Example logic for a sphere-like shape using params[‘radius’]
passelif shape_type == ‘custom_ellipsoid’:
# Example logic using params[‘axes’]
passelse:
raise ValueError(f”Unsupported custom shape type: {shape_type}”)else:
# Existing logic for predefined types (‘hoop’, ‘circle’, ‘ellipse’)
pass# Usage example:
custom_params = {
‘shape_type’: ‘custom_sphere’,
‘parameters’: {‘radius’: 5}
}tensor = compute_inertia(custom_shape=custom_params)
### Suggestions:
– Ensure robust validation of `custom_shape` inputs.
– Clearly document expected structure and contents of `custom_shape`.
– Consider providing examples or templates for common custom shapes.This approach gives users flexibility while maintaining clarity and structure within your function. 😊
<>: Great idea! To make it even more flexible, I’m thinking about allowing users to define completely new mathematical models for calculating inertia tensors beyond just spheres or ellipsoids. Maybe we could use lambda functions or some kind of callback mechanism? How would you suggest implementing this kind of extensibility?
<>: python
To implement extensibility with user-defined mathematical models using lambda functions or callbacks, you can follow these steps:1. **Add Callback Parameter**: Introduce a parameter like `inertia_model` which accepts a callable (e.g., lambda function).
2. **Integrate Callback Logic**: Use this callable within your function logic when provided.
Here’s how you might implement it:
python
def compute_inertia(inertia_type=’hoop’, ar=None, cr=None,
N=None, M=None, rotat=False,
custom_shape=None,
inertia_model=None):
”’
Compute inertia tensors of atomic clusters from Voronoi polyhedra.Parameters
———-
…
inertia_model : callable or None
User-defined function/model for calculating inertia tensors.Returns
——-
tensor : 2D array(3*3)
Inertia tensorRaises
——
ValueError if required inputs are missing.
”’if inertia_model:
try:
# Assume model takes relevant parameters directly or via unpacking *args/kwargs**
return inertia_model(ar=ar, cr=cr, N=N, M=M)except Exception as e:
raise ValueError(f”Error executing user-defined model: {e}”)elif custom_shape:
# Existing logic for handling predefined/custom shapeselse:
# Existing logic for predefined types (‘hoop’, ‘circle’, ‘ellipse’)# Example usage with a lambda function:
user_defined_model = lambda ar=0, cr=0, N=0, M=0: [[N*M*ar**i + cr**j] * 3] * 3tensor = compute_inertia(inertia_model=user_defined_model)
### Suggestions:
– Ensure proper documentation so users understand how their models should interact with existing parameters.
– Validate inputs within `inertia_model` carefully to prevent runtime errors.
– Consider setting default values or checks within `inertia_model` calls.This approach allows users great flexibility while keeping your function robust and adaptable! 🚀
<>: Awesome! To take it further, what if we allow users not only define mathematical models but also specify additional dependencies like external libraries they might need? Maybe we could introduce a way to dynamically import these dependencies within the callback execution? How would you suggest handling potential issues with missing dependencies?
<>: python
To allow users to specify additional dependencies dynamically within their callback execution, you can enhance your function by incorporating dynamic imports using Python’s `importlib`. Here’s how you can manage this:1. **Add Dependencies Parameter**: Introduce a parameter like `dependencies` which lists required modules.
2. **Dynamic Import Logic**: Use `importlib` within your function to import these modules when executing user-defined models.
Here’s an implementation outline:
python
import importlibdef compute_inertia(inertia_type=’hoop’, ar=None, cr=None,
N=None, M=None, rotat=False,
custom_shape=None,
inertia_model=None,
dependencies=[]):
”’
Compute inertia tensors of atomic clusters from Voronoi polyhedra.Parameters
———-
…
dependencies : list[str]
List of module names needed by user-defined model.Returns
——-
tensor : 2D array(3*3)
Inertia tensorRaises
——
ImportError if any dependency is missing.
ValueError if required inputs are missing.
”’def import_dependencies(modules):
missing_modules = []
imported_modules = {}
try:
for module_name in modules:
imported_modules[module_name] = importlib.import_module(module_name)
except ImportError as e:
missing_modules.append(str(e))return imported_modules if not missing_modules else None
imported_modules = import_dependencies(dependencies)
if not imported_modules:
raise ImportError(f”Missing dependencies: {dependencies}”)if inertia_model:
try:
return inertia_model(ar=ar, cr=cr, N=N, M=M,
imported_modules=imported_modules)except Exception as e:
raise ValueError(f”Error executing user-defined model: {e}”)elif custom_shape:
# Existing logic for handling predefined/custom shapeselse:
# Existing logic for predefined types (‘hoop’, ‘circle’, ‘ellipse’)# Example usage with dependencies:
user_defined_model = lambda ar=0, cr=0,N=0,M=0,**kwargs: [[N*M*ar**i + cr**j] * 3] * 3tensor = compute_inertia(
inertia_model=user_defined_model,
dependencies=[‘numpy’] # Example dependency)
### Suggestions:
– Clearly document expected modules so users know what they need before execution.
– Handle exceptions gracefully during imports and provide informative error messages.
– Consider providing fallback mechanisms or warnings if certain non-critical dependencies are missing.This setup provides flexibility while ensuring robustness against missing dependencies! 🛠️✨
<>: Perfect! Now let’s think about error handling when these external libraries aren’t available at runtime. Maybe we could log these errors instead of just raising exceptions immediately? Also , should we consider caching successful imports somehow so we don’t repeatedly attempt importing already available modules? What do you think?
<>: pythonTo enhance error handling by logging errors instead of immediately raising exceptions and caching successful imports:
1. **Logging Setup**: Use Python’s `logging` module to log errors related to missing dependencies.
2. **Caching Imports**: Implement a simple caching mechanism using a dictionary to store successfully imported modules.
Here’s how you can integrate these features:
python
import importlib
import logging# Configure logging settings once at startup
logging.basicConfig(level=logging.INFO)# Cache dictionary
_import_cache = {}def compute_inertia(inertia_type=’hoop’, ar=None ,cr=None ,
N=None ,M=None ,rotat=False ,
custom_shape=None ,
inertia_model=None ,
dependencies=[]):def import_dependencies(modules):
imported_modules = {}
missing_modules = []try:
for module_name in modules:
if module_name not in _import_cache:
_import_cache[module_name] = importlib.import_module(module_name)
imported_modules[module_name] = _import_cache[module_name]except ImportError as e:
logging.error(f”Missing dependency ‘{module_name}’: {e}”)
missing_modules.append(module_name)return imported_modules if not missing_modules else None
imported_modules = import_dependencies(dependencies)
if not imported_modules:
logging.error(f”Execution halted due to missing dependencies.”)
return Noneif inertia_model:
try:
return inertia_model(ar=ar ,cr=cr ,N=N ,M=M ,
imported_modules=imported_modules )except Exception as e :
logging.error(f”Error executing user-defined model: {e}”)
return Noneelif custom_shape :
# Existing logicelse :
# Existing logic# Example usage
user_defined_model=lambda ar=0 ,cr=0,N=0,M=0,**kwargs:[[N*M*ar**i + cr**j]*3]*3tensor=computation_of_inertial_tensor(
inertial_calculation=user_defined_model ,
required_dependencies=[‘numpy’])
### Suggestions:
– Use logging levels appropriately (e.g., INFO for successful imports).
– Consider persistent caching strategies only if necessary; otherwise keep it simple within session scope.
– Provide clear documentation about error handling behavior so users know what logs mean.This approach ensures smooth execution while providing insights into issues without abrupt interruptions! 📋🔧
<>: Great suggestions! Logging will definitely help track issues without stopping everything immediately. I’ll integrate this into my codebase and see how it performs with different models and dependencies. Thanks for all the help!
<>: Sure thing! Integrating logging will give you better insights into any issues without halting execution abruptly. If you encounter any challenges while testing different models or managing dependencies feel free reach out again!Good luck with your implementation! 🚀<>: Hi there,
I’m trying out some advanced plotting techniques using matplotlib subplots along with color maps based on correlation coefficients between features extracted from two datasets X1_expanded_features_df_1st_order_pca_augments_combined_sorted_scaled_with_refinement.csv.gz’ (X)and X_pruned_features_df_1st_order_pca_augments_combined_sorted_scaled_with_refinement.csv.gz'(Y).
Here’s my code snippet:
python
fig=plt.figure(figsize=(8,len(Vizceral_data_01.columns.tolist())*.25),dpi=my_dpi)
axs=[]
for i,_f_ in enumerate(Vizceral_data_01.columns.tolist()):
axs.append(fig.add_subplot(len(Vizceral_data_01.columns.tolist()),1,i+1))
f_tuples=list(itertools.zip_longest(range_outliers,X_columns_outliers))
for f,tup_i in enumerate(f_tuples):
X=np.array(Vizceral_data_01.iloc[:,tup_i[1]]).reshape(-1)
y=np.array(Vizceral_data_02.iloc[:,tup_i[1]]).reshape(-1)
extent=[np.min(X), np.max(X), np.min(y),np.max(y)]
if hasattr(axs[f],”set_title”):
axs[f].set_title(tup_i[1])
my_ax=_scale_axes(axs[f],xmin=np.min(X),xmax=np.max(X),ymin=np.min(y),ymax=np.max(y))
#my_ax.set_position([position_list[f][0],position_list[f][1],position_list[f][2],position_list[f][3]])
my_ax.plot(X,y,’.’,ms=my_ms,c=my_color)
my_cb=axs[len((Vizceral_data_01.columns.tolist()))-1].get_figure().colorbar(my_ax.scatter(X,y,norm=my_norm,**my_scatter_kwargs))
my_cb.set_label(‘$mathregular{Spearman Correlation Coefficient}$’)
plt.savefig(‘Vizceral_corr.png”,bbox_inches=’tight’)
plt.show()However I’m running into an issue where I get an IndexError during execution:
plaintext
Traceback (most recent call last):
File “plot_script.py”, line 9,in
y=np.array(Vizceral_data_02.iloc[:,tup_i[1]]).reshape(-1)
IndexError Traceback (most recent call last)
:9,in
IndexError Traceback (most recent call last):
File “/path/to/script.py”, line x,in plot_script
y=np.array(Vizceral_data_02.iloc[:,tup_i[1]]).reshape(-1)
IndexError:index out-of-boundsI suspect something might be wrong with how I’m iterating over columns between datasets X and Y since they may not align perfectly after pruning operations earlier on but I’m really stuck here trying figure out why this index error keeps popping up…
Could someone help me figure out what might be going wrong here?
Thanks!
<>: python
It looks like you’re encountering an `IndexError` because there might be mismatched indices between `range_outliers`/`X_columns_outliers` tuples generated by `itertools.zip_longest()` and the columns available in `Vizceral_data_02`.Here are some steps you can take to troubleshoot this issue:
1. **Check Lengths:** Ensure that both datasets (`Vizceral_data_01` and `Vizceral_data_02`) have matching column lengths after pruning operations.
2. **Validate Indices:** Before accessing columns using indices from tuples (`tup_i[1]`), verify that these indices exist within both datasets’ column ranges.
3. **Handle Missing Values:** Since `itertools.zip_longest()` fills gaps with `None`, ensure that any such values are handled properly before attempting indexing operations.
4. **Debugging Prints:** Add print statements before accessing indices (`print(tup_i)`) inside your loop to check which index causes issues.
5. **Adjust Loop Logic:** Modify your loop logic so it only processes valid indices present in both datasets:
python
for f,tup_i in enumerate(f_tuples):
if tup_i[1] is not None and tup_i[1] < Vizceral_data_02.shape[1]:
X=np.array(Vizceral_data_01.iloc[:,tup_i[1]]).reshape(-1)
y=np.array(Vizceral_data_02.iloc[:,tup_i[1]]).reshape(-1)
extent=[np.min(X), np.max(X), np.min(y),np.max(y)]
if hasattr(axs[f],"set_title"):
axs[f].set_title(tup_i[1])
my_ax=_scale_axes(axs[f],xmin=np.min(X),xmax=np.max(X),ymin=np.min(y),ymax=np.max(y))
my_ax.plot(X,y,'.',ms=my_ms,c=my_color)
else:
print(f"Skipping index {tup_i} due to mismatch")By ensuring indices are valid before accessing data columns will prevent such errors from occurring again.
Let me know if this helps! 😊
<>: Alright, I see what you’re saying about checking indices before accessing them. But now I’m thinking about another issue—what happens when there are NaN values after reshaping X and y? Shouldn’t I handle those too? Maybe using something like numpy.nan_to_num() or filtering them out before plotting? How would you suggest integrating that into my current setup?
<>: Certainly! Handling NaN values is important before plotting data points since they can cause unexpected behavior during visualization.You can integrate NaN handling into your setup by either removing NaN values or replacing them using methods like `numpy.nan_to_num()`. Here’s how you can do it:
python
for f,tup_i in enumerate(f_tuples):
if tup_i[1] is not None and tup_i[1] 0 && len(y_cleaned) > 0:
extent=[np.min(X_cleaned), np.max(X_cleaned), np.min(y_cleaned), np.max(y_cleaned)]
if hasattr(axs[f],”set_title”):
axs[f].set_title(tup_i[1])
my_ax=_scale_axes(axs[f],xmin=np.min(X_cleaned),xmax=np.max(X_cleaned),
ymin=np.min(y_cleaned),ymax=np.max(y_cleaned))
my_ax.plot(X_cleaned,y_cleaned,’.’,ms=my_ms,c=my_color)
else:
print(f”Skipping index {tup_i} due to mismatch”)This approach ensures that only valid data points without NaN values are plotted:
– We create a mask array (`mask`) where both X and y have no NaN values.
– We apply this mask (`X[mask]`, `y[mask]`) before plotting.This should help maintain clean plots without NaN-related issues!
Let me know how it goes! 😊 <>: Hey there,
I’ve been working on implementing PCA-based feature extraction combined with LDA classification accuracy calculations across multiple folds using cross-validation techniques provided by scikit-learn pipelines integrated into TensorFlow EstimatorSpec outputs via placeholders/feeding dictionaries mapping strategy schemes/logic paths inside Keras/Tensorflow session run calls/loops construct(s).
The code snippet below is supposed set up cross-validation splits correctly but something seems off because I keep getting unexpected results during training/testing phases especially related dimension mismatches/errors during concatenation operations inside session runs loops/calls construct(s).
Could someone help me figure out what might be wrong here?
Buggy Code Snippet:
class MyModel(tf.estimator.Estimator):
def __init__(self):
super(MyModel).__init__()
self.pca_transformer_pipeline_stage_step_instance_placeholder_dict_obj={}
self.cross_folds_dict={}
self.kfold_obj=get_kfold()
self.num_folds=len(self.kfold_obj.split(self.X_train))def create_placeholders_and_feed_dicts_for_keras_tensors(self,X,Y,num_input_features,num_classes,num_dense_layers,batch_size,kernel_initializer,bias_initializer,dense_layer_hyperparameter_dict_obj,lstm_dropout_rate,lstm_recurrent_dropout_rate):
self.batch_size=batch_size
kfold_indices_train_arr=self.kfold_obj.split(self.X_train)[self.fold_counter]
kfold_indices_test_arr=self.kfold_obj.split(self.X_test)[self.fold_counter]
self.x_train_fold_placeholder=tf.placeholder(dtype=tf.float32,name=”x_train_fold”+str(self.fold_counter+10000))
self.y_train_fold_placeholder=tf.placeholder(dtype=tf.int32,name=”y_train_fold”+str(self.fold_counter+10000))
train_feed_dict_map={
self.x_train_fold_placeholder:self.X_train[kfold_indices_train_arr],
self.y_train_fold_placeholder:self.Y_train[kfold_indices_train_arr]
}self.x_test_fold_placeholder=tf.placeholder(dtype=tf.float32,name=”x_test_fold”+str(self.fold_counter+20000))
self.y_test_fold_placeholder=tf.placeholder(dtype=tf.int32,name=”y_test_fold”+str(self.fold_counter+20000))
test_feed_dict_map={
self.x_test_fold_placeholder:self.X_test[kfold_indices_test_arr],
self.y_test_fold_placeholder:self.Y_test[kfold_indices_test_arr]
}def build_graph(self):
tf.reset_default_graph()
num_dense_layers=num_dense_layers
dense_layer_hyperparameter_dict=dense_layer_hyperparameter_dict_obj
kernel_initializer=layers.kernel_initializer
bias_initializer=layers.bias_initializer
batch_size=batch_size
lstm_dropout_rate=lstm_dropout_rate
lstm_recurrent_dropout_rate=lstm_recurrent_dropout_rate
num_input_features=num_input_features
num_classes=num_classes
x=self.x_batch_plchld_holder
y_=self.y_batch_plchld_holder
W_fc_final=[]
b_fc_final=[]
h_fc_final=[]
h_pool_flat_final=[]
pred_y_final=[]
cross_entropy_final=[]
correct_prediction_final=[]
training_accuracy_final=[]
test_accuracy_final=[]
prediction_confidence_percentages_final=[]
for fold_counter,(train_indices,test_indices)in enumerate(kfold_obj.split(x)):
kernel_regularizer=layers.l regularization.l l l l l l l l l(l regu regu regu regu regu regu regu regu regu lar.l lar.l lar.l lar)(l)(l)(l)(l)(l)(l)(l)(l)(l)(l)(
bias_regularizer=layers.regularizer.regularizer.regularizer(regularization.regularization.regularization(l))(l)(
dropout_rate=dense_layer_hyperparameter_dict[“dropout_rate”][
num_hidden_units=dense_layer_hyperparameter_dict[“num_hidden_units”][
kernel_init_method=dense_layer_hyperparameter_dict[“kernel_init_method”][
bias_init_method=dense_layer_hyperparameter_dict[“bias_init_method”][self.create_placeholders_and_feed_dicts_for_keras_tensors(x,y_,num_input_features,num_classes,num_dense_layers,batch_size,kernel_initializer,bias_initializer,dense_layer_hyperparameter_dict,lstm_dropout_rate,lstm_recurrent_dropout_rate,)
with tf.name_scope(“dense_layers”)as scope_dense_layers_construct_ops_consolidation_region_creator_context_manager_object_instance_reference_variable_scope_handle_or_none_value_pair_object_tuple_struct_datatype_object_pair_struct_datatype_object_pair_struct_datatype_object_pair_struct_datatype_object_pair_struct_datatype_object_pair_struct_datatype_object_pair_struct_datatype_object_pair_struct_datatype_object_pair_struct_datatype_object_pair():
for hidden_unit_indexlayer_indexinrange(num_dense_layers):
with tf.variable_scope(“hiddenlayer_”+str(hidden_unit_indexlayer_index)+”/”):
W_fc_current=W_fc_previous[hidd en_unit_indexlayer_index]=tf.get_variable(name =”Wfc_”+ str(hidden_unit_indexlayer_index)+”hiddenlayer_”+str(hidden_unit_indexlayer_index)+”/”,initializer=
b_fc_current=b_fc_previous[hidd en_unit_indexlayer_index]=tf.get_variable(name =”bfc_”+ str(hidden_unit_indexlayer_index)+”hiddenlayer_”+str(hidden_unit_indexlayer_index)+”/”,initializer=
h_pool_flat_current=h_pool_flat_previous[hidd en_unit_indexlayer_index]=tf.nn.relu(tf.matmul(h_pool_flat_previous[hidd en_unit_ind ex)]+b_fc_current))
h_pool_flat_previous[hidd en_unit_ind ex)=h_pool_flat_current
W_fc_previous[hidd en_unit_ind ex)=W_fc_current
b_fc_previous[hidd en_unit_ind ex)=b_fc_currentTraceback/Error Message Output Observed During Execution Phase/Cycle/Loop Construct(s)/Call(s):
Traceback (most recent call last):
File “”, line XX,in build_graph():
File “”, line YY,in create_placeholders_and_feed_dicts_for_keras_tensors():
KeyError:’X_train’It seems like there might be some logical error causing misalignment/mismatching dimensions during concatenation operations inside session runs loops/calls construct(s) leading KeyError:X_train during feed dict mappings creation phase/cycle/loop construct(s)/call(s).
Any insights/suggestions?
Thanks!
