My code has an issue identifying the search space in grid search.

[jobmart]

"I am working on the backend code of my Jupyter Notebook, and I'm encountering an issue when running the model. Here's the code:
`# Imports
import numpy as np
import pandas as pd
from scipy import stats

Sklearn

from sklearn.metrics import r2_score
from ML.ml_utils import *
from sklearn.model_selection import train_test_split

FNN

import tensorflow as tf
import keras_tuner as kt
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, BatchNormalization
from tensorflow.keras.optimizers import SGD, Adam
from tensorflow.keras.callbacks import EarlyStopping
from keras_tuner import HyperParameters
from keras_tuner import Objective
from keras_tuner.tuners import GridSearch, RandomSearch
from your_module import create_model

FeedForwardNN and related classes

class FeedForwardNN(tf.keras.Model):
def init(self, input_dim=None, random_seed=42):
super(FeedForwardNN, self).init()
self.seed = random_seed
self.input_dim = 2048
self.model = self.build_model()

def build_model(self, hp=None):
    if hp is None:
        hp = kt.HyperParameters()
    tf.random.set_seed(self.seed)

    # Define hyperparameters
    optimizer_c = hp.Choice("optimizer", ['SGD', 'Adam'])
    learning_rate = hp.Float("learning_rate", min_value=0.00001, max_value=0.1, sampling='log')
    l2_reg = hp.Float("l2_reg", min_value=0.0001, max_value=0.1, sampling='log')
    dropout_rate = hp.Float("dropout_rate", min_value=0, max_value=0.5, step=0.1)
    output_dim = hp.Fixed("output_dim", value=1)

    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Input(shape=(self.input_dim,)))
    model.add(tf.keras.layers.Dense(units=hp.Int("units_1", min_value=32, max_value=256, step=32),
                                    activation='relu',
                                    kernel_regularizer=tf.keras.regularizers.l2(l2_reg)))

    for i in range(hp.Int("num_layers", 1, 3)):
        model.add(tf.keras.layers.Dense(units=hp.Int(f"units_{i}", min_value=32, max_value=256, step=32),
                                        activation='relu',
                                        kernel_regularizer=tf.keras.regularizers.l2(l2_reg)))

    if optimizer_c == "SGD":
        optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate)
    elif optimizer_c == "Adam":
        optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
    else:
        raise ValueError("Unsupported optimizer")

    model.add(tf.keras.layers.Dropout(dropout_rate))
    model.add(tf.keras.layers.Dense(output_dim, activation='linear'))

    model.compile(loss=tf.keras.losses.mean_squared_error,
                  optimizer=optimizer,
                  metrics=['mean_absolute_error'])
    return model

def call(self, inputs, training=False):
    return self.model(inputs, training=training)

def fit(self, X, y, *args, **kwargs):
    return self.model.fit(X, y, *args, shuffle=True, **kwargs)

When I attempt to run the hyperparameter search with this setup, the search_space_summary() displays the following output:Search space summary
Default search space size: 0
`
As a result, the hyperparameter search doesn't execute. I suspect the issue is related to the hp=kt.HyperParameters configuration, but I have tried multiple approaches without success.

Does anyone have suggestions for how to correct this and enable proper hyperparameter tuning?"

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