diff --git a/test_tensorflow_keras.py b/test_tensorflow_keras.py
new file mode 100644
index 0000000..1c89bee
--- /dev/null
+++ b/test_tensorflow_keras.py
@@ -0,0 +1,21 @@
+import tensorflow as tf
+
+from tensorflow.python.framework.ops import EagerTensor
+print("Eager Execution Enabled:", tf.executing_eagerly())
+print("Available Devices:")
+for device in tf.config.list_physical_devices():
+    print(device)
+
+tf.debugging.set_log_device_placement(False)
+
+cifar = tf.keras.datasets.cifar100
+(x_train, y_train), (x_test, y_test) = cifar.load_data()
+model = tf.keras.applications.ResNet50(
+    include_top=True,
+    weights=None,
+    input_shape=(32, 32, 3),
+    classes=100,)
+
+loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)
+model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
+model.fit(x_train, y_train, epochs=5, batch_size=64)
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diff --git a/test_tensorflow_regression.py b/test_tensorflow_regression.py
new file mode 100644
index 0000000..0f83a47
--- /dev/null
+++ b/test_tensorflow_regression.py
@@ -0,0 +1,107 @@
+import tensorflow as tf
+import numpy as np
+import optuna
+from sklearn.model_selection import KFold
+from datetime import datetime
+
+# Generate synthetic dataset
+np.random.seed(42)
+tf.random.set_seed(42)
+W_true, b_true = 0.5, 1.4
+X = np.linspace(0, 100, num=100)
+y = np.random.normal(loc=W_true * X + b_true, scale=2.0, size=len(X))
+now = datetime.now()
+logdir = "logs/regression"
+writer = tf.summary.create_file_writer(logdir)
+
+def objective(trial):
+    # Hyperparameters to tune
+    learning_rate = trial.suggest_float("learning_rate", 0.05, 0.075)
+    epochs = trial.suggest_int("epochs", 350, 450, step=10)
+    k_folds = trial.suggest_int("k_folds", 3, 7)  # Tune number of folds
+
+    # Cross-validation setup
+    kf = KFold(n_splits=k_folds, shuffle=True, random_state=42)
+    fold_losses = []
+
+    for fold, (train_index, val_index) in enumerate(kf.split(X)):
+        # Split into training and validation sets
+        X_train, X_val = X[train_index], X[val_index]
+        y_train, y_val = y[train_index], y[val_index]
+
+        # Define model parameters
+        weight = tf.Variable(tf.random.normal([1]), name="weight", dtype=tf.float32)
+        bias = tf.Variable(tf.random.normal([1]), name="bias", dtype=tf.float32)
+        optimizer = tf.optimizers.Adam(learning_rate=learning_rate)
+
+        # Training loop with early pruning
+        for epoch in range(epochs):
+            with tf.GradientTape() as tape:
+                y_pred = weight * X_train + bias
+                train_loss = tf.reduce_mean((y_pred - y_train) ** 2)
+            gradients = tape.gradient(train_loss, [weight, bias])
+            optimizer.apply_gradients(zip(gradients, [weight, bias]))
+
+            # Evaluate on validation set
+            y_val_pred = weight * X_val + bias
+            val_loss = tf.reduce_mean((y_val_pred - y_val) ** 2).numpy()
+
+            # Report to Optuna for pruning
+            #if epoch % 10 == 0:  # Reduce frequency of reporting (optional)
+            #    trial.report(val_loss, step=epoch)
+            #   if trial.should_prune():
+            #       raise optuna.TrialPruned()
+
+        fold_losses.append(val_loss)
+
+    # Return the average validation loss across folds
+    return np.mean(fold_losses)
+
+
+# Create an Optuna study with k_folds optimization
+pruner = optuna.pruners.HyperbandPruner()
+study = optuna.create_study(direction="minimize", pruner=pruner, sampler=optuna.samplers.TPESampler())
+study.optimize(objective, n_trials=5, show_progress_bar=True, n_jobs=1)
+
+# Print best hyperparameters
+print("Best hyperparameters:")
+print(study.best_params)
+
+# Train the final model with best hyperparameters
+best_learning_rate = study.best_params["learning_rate"]
+best_epochs = study.best_params["epochs"]
+best_k_folds = study.best_params["k_folds"]
+
+kf = KFold(n_splits=best_k_folds, shuffle=True, random_state=42)
+final_losses = []
+
+for fold, (train_index, val_index) in enumerate(kf.split(X)):
+    X_train, X_val = X[train_index], X[val_index]
+    y_train, y_val = y[train_index], y[val_index]
+
+    weight = tf.Variable(tf.random.normal([1]), name="weight", dtype=tf.float32)
+    bias = tf.Variable(tf.random.normal([1]), name="bias", dtype=tf.float32)
+    optimizer = tf.optimizers.Adam(learning_rate=best_learning_rate)
+
+    for epoch in range(best_epochs):
+        with tf.GradientTape() as tape:
+            y_pred = weight * X_train + bias
+            train_loss = tf.reduce_mean((y_pred - y_train) ** 2)
+        gradients = tape.gradient(train_loss, [weight, bias])
+        optimizer.apply_gradients(zip(gradients, [weight, bias]))
+
+        with writer.as_default():
+            # Include the fold index in the step parameter
+            step = epoch + (fold * best_epochs)
+            tf.summary.scalar('MSE Loss', train_loss, step=step)
+            tf.summary.histogram('Model Weight', weight, step=step)
+            tf.summary.histogram('Model Bias', bias, step=step)
+
+    # Evaluate final model on validation set
+    y_val_pred = weight * X_val + bias
+    val_loss = tf.reduce_mean((y_val_pred - y_val) ** 2).numpy()
+    final_losses.append(val_loss)
+
+# Print final results
+print(f"Final Validation Loss (avg across folds): {np.mean(final_losses)}")
+print(f"Final Weight: {weight.numpy()[0]}, Final Bias: {bias.numpy()[0]}")
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