diff --git a/cvdm/utils/metrics_utils.py b/cvdm/utils/metrics_utils.py
deleted file mode 100644
index 5e9ce81..0000000
--- a/cvdm/utils/metrics_utils.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from typing import Dict, Optional
-
-import numpy as np
-from skimage.metrics import peak_signal_noise_ratio, structural_similarity
-
-
-def nmae(y_pred: np.ndarray, y_real: np.ndarray) -> float:
-    nmae: float = np.sqrt(np.sum((y_pred - y_real) ** 2)) / np.sqrt(np.sum(y_real**2))
-    return nmae
-
-
-def calculate_metrics(
-    y_pred_batch: np.ndarray,
-    y_real_batch: np.ndarray,
-) -> Dict[str, float]:
-    y_pred_batch = np.array(y_pred_batch)
-    y_real_batch = np.array(y_real_batch)
-
-    if y_pred_batch.shape[3] > 1:
-        channel_axis = 2
-    else:
-        channel_axis = None
-
-    metrics = {
-        "mse": np.mean((y_pred_batch - y_real_batch) ** 2),
-        "mape": np.mean(np.abs((y_real_batch - y_pred_batch) / y_real_batch + 1e-10))
-        * 100,
-        "nmae": np.mean(
-            [
-                np.mean(np.abs(y_pred - y_real)) / np.mean(np.abs(y_real) + 1e-10)
-                for y_pred, y_real in zip(y_pred_batch, y_real_batch)
-            ]
-        ),
-        "psnr": np.mean(
-            [
-                peak_signal_noise_ratio(
-                    np.squeeze(y_pred), np.squeeze(y_real), data_range=2
-                )
-                for y_pred, y_real in zip(y_pred_batch, y_real_batch)
-            ]
-        ),
-        "ssim": np.mean(
-            [
-                structural_similarity(
-                    np.squeeze(y_pred),
-                    np.squeeze(y_real),
-                    data_range=2,
-                    channel_axis=channel_axis,
-                )
-                for y_pred, y_real in zip(y_pred_batch, y_real_batch)
-            ]
-        ),
-    }
-
-    return metrics
diff --git a/scripts/eval.py b/eval.py
similarity index 100%
rename from scripts/eval.py
rename to eval.py
diff --git a/scripts/train.py b/train.py
similarity index 73%
rename from scripts/train.py
rename to train.py
index d94b85a..779e39f 100644
--- a/scripts/train.py
+++ b/train.py
@@ -67,16 +67,11 @@ def main() -> None:
     print("Getting data...")
     batch_size = data_config.batch_size
     dataset, x_shape, y_shape = prepare_dataset(task, data_config, training=True)
-    val_dataset, x_shape, y_shape = prepare_dataset(task, data_config, training=False)
-
     dataset = dataset.shuffle(5000, reshuffle_each_iteration=False)
-
     val_len = eval_config.val_len
-    val_dataset = val_dataset.take(val_len)
     dataset = dataset.skip(val_len)
 
     dataset = dataset.batch(batch_size, drop_remainder=True)
-    val_dataset = val_dataset.batch(batch_size, drop_remainder=True)
 
     epochs = training_config.epochs
     generation_timesteps = eval_config.generation_timesteps
@@ -107,7 +102,6 @@ def main() -> None:
     log_freq = eval_config.log_freq
     checkpoint_freq = eval_config.checkpoint_freq
     image_freq = eval_config.image_freq
-    val_freq = eval_config.val_freq
     output_path = eval_config.output_path
     diff_inp = model_config.diff_inp
 
@@ -161,46 +155,6 @@ def main() -> None:
                     prefix="train",
                     cmap=cmap,
                 )
-
-            if step % val_freq == 0:
-                if model_config.zmd:
-                    val_loss = np.zeros(6)
-                else:
-                    val_loss = np.zeros(5)
-                for batch in val_dataset:
-                    batch_x, batch_y = batch
-                    model_input = prepare_model_input(
-                        batch_x, batch_y, diff_inp=diff_inp
-                    )
-                    val_loss += joint_model.evaluate(
-                        model_input, np.zeros_like(batch_y), verbose=0
-                    )
-
-                log_loss(run=run, avg_loss=val_loss, prefix="val")
-                # To speed up, images are only generated and metrics are calculated only for one batch.
-                random_batch = val_dataset.take(1)
-                for batch_x, batch_y in random_batch:
-                    output_montage, metrics = obtain_output_montage_and_metrics(
-                        batch_x,
-                        batch_y.numpy(),
-                        noise_model,
-                        schedule_model,
-                        mu_model,
-                        generation_timesteps,
-                        diff_inp,
-                        task,
-                    )
-                    log_metrics(run, metrics, prefix="val")
-                    save_output_montage(
-                        run=run,
-                        output_montage=output_montage,
-                        step=step,
-                        output_path=output_path,
-                        run_id=run_id,
-                        prefix="val",
-                        cmap=cmap,
-                    )
-
             step += 1
 
     if run is not None: