diff --git a/ptychonn/_infer/__main__.py b/ptychonn/_infer/__main__.py
index 1d23b95..8127495 100644
--- a/ptychonn/_infer/__main__.py
+++ b/ptychonn/_infer/__main__.py
@@ -211,7 +211,7 @@ def infer(
     inferences : (POSITION, 2, WIDTH, HEIGHT)
         The reconstructed patches inferred by the model.
     '''
-    model.eval()
+    model = model.eval().to("cuda")
     result = []
     with torch.no_grad():
         for batch in data:
diff --git a/ptychonn/_train/__main__.py b/ptychonn/_train/__main__.py
index c0e0b70..92d884d 100644
--- a/ptychonn/_train/__main__.py
+++ b/ptychonn/_train/__main__.py
@@ -153,6 +153,7 @@ def train(
     epochs: int = 1,
     batch_size: int = 32,
     training_fraction: float = 0.8,
+    log_frequency: int = 50,
 ) -> typing.Tuple[lightning.Trainer, lightning.pytorch.loggers.CSVLogger | ListLogger]:
     """Train a PtychoNN model.
 
@@ -181,7 +182,17 @@ def train(
         The size of one training batch.
     training_fraction
         The proprotion of X_train and Y_train that is used for training.
+    log_frequency
+        Write to the logs every this number of steps
     """
+    if batch_size <= 0:
+        msg = f"Number of batches must be positive not f{batch_size}"
+        raise ValueError(msg)
+    if epochs <= 0:
+        msg = f"Number of epochs must be positive or zero not f{epochs}"
+        raise ValueError(msg)
+    # X_train, Y_train and training_fraction checked in create_training_dataloader
+
     if out_dir is not None:
         checkpoint_callback = lightning.pytorch.callbacks.ModelCheckpoint(
             dirpath=out_dir,
@@ -207,6 +218,7 @@ def train(
         callbacks=None if out_dir is None else [checkpoint_callback],
         logger=logger,
         enable_checkpointing=False if out_dir is None else True,
+        log_every_n_steps=log_frequency,
     )
 
     train_dataloader, val_dataloader = create_training_dataloader(
@@ -259,13 +271,13 @@ def create_training_dataloader(
     assert X_train.dtype == np.float32
     assert np.all(np.isfinite(X_train))
 
-    if X_train.ndim != 3:
+    if X_train.ndim != 3 or X_train.shape[0] < 1:
         msg = (
-            "X_train must have 3 dimemnsions: (N, WIDTH, HEIGHT); "
+            "X_train must have 3 dimensions: (N, WIDTH, HEIGHT); "
             f" not {X_train.shape}"
         )
         raise ValueError(msg)
-    if Y_train.ndim != 4:
+    if Y_train.ndim != 4 or Y_train.shape[0] < 1 or Y_train.shape[1] not in [1, 2]:
         msg = (
             f"Y_train must have 4 dimensions: (N, [1,2], WIDTH, HEIGHT); "
             f"not {Y_train.shape}"
@@ -292,6 +304,15 @@ def create_training_dataloader(
         [training_fraction, 1.0 - training_fraction],
     )
 
+    if len(training) // batch_size <= 0:
+        msg = ("The training dataset is smaller than one batch. "
+        "Adjust the batch size so there is training data.")
+        raise ValueError(msg)
+    if len(validation) // batch_size <= 0:
+        msg = ("The validation dataset is smaller than one batch. "
+        "Adjust the batch_size so there is validation data.")
+        raise ValueError(msg)
+
     trainingloader = torch.utils.data.DataLoader(
         training,
         batch_size=batch_size,