Simply train settings.

train.py

@@ -67,13 +67,23 @@ def train(data_type, seq_length, model, saved_model=None,
 def main():
     """These are the main training settings. Set each before running
     this file."""
-    data_type = 'features'  # can be 'features' or 'images'
-    seq_length = 40
     model = 'lstm'  # see `models.py` for more
-    class_limit = None  # int, can be 1-101 or None
     saved_model = None  # None or weights file
-    concat = False  # true for MLP only
-    image_shape = None  # Use None for default or specify a new size
+    class_limit = None  # int, can be 1-101 or None
+
+    # Chose images or features and image shape based on network.
+    if model == 'conv_3d' or model == 'crnn':
+        data_type = 'images'
+        image_shape = (80, 80, 3)
+    else:
+        data_type = 'features'
+        image_shape = None
+
+    # MLP requires flattened features.
+    if model == 'mlp':
+        concat = True
+    else:
+        concat = False
 
     train(data_type, seq_length, model, saved_model=saved_model,
           class_limit=class_limit, concat=concat, image_shape=image_shape)

validate_rnn.py

+"""
+Validate our RNN. Basically just runs a validation generator on
+about the same number of videos as we have in our test set.
+"""
+from keras.callbacks import TensorBoard, ModelCheckpoint, EarlyStopping, CSVLogger
+from models import ResearchModels
+from data import DataSet
+from PIL import Image
+import time
+
+def validate(data_type, model, seq_length=40, saved_model=None,
+          concat=False, class_limit=None, image_shape=None):
+    batch_size = 32
+
+    # Get the data and process it.
+    if image_shape is None:
+        data = DataSet(
+            seq_length=seq_length,
+            class_limit=class_limit
+        )
+    else:
+        data = DataSet(
+            seq_length=seq_length,
+            class_limit=class_limit,
+            image_shape=image_shape
+        )
+    
+    val_generator = data.frame_generator(batch_size, 'test', data_type, concat)
+
+    # Get the model.
+    rm = ResearchModels(len(data.classes), model, seq_length, saved_model)
+
+    # Evaluate!
+    results = rm.model.evaluate_generator(
+        generator=val_generator,
+        val_samples=3200)
+
+    print(results)
+    print(rm.model.metrics_names)
+
+def main():
+    model = 'mlp'
+    saved_model = 'data/ucf101/checkpoints/mlp-features.023-0.926.hdf5'
+
+    if model == 'conv_3d' or model == 'crnn':
+        data_type = 'images'
+        image_shape = (80, 80, 3)
+    else:
+        data_type = 'features'
+        image_shape = None
+
+    if model == 'mlp':
+        concat = True
+    else:
+        concat = False
+
+    validate(data_type, model, saved_model=saved_model,
+             concat=concat, image_shape=image_shape)
+
+if __name__ == '__main__':
+    main()