pnb app changes

run_pnb.py

@@ -17,9 +17,10 @@ import tensorflow.keras as keras
 from sparse_coding_torch.keras_model import SparseCode, PNBClassifier, PTXClassifier, ReconSparse
 import glob
 
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument('--input_dir', default='/shared_data/bamc_pnb_data/full_training_data', type=str)
+    parser.add_argument('--input_dir', default='/shared_data/bamc_pnb_data/revised_training_data', type=str)
     parser.add_argument('--kernel_size', default=15, type=int)
     parser.add_argument('--kernel_depth', default=5, type=int)
     parser.add_argument('--num_kernels', default=48, type=int)
@@ -28,7 +29,7 @@ if __name__ == "__main__":
     parser.add_argument('--activation_lr', default=1e-2, type=float)
     parser.add_argument('--lam', default=0.05, type=float)
     parser.add_argument('--sparse_checkpoint', default='sparse_coding_torch/output/sparse_pnb_48/sparse_conv3d_model-best.pt/', type=str)
-    parser.add_argument('--checkpoint', default='sparse_coding_torch/classifier_outputs/48_filters_6/best_classifier.pt/', type=str)
+    parser.add_argument('--checkpoint', default='sparse_coding_torch/classifier_outputs/48_filters_grouped/best_classifier.pt/', type=str)
     parser.add_argument('--run_2d', action='store_true')
     
     args = parser.parse_args()
@@ -37,6 +38,7 @@ if __name__ == "__main__":
 
     image_height = 285
     image_width = 235
+    clip_depth = 5
 
     if args.run_2d:
         inputs = keras.Input(shape=(image_height, image_width, 5))
@@ -45,7 +47,7 @@ if __name__ == "__main__":
 
     filter_inputs = keras.Input(shape=(5, args.kernel_size, args.kernel_size, 1, args.num_kernels), dtype='float32')
 
-    output = SparseCode(batch_size=batch_size, image_height=image_height, image_width=image_width, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d, padding='VALID')(inputs, filter_inputs)
+    output = SparseCode(batch_size=batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d, padding='VALID')(inputs, filter_inputs)
 
     sparse_model = keras.Model(inputs=(inputs, filter_inputs), outputs=output)
 
@@ -58,7 +60,7 @@ if __name__ == "__main__":
     transform = torchvision.transforms.Compose(
     [VideoGrayScaler(),
      MinMaxScaler(0, 255),
-     torchvision.transforms.Resize((285, 235))
+     torchvision.transforms.Resize((image_height, image_width))
     ])
 
     all_predictions = []
@@ -71,7 +73,9 @@ if __name__ == "__main__":
         vc = tv.io.read_video(f)[0].permute(3, 0, 1, 2)
         is_right = classify_nerve_is_right(yolo_model, vc)
         
-        vc_sub = vc[:, -5:, :, :]
+        all_preds = []
+        for i in range(1, 5):
+            vc_sub = vc[:, -5*(i+1):-5*i, :, :]
             if vc_sub.size(1) < 5:
                 print(f + ' does not contain enough frames for processing')
                 continue
@@ -79,13 +83,8 @@ if __name__ == "__main__":
             ### START time after loading video ###
             start_time = time.time()
             clip = None
-        i = 1
-        while not clip and i < 5:
-            if vc_sub.size(1) < 5:
-                break
-            clip = get_yolo_regions(yolo_model, vc_sub, is_right)
-            vc_sub = vc[:, -5*(i+1):-5*i, :, :]
-            i += 1
+        
+            clip = get_yolo_regions(yolo_model, vc_sub, is_right, crop_width=image_width, crop_height=image_height)
             
             if clip:
                 clip = clip[0]
@@ -96,14 +95,15 @@ if __name__ == "__main__":
 
                 pred = tf.math.sigmoid(classifier_model(activations))
                 
-            final_pred = tf.math.round(pred)
+                all_preds.append(pred)
                 
+        if all_preds:
+            final_pred = np.round(np.mean(np.array(all_preds)))
             if final_pred == 1:
                 str_pred = 'Positive'
             else:
                 str_pred = 'Negative'
         else:
-            print('here')
             str_pred = "Positive"
 
         end_time = time.time()

run_pnb_old.py

+import torch
+import os
+from sparse_coding_torch.keras_model import SparseCode, PNBClassifier, PTXClassifier, ReconSparse
+import time
+import numpy as np
+import torchvision
+from sparse_coding_torch.video_loader import VideoGrayScaler, MinMaxScaler
+from torchvision.datasets.video_utils import VideoClips
+import csv
+from datetime import datetime
+from yolov4.get_bounding_boxes import YoloModel
+import argparse
+import tensorflow as tf
+import tensorflow.keras as keras
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input_dir', default='/shared_data/bamc_data/PTX_Sliding', type=str)
+    parser.add_argument('--kernel_size', default=15, type=int)
+    parser.add_argument('--kernel_depth', default=5, type=int)
+    parser.add_argument('--num_kernels', default=64, type=int)
+    parser.add_argument('--stride', default=2, type=int)
+    parser.add_argument('--max_activation_iter', default=100, type=int)
+    parser.add_argument('--activation_lr', default=1e-2, type=float)
+    parser.add_argument('--lam', default=0.05, type=float)
+    parser.add_argument('--sparse_checkpoint', default='converted_checkpoints/sparse.pt', type=str)
+    parser.add_argument('--checkpoint', default='converted_checkpoints/classifier.pt', type=str)
+    parser.add_argument('--run_2d', action='store_true')
+    parser.add_argument('--dataset', default='ptx', type=str)
+    
+    args = parser.parse_args()
+    #print(args.accumulate(args.integers))
+    batch_size = 1
+    
+    if args.dataset == 'pnb':
+        image_height = 250
+        image_width = 600
+    elif args.dataset == 'ptx':
+        image_height = 100
+        image_width = 200
+    else:
+        raise Exception('Invalid dataset')
+
+    if args.run_2d:
+        inputs = keras.Input(shape=(image_height, image_width, 5))
+    else:
+        inputs = keras.Input(shape=(5, image_height, image_width, 1))
+
+    filter_inputs = keras.Input(shape=(5, args.kernel_size, args.kernel_size, 1, args.num_kernels), dtype='float32')
+
+    output = SparseCode(batch_size=batch_size, image_height=image_height, image_width=image_width, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d, padding='SAME')(inputs, filter_inputs)
+
+    sparse_model = keras.Model(inputs=(inputs, filter_inputs), outputs=output)
+    
+    recon_inputs = keras.Input(shape=(1, image_height // args.stride, image_width // args.stride, args.num_kernels))
+    
+    recon_outputs = ReconSparse(batch_size=batch_size, image_height=image_height, image_width=image_width, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(recon_inputs)
+    
+    recon_model = keras.Model(inputs=recon_inputs, outputs=recon_outputs)
+
+    if args.sparse_checkpoint:
+        recon_model = keras.models.load_model(args.sparse_checkpoint)
+        
+    if args.checkpoint:
+        classifier_model = keras.models.load_model(args.checkpoint)
+    else:
+        classifier_inputs = keras.Input(shape=(1, image_height // args.stride, image_width // args.stride, args.num_kernels))
+
+        if args.dataset == 'pnb':
+            classifier_outputs = PNBClassifier()(classifier_inputs)
+        elif args.dataset == 'ptx':
+            classifier_outputs = PTXClassifier()(classifier_inputs)
+        else:
+            raise Exception('No classifier exists for that dataset')
+
+        classifier_model = keras.Model(inputs=classifier_inputs, outputs=classifier_outputs)
+        
+    yolo_model = YoloModel()
+        
+    transform = torchvision.transforms.Compose(
+    [VideoGrayScaler(),
+     MinMaxScaler(0, 255),
+     torchvision.transforms.Normalize((0.2592,), (0.1251,)),
+     torchvision.transforms.CenterCrop((100, 200))
+    ])
+    
+    all_predictions = []
+    
+    all_files = list(os.listdir(args.input_dir))
+    
+    for f in all_files:
+        print('Processing', f)
+        #start_time = time.time()
+        
+        clipstride = 15
+        
+        vc = VideoClips([os.path.join(args.input_dir, f)],
+                        clip_length_in_frames=5,
+                        frame_rate=20,
+                       frames_between_clips=clipstride)
+    
+        ### START time after loading video ###
+        start_time = time.time()
+        clip_predictions = []
+        i = 0
+        cliplist = []
+        countclips = 0
+        for i in range(vc.num_clips()):
+
+            clip, _, _, _ = vc.get_clip(i)
+            clip = clip.swapaxes(1, 3).swapaxes(0, 1).swapaxes(2, 3).numpy()
+            
+            bounding_boxes, classes = yolo_model.get_bounding_boxes(clip[:, 2, :, :].swapaxes(0, 2).swapaxes(0, 1))
+            bounding_boxes = bounding_boxes.squeeze(0)
+            if bounding_boxes.size == 0:
+                continue
+            #widths = []
+            countclips = countclips + len(bounding_boxes)
+            
+            widths = [(bounding_boxes[i][3] - bounding_boxes[i][1]) for i in range(len(bounding_boxes))]
+            
+            #for i in range(len(bounding_boxes)):
+            #    widths.append(bounding_boxes[i][3] - bounding_boxes[i][1])
+
+            ind =  np.argmax(np.array(widths))
+            #for bb in bounding_boxes:
+            bb = bounding_boxes[ind]
+            center_x = (bb[3] + bb[1]) / 2 * 1920
+            center_y = (bb[2] + bb[0]) / 2 * 1080
+
+            width=400
+            height=400
+
+            lower_y = round(center_y - height / 2)
+            upper_y = round(center_y + height / 2)
+            lower_x = round(center_x - width / 2)
+            upper_x = round(center_x + width / 2)
+
+            trimmed_clip = clip[:, :, lower_y:upper_y, lower_x:upper_x]
+
+            trimmed_clip = torch.tensor(trimmed_clip).to(torch.float)
+
+            trimmed_clip = transform(trimmed_clip)
+            trimmed_clip.pin_memory()
+            cliplist.append(trimmed_clip)
+
+        if len(cliplist) > 0:
+            with torch.no_grad():
+                trimmed_clip = torch.stack(cliplist)
+                images = trimmed_clip.permute(0, 2, 3, 4, 1).numpy()
+                activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.weights[0], axis=0))]))
+
+                pred = classifier_model(activations)
+                #print(torch.nn.Sigmoid()(pred))
+                clip_predictions = tf.math.round(tf.math.sigmoid(pred))
+
+            final_pred = torch.mode(torch.tensor(clip_predictions.numpy()).view(-1))[0].item()
+            if len(clip_predictions) % 2 == 0 and tf.math.reduce_sum(clip_predictions) == len(clip_predictions)//2:
+                #print("I'm here")
+                final_pred = torch.mode(torch.tensor(clip_predictions.numpy()).view(-1))[0].item()
+                
+            if final_pred == 1:
+                str_pred = 'No Sliding'
+            else:
+                str_pred = 'Sliding'
+
+        else:
+            str_pred = "No Sliding"
+            
+        print(str_pred)
+            
+        end_time = time.time()
+        
+        all_predictions.append({'FileName': f, 'Prediction': str_pred, 'TotalTimeSec': end_time - start_time})
+        
+    with open('output_' + datetime.now().strftime("%Y%m%d-%H%M%S") + '.csv', 'w+', newline='') as csv_out:
+        writer = csv.DictWriter(csv_out, fieldnames=all_predictions[0].keys())
+        
+        writer.writeheader()
+        writer.writerows(all_predictions)

run_pnb_only_yolo.py

+import os
+import time
+import numpy as np
+from yolov4.get_bounding_boxes import YoloModel
+import argparse
+import glob
+from sklearn.linear_model import LogisticRegression
+import torchvision as tv
+from tqdm import tqdm
+import random
+
+def calc_yolo_dist(yolo_model, frame):
+    bounding_boxes, classes = yolo_model.get_bounding_boxes(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
+    bounding_boxes = bounding_boxes.squeeze(0)
+    classes = classes.squeeze(0)
+    
+    needle = None
+    nerve = None
+
+    for bb, class_pred in zip(bounding_boxes, classes):
+        if class_pred == 0 and nerve is None:
+            nerve = np.array(bb)
+        elif class_pred == 2 and needle is None:
+            needle = np.array(bb)
+            
+    if nerve is None or needle is None:
+        return None
+
+    return np.concatenate([nerve, needle])
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input_dir', default='/shared_data/bamc_pnb_data/revised_training_data', type=str)
+    
+    args = parser.parse_args()
+        
+    yolo_model = YoloModel()
+
+    all_predictions = []
+
+    all_files = glob.glob(pathname=os.path.join(args.input_dir, '**', '*.mp4'), recursive=True)
+    
+    all_data = []
+    
+    print('Building dataset...')
+
+    for f in tqdm(all_files):
+        vc = tv.io.read_video(f)[0].permute(3, 0, 1, 2)
+        
+        x = None
+        i = -1
+        while x is None and i >= -5:
+            x = calc_yolo_dist(yolo_model, vc[:, i, :, :])
+            i -= 1
+            
+        if x is None:
+            continue
+            
+        y = 1 if f.split('/')[-3] == 'Positives' else 0
+        
+        all_data.append((x, y))
+        
+    random.shuffle(all_data)
+    
+    split = int(len(all_data) * 0.8)
+    
+    train_data = all_data[:split]
+    test_data = all_data[split:]
+    
+    print('Loaded {} train examples.'.format(len(train_data)))
+    print('Loaded {} test examples.'.format(len(test_data)))
+    
+    train_x = [ex[0] for ex in train_data]
+    train_y = [ex[1] for ex in train_data]
+    
+    test_x = [ex[0] for ex in test_data]
+    test_y = [ex[1] for ex in test_data]
+    
+    print('Fitting to data...')
+    
+    clf = LogisticRegression(random_state=0, max_iter=1000).fit(train_x, train_y)
+    
+    print('Evaluating model...')
+    
+    score = clf.score(test_x, test_y)
+    
+    print(score)
\ No newline at end of file

run_tflite_pnb.py

@@ -19,7 +19,10 @@ if __name__ == "__main__":
 
     parser = argparse.ArgumentParser(description='Python program for processing PNB data')
     parser.add_argument('--classifier', type=str, default='sparse_coding_torch/mobile_output/pnb.tflite')
-    parser.add_argument('--input_dir', default='/shared_data/bamc_pnb_data/full_training_data', type=str)
+    parser.add_argument('--input_dir', default='/shared_data/bamc_pnb_data/revised_training_data', type=str)
+    parser.add_argument('--stride', default=30, type=int)
+    parser.add_argument('--image_width', default=400, type=int)
+    parser.add_argument('--image_height', default=285, type=int)
     args = parser.parse_args()
 
     interpreter = tf.lite.Interpreter(args.classifier)
@@ -31,9 +34,9 @@ if __name__ == "__main__":
     yolo_model = YoloModel()
 
     transform = torchvision.transforms.Compose(
-    [#VideoGrayScaler(),
-     MinMaxScaler(0, 255),
-#      torchvision.transforms.Resize((285, 235))
+    [VideoGrayScaler(),
+#      MinMaxScaler(0, 255),
+     torchvision.transforms.Resize((args.image_height, args.image_width))
     ])
 
     all_predictions = []
@@ -46,24 +49,22 @@ if __name__ == "__main__":
         vc = tv.io.read_video(f)[0].permute(3, 0, 1, 2)
         is_right = classify_nerve_is_right(yolo_model, vc)
         
-        vc_sub = vc[:, -5:, :, :]
+        all_preds = []
+        
+        for j in range(0, vc.size(1) - 5, args.stride):
+            vc_sub = vc[:, j:j+5, :, :]
+            
             if vc_sub.size(1) < 5:
-            print(f + ' does not contain enough frames for processing')
                 continue
             
             ### START time after loading video ###
             start_time = time.time()
             
-        clip = None
-        i = 1
-        while not clip and i < 5:
-            if vc_sub.size(1) < 5:
-                break
-            clip = get_yolo_regions(yolo_model, vc_sub, is_right)
-            vc_sub = vc[:, -5*(i+1):-5*i, :, :]
-            i += 1
+            clip = get_yolo_regions(yolo_model, vc_sub, is_right, args.image_width, args.image_height)
+            
+            if not clip:
+                continue
 
-        if clip:
             clip = clip[0]
             clip = transform(clip).to(torch.float32)
 
@@ -74,11 +75,18 @@ if __name__ == "__main__":
             output_array = np.array(interpreter.get_tensor(output_details[0]['index']))
 
             pred = output_array[0][0]
-            print(pred)
 
             final_pred = pred.round()
             
-            if final_pred == 1:
+            all_preds.append(final_pred)
+            
+        print(all_preds)
+            
+            
+        if all_preds[-5:-2]:
+            video_pred = np.round(sum(all_preds[-5:-2]) / len(all_preds[-5:-2]))
+
+            if video_pred == 1:
                 str_pred = 'Positive'
             else:
                 str_pred = 'Negative'

sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/arguments.txt

+Namespace(batch_size=24, kernel_size=15, kernel_depth=5, num_kernels=32, stride=4, max_activation_iter=150, activation_lr=0.01, lr=0.003, epochs=20, lam=0.05, output_dir='sparse_coding_torch/classifier_outputs/32_filters_no_aug_9', sparse_checkpoint='sparse_coding_torch/output/sparse_pnb_32/sparse_conv3d_model-best.pt/', checkpoint=None, splits='k_fold', seed=26, train=True, num_positives=100, n_splits=5, save_train_test_splits=False, run_2d=False, balance_classes=False, dataset='pnb', train_sparse=False, mixing_ratio=1.0, sparse_lr=0.003, crop_height=285, crop_width=400, scale_factor=1, clip_depth=5, frames_to_skip=1)
\ No newline at end of file

sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_classifier_0.pt/keras_metadata.pb

+
+root"_tf_keras_network*{"name": "model_2", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Functional", "config": {"name": "model_2", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_4"}, "name": "input_4", "inbound_nodes": []}, {"class_name": "PNBClassifier", "config": {"name": "pnb_classifier", "trainable": true, "dtype": "float32"}, "name": "pnb_classifier", "inbound_nodes": [[["input_4", 0, 0, {}]]]}], "input_layers": [["input_4", 0, 0]], "output_layers": [["pnb_classifier", 0, 0]]}, "shared_object_id": 2, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "ndim": 5, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", "input_4"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", "input_4"]}, "keras_version": "2.8.0", "backend": "tensorflow", "model_config": {"class_name": "Functional", "config": {"name": "model_2", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_4"}, "name": "input_4", "inbound_nodes": [], "shared_object_id": 0}, {"class_name": "PNBClassifier", "config": {"name": "pnb_classifier", "trainable": true, "dtype": "float32"}, "name": "pnb_classifier", "inbound_nodes": [[["input_4", 0, 0, {}]]], "shared_object_id": 1}], "input_layers": [["input_4", 0, 0]], "output_layers": [["pnb_classifier", 0, 0]]}}}2

+
root.layer-0"_tf_keras_input_layer*{"class_name": "InputLayer", "name": "input_4", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_4"}}2

+root.layer_with_weights-0"_tf_keras_layer*{"name": "pnb_classifier", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "PNBClassifier", "config": {"name": "pnb_classifier", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["input_4", 0, 0, {}]]], "shared_object_id": 1}2

+	 root.layer_with_weights-0.conv_1"_tf_keras_layer*	{"name": "conv2d", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [8, 8]}, "strides": {"class_name": "__tuple__", "items": [4, 4]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 4}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 5}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 6, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 32}}, "shared_object_id": 7}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 68, 97, 32]}}2

+	 root.layer_with_weights-0.conv_2"_tf_keras_layer*	{"name": "conv2d_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [4, 4]}, "strides": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 8}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 9}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 10, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 32}}, "shared_object_id": 11}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 23, 32]}}2

+
!root.layer_with_weights-0.flatten"_tf_keras_layer*{"name": "flatten", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "shared_object_id": 12, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 1, "axes": {}}, "shared_object_id": 13}}2

+root.layer_with_weights-0.ff_2"_tf_keras_layer*{"name": "dense", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 40, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 14}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 15}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 16, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 2240}}, "shared_object_id": 17}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 2240]}}2

+root.layer_with_weights-0.ff_3"_tf_keras_layer*{"name": "dense_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 20, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 18}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 19}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 20, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 40}}, "shared_object_id": 21}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 40]}}2

+root.layer_with_weights-0.ff_4"_tf_keras_layer*{"name": "dense_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 22}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 23}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 24, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 20}}, "shared_object_id": 25}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 20]}}2

\ No newline at end of file

sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_classifier_1.pt/keras_metadata.pb

+
+root"_tf_keras_network*{"name": "model_3", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Functional", "config": {"name": "model_3", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_5"}, "name": "input_5", "inbound_nodes": []}, {"class_name": "PNBClassifier", "config": {"name": "pnb_classifier_1", "trainable": true, "dtype": "float32"}, "name": "pnb_classifier_1", "inbound_nodes": [[["input_5", 0, 0, {}]]]}], "input_layers": [["input_5", 0, 0]], "output_layers": [["pnb_classifier_1", 0, 0]]}, "shared_object_id": 2, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "ndim": 5, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", "input_5"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", "input_5"]}, "keras_version": "2.8.0", "backend": "tensorflow", "model_config": {"class_name": "Functional", "config": {"name": "model_3", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_5"}, "name": "input_5", "inbound_nodes": [], "shared_object_id": 0}, {"class_name": "PNBClassifier", "config": {"name": "pnb_classifier_1", "trainable": true, "dtype": "float32"}, "name": "pnb_classifier_1", "inbound_nodes": [[["input_5", 0, 0, {}]]], "shared_object_id": 1}], "input_layers": [["input_5", 0, 0]], "output_layers": [["pnb_classifier_1", 0, 0]]}}}2

+
root.layer-0"_tf_keras_input_layer*{"class_name": "InputLayer", "name": "input_5", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_5"}}2

+root.layer_with_weights-0"_tf_keras_layer*{"name": "pnb_classifier_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "PNBClassifier", "config": {"name": "pnb_classifier_1", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["input_5", 0, 0, {}]]], "shared_object_id": 1}2

+	 root.layer_with_weights-0.conv_1"_tf_keras_layer*	{"name": "conv2d_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [8, 8]}, "strides": {"class_name": "__tuple__", "items": [4, 4]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 4}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 5}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 6, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 32}}, "shared_object_id": 7}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 68, 97, 32]}}2

+	 root.layer_with_weights-0.conv_2"_tf_keras_layer*	{"name": "conv2d_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_3", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [4, 4]}, "strides": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 8}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 9}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 10, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 32}}, "shared_object_id": 11}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 23, 32]}}2

+
!root.layer_with_weights-0.flatten"_tf_keras_layer*{"name": "flatten_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "shared_object_id": 12, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 1, "axes": {}}, "shared_object_id": 13}}2

+root.layer_with_weights-0.ff_2"_tf_keras_layer*{"name": "dense_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "dtype": "float32", "units": 40, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 14}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 15}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 16, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 2240}}, "shared_object_id": 17}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 2240]}}2

+root.layer_with_weights-0.ff_3"_tf_keras_layer*{"name": "dense_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "dtype": "float32", "units": 20, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 18}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 19}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 20, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 40}}, "shared_object_id": 21}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 40]}}2

+root.layer_with_weights-0.ff_4"_tf_keras_layer*{"name": "dense_5", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_5", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 22}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 23}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 24, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 20}}, "shared_object_id": 25}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 20]}}2

\ No newline at end of file

sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_classifier_2.pt/keras_metadata.pb

+
+root"_tf_keras_network*{"name": "model_4", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Functional", "config": {"name": "model_4", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_6"}, "name": "input_6", "inbound_nodes": []}, {"class_name": "PNBClassifier", "config": {"name": "pnb_classifier_2", "trainable": true, "dtype": "float32"}, "name": "pnb_classifier_2", "inbound_nodes": [[["input_6", 0, 0, {}]]]}], "input_layers": [["input_6", 0, 0]], "output_layers": [["pnb_classifier_2", 0, 0]]}, "shared_object_id": 2, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "ndim": 5, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", "input_6"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", "input_6"]}, "keras_version": "2.8.0", "backend": "tensorflow", "model_config": {"class_name": "Functional", "config": {"name": "model_4", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_6"}, "name": "input_6", "inbound_nodes": [], "shared_object_id": 0}, {"class_name": "PNBClassifier", "config": {"name": "pnb_classifier_2", "trainable": true, "dtype": "float32"}, "name": "pnb_classifier_2", "inbound_nodes": [[["input_6", 0, 0, {}]]], "shared_object_id": 1}], "input_layers": [["input_6", 0, 0]], "output_layers": [["pnb_classifier_2", 0, 0]]}}}2

+
root.layer-0"_tf_keras_input_layer*{"class_name": "InputLayer", "name": "input_6", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_6"}}2

+root.layer_with_weights-0"_tf_keras_layer*{"name": "pnb_classifier_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "PNBClassifier", "config": {"name": "pnb_classifier_2", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["input_6", 0, 0, {}]]], "shared_object_id": 1}2

+	 root.layer_with_weights-0.conv_1"_tf_keras_layer*	{"name": "conv2d_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_4", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [8, 8]}, "strides": {"class_name": "__tuple__", "items": [4, 4]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 4}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 5}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 6, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 32}}, "shared_object_id": 7}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 68, 97, 32]}}2

+	 root.layer_with_weights-0.conv_2"_tf_keras_layer*	{"name": "conv2d_5", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_5", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [4, 4]}, "strides": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 8}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 9}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 10, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 32}}, "shared_object_id": 11}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 23, 32]}}2

+
!root.layer_with_weights-0.flatten"_tf_keras_layer*{"name": "flatten_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Flatten", "config": {"name": "flatten_2", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "shared_object_id": 12, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 1, "axes": {}}, "shared_object_id": 13}}2

+root.layer_with_weights-0.ff_2"_tf_keras_layer*{"name": "dense_6", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_6", "trainable": true, "dtype": "float32", "units": 40, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 14}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 15}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 16, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 2240}}, "shared_object_id": 17}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 2240]}}2

+root.layer_with_weights-0.ff_3"_tf_keras_layer*{"name": "dense_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_7", "trainable": true, "dtype": "float32", "units": 20, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 18}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 19}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 20, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 40}}, "shared_object_id": 21}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 40]}}2

+root.layer_with_weights-0.ff_4"_tf_keras_layer*{"name": "dense_8", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_8", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 22}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 23}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 24, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 20}}, "shared_object_id": 25}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 20]}}2

\ No newline at end of file

sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_classifier_3.pt/keras_metadata.pb

+
+root"_tf_keras_network*{"name": "model_5", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Functional", "config": {"name": "model_5", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_7"}, "name": "input_7", "inbound_nodes": []}, {"class_name": "PNBClassifier", "config": {"name": "pnb_classifier_3", "trainable": true, "dtype": "float32"}, "name": "pnb_classifier_3", "inbound_nodes": [[["input_7", 0, 0, {}]]]}], "input_layers": [["input_7", 0, 0]], "output_layers": [["pnb_classifier_3", 0, 0]]}, "shared_object_id": 2, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "ndim": 5, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", "input_7"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", "input_7"]}, "keras_version": "2.8.0", "backend": "tensorflow", "model_config": {"class_name": "Functional", "config": {"name": "model_5", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_7"}, "name": "input_7", "inbound_nodes": [], "shared_object_id": 0}, {"class_name": "PNBClassifier", "config": {"name": "pnb_classifier_3", "trainable": true, "dtype": "float32"}, "name": "pnb_classifier_3", "inbound_nodes": [[["input_7", 0, 0, {}]]], "shared_object_id": 1}], "input_layers": [["input_7", 0, 0]], "output_layers": [["pnb_classifier_3", 0, 0]]}}}2

+
root.layer-0"_tf_keras_input_layer*{"class_name": "InputLayer", "name": "input_7", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_7"}}2

+root.layer_with_weights-0"_tf_keras_layer*{"name": "pnb_classifier_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "PNBClassifier", "config": {"name": "pnb_classifier_3", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["input_7", 0, 0, {}]]], "shared_object_id": 1}2

+	 root.layer_with_weights-0.conv_1"_tf_keras_layer*	{"name": "conv2d_6", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_6", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [8, 8]}, "strides": {"class_name": "__tuple__", "items": [4, 4]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 4}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 5}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 6, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 32}}, "shared_object_id": 7}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 68, 97, 32]}}2

+	 root.layer_with_weights-0.conv_2"_tf_keras_layer*	{"name": "conv2d_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_7", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [4, 4]}, "strides": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 8}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 9}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 10, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 32}}, "shared_object_id": 11}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 23, 32]}}2

+
!root.layer_with_weights-0.flatten"_tf_keras_layer*{"name": "flatten_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Flatten", "config": {"name": "flatten_3", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "shared_object_id": 12, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 1, "axes": {}}, "shared_object_id": 13}}2

+root.layer_with_weights-0.ff_2"_tf_keras_layer*{"name": "dense_9", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_9", "trainable": true, "dtype": "float32", "units": 40, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 14}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 15}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 16, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 2240}}, "shared_object_id": 17}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 2240]}}2

+root.layer_with_weights-0.ff_3"_tf_keras_layer*{"name": "dense_10", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_10", "trainable": true, "dtype": "float32", "units": 20, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 18}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 19}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 20, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 40}}, "shared_object_id": 21}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 40]}}2

+root.layer_with_weights-0.ff_4"_tf_keras_layer*{"name": "dense_11", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_11", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 22}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 23}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 24, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 20}}, "shared_object_id": 25}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 20]}}2

\ No newline at end of file