tf.config.experimental.set_memory_growth() failed, err: list index out of range
Selected GPU : Quadro RTX 6000 (id=0)
Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 32, 32, 3)]  0                                            
__________________________________________________________________________________________________
conv2d (Conv2D)                 (None, 32, 32, 16)   448         input_1[0][0]                    
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 32, 32, 16)   64          conv2d[0][0]                     
__________________________________________________________________________________________________
activation (Activation)         (None, 32, 32, 16)   0           batch_normalization[0][0]        
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 32, 32, 16)   2320        activation[0][0]                 
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 32, 32, 16)   64          conv2d_1[0][0]                   
__________________________________________________________________________________________________
activation_1 (Activation)       (None, 32, 32, 16)   0           batch_normalization_1[0][0]      
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 32, 32, 16)   2320        activation_1[0][0]               
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 32, 32, 16)   64          conv2d_2[0][0]                   
__________________________________________________________________________________________________
add (Add)                       (None, 32, 32, 16)   0           activation[0][0]                 
                                                                 batch_normalization_2[0][0]      
__________________________________________________________________________________________________
activation_2 (Activation)       (None, 32, 32, 16)   0           add[0][0]                        
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 16, 16, 32)   4640        activation_2[0][0]               
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 16, 16, 32)   128         conv2d_3[0][0]                   
__________________________________________________________________________________________________
activation_3 (Activation)       (None, 16, 16, 32)   0           batch_normalization_3[0][0]      
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 16, 16, 32)   9248        activation_3[0][0]               
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 16, 16, 32)   544         activation_2[0][0]               
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 16, 16, 32)   128         conv2d_4[0][0]                   
__________________________________________________________________________________________________
add_1 (Add)                     (None, 16, 16, 32)   0           conv2d_5[0][0]                   
                                                                 batch_normalization_4[0][0]      
__________________________________________________________________________________________________
activation_4 (Activation)       (None, 16, 16, 32)   0           add_1[0][0]                      
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, 8, 8, 64)     18496       activation_4[0][0]               
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 8, 8, 64)     256         conv2d_6[0][0]                   
__________________________________________________________________________________________________
activation_5 (Activation)       (None, 8, 8, 64)     0           batch_normalization_5[0][0]      
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, 8, 8, 64)     36928       activation_5[0][0]               
__________________________________________________________________________________________________
conv2d_8 (Conv2D)               (None, 8, 8, 64)     2112        activation_4[0][0]               
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 8, 8, 64)     256         conv2d_7[0][0]                   
__________________________________________________________________________________________________
add_2 (Add)                     (None, 8, 8, 64)     0           conv2d_8[0][0]                   
                                                                 batch_normalization_6[0][0]      
__________________________________________________________________________________________________
activation_6 (Activation)       (None, 8, 8, 64)     0           add_2[0][0]                      
__________________________________________________________________________________________________
average_pooling2d (AveragePooli (None, 1, 1, 64)     0           activation_6[0][0]               
__________________________________________________________________________________________________
flatten (Flatten)               (None, 64)           0           average_pooling2d[0][0]          
__________________________________________________________________________________________________
dense (Dense)                   (None, 10)           650         flatten[0][0]                    
==================================================================================================
Total params: 78,666
Trainable params: 78,186
Non-trainable params: 480
__________________________________________________________________________________________________

Total MACs: 12.502 M
Total OPs: 25.270 M
Name: image_classification
Version: 1
Description: TinyML: Image classification - ResNetv1-10 with CIFAR10
Classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck
hash: None
date: None
Training dataset: Found 50000 samples belonging to 10 classes:
  airplane = 5000
automobile = 5000
      bird = 5000
       cat = 5000
      deer = 5000
       dog = 5000
      frog = 5000
     horse = 5000
      ship = 5000
     truck = 5000
Validation dataset: Found 10000 samples belonging to 10 classes:
  airplane = 1000
automobile = 1000
      bird = 1000
       cat = 1000
      deer = 1000
       dog = 1000
      frog = 1000
     horse = 1000
      ship = 1000
     truck = 1000
Using default TensorBoard callback with following parameters:
{'histogram_freq': 1,
 'log_dir': '/home/dariedle/.mltk/models/image_classification/train/tensorboard',
 'profile_batch': 2,
 'update_freq': 'epoch',
 'write_graph': True,
 'write_images': False}
Using default ModelCheckpoint callback with following parameters:
{'filepath': '/home/dariedle/.mltk/models/image_classification/train/weights/weights-{epoch:03d}-{val_accuracy:.4f}.h5',
 'mode': 'auto',
 'monitor': 'val_accuracy',
 'options': None,
 'save_best_only': True,
 'save_freq': 'epoch',
 'save_weights_only': True,
 'verbose': 0}
Enabling model checkpoints
Using Keras callbacks: TensorBoard, ModelCheckpoint, ModelCheckpoint
Starting model training ...
Generating /home/dariedle/.mltk/models/image_classification/image_classification.h5


*** Best training val_accuracy = 0.839


Generating /home/dariedle/.mltk/models/image_classification/train/training-history.json
Generating /home/dariedle/.mltk/models/image_classification/train/training-history.png
Creating /data/dariedle/mltk/mltk/models/tinyml/image_classification.mltk.zip
