313/313 [==============================] - 1957s 6s/step - loss: 0.0287 - acc: 0.9632
Loss of test model is 0.028715036809444427
Accuracy of test model is 96.31999731063843 %
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
up_sampling2d (UpSampling2D) (None, None, None, None) 0
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, None, None, None) 0
_________________________________________________________________
up_sampling2d_2 (UpSampling2 (None, None, None, None) 0
_________________________________________________________________
resnet50 (Functional) (None, 8, 8, 2048) 23587712
_________________________________________________________________
flatten (Flatten) (None, 131072) 0
_________________________________________________________________
batch_normalization (BatchNo (None, 131072) 524288
_________________________________________________________________
dense (Dense) (None, 128) 16777344
_________________________________________________________________
dropout (Dropout) (None, 128) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 128) 512
_________________________________________________________________
dense_1 (Dense) (None, 64) 8256
_________________________________________________________________
dropout_1 (Dropout) (None, 64) 0
_________________________________________________________________
batch_normalization_2 (Batch (None, 64) 256
_________________________________________________________________
dense_2 (Dense) (None, 10) 650
=================================================================
- Total params: 40,899,018
- Trainable params: 40,583,370
- Non-trainable params: 315,648
- Use BCE loss
- Cifar10
- Optimizer: RMSprop, with LR: 2e-5, Number of epoch: 60
- Training with batch size: 64
- Data augmentation:
datagen = ImageDataGenerator(
horizontal_flip=flip,
width_shift_range=width_shift,
height_shift_range=height_shift,
rotation_range=15,
)
- Checkpoint Weight & Model at: https://drive.google.com/file/d/1ZtqDwK5e2vCJFEYfJdp4aYY5NjdkIZI6/view?usp=sharing
- Classification Report
precision recall f1-score support
airplane 0.98 0.98 0.98 1000
automobile 0.97 0.98 0.98 1000
bird 0.96 0.96 0.96 1000
cat 0.93 0.90 0.91 1000
deer 0.96 0.96 0.96 1000
dog 0.94 0.92 0.93 1000
frog 0.96 0.99 0.97 1000
horse 0.97 0.98 0.97 1000
ship 0.98 0.98 0.98 1000
truck 0.98 0.97 0.97 1000
accuracy 0.96 10000
macro avg 0.96 0.96 0.96 10000
weighted avg 0.96 0.96 0.96 10000
- Confusion Matrix
