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Epoch 18/50 |
352/352 [==============================] - 9s 26ms/step - loss: 2.1574 - acc: 0.4258 - top5-acc: 0.7451 - val_loss: 2.3140 - val_acc: 0.4052 - val_top5-acc: 0.7256 |
Epoch 19/50 |
352/352 [==============================] - 9s 26ms/step - loss: 2.1369 - acc: 0.4309 - top5-acc: 0.7487 - val_loss: 2.3012 - val_acc: 0.4124 - val_top5-acc: 0.7190 |
Epoch 20/50 |
352/352 [==============================] - 9s 26ms/step - loss: 2.1222 - acc: 0.4350 - top5-acc: 0.7494 - val_loss: 2.3294 - val_acc: 0.4076 - val_top5-acc: 0.7186 |
Epoch 21/50 |
352/352 [==============================] - 9s 26ms/step - loss: 2.0822 - acc: 0.4436 - top5-acc: 0.7576 - val_loss: 2.2498 - val_acc: 0.4302 - val_top5-acc: 0.7276 |
Epoch 22/50 |
352/352 [==============================] - 9s 26ms/step - loss: 2.0609 - acc: 0.4518 - top5-acc: 0.7610 - val_loss: 2.2915 - val_acc: 0.4232 - val_top5-acc: 0.7280 |
Epoch 23/50 |
352/352 [==============================] - 9s 26ms/step - loss: 2.0482 - acc: 0.4590 - top5-acc: 0.7648 - val_loss: 2.2448 - val_acc: 0.4242 - val_top5-acc: 0.7296 |
Epoch 24/50 |
352/352 [==============================] - 9s 26ms/step - loss: 2.0292 - acc: 0.4560 - top5-acc: 0.7705 - val_loss: 2.2526 - val_acc: 0.4334 - val_top5-acc: 0.7324 |
Epoch 25/50 |
352/352 [==============================] - 9s 26ms/step - loss: 2.0316 - acc: 0.4544 - top5-acc: 0.7687 - val_loss: 2.2430 - val_acc: 0.4318 - val_top5-acc: 0.7338 |
Epoch 26/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.9988 - acc: 0.4616 - top5-acc: 0.7748 - val_loss: 2.2053 - val_acc: 0.4470 - val_top5-acc: 0.7366 |
Epoch 27/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.9788 - acc: 0.4646 - top5-acc: 0.7806 - val_loss: 2.2313 - val_acc: 0.4378 - val_top5-acc: 0.7420 |
Epoch 28/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.9702 - acc: 0.4688 - top5-acc: 0.7829 - val_loss: 2.2392 - val_acc: 0.4344 - val_top5-acc: 0.7338 |
Epoch 29/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.9488 - acc: 0.4699 - top5-acc: 0.7866 - val_loss: 2.1600 - val_acc: 0.4490 - val_top5-acc: 0.7446 |
Epoch 30/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.9302 - acc: 0.4803 - top5-acc: 0.7878 - val_loss: 2.2069 - val_acc: 0.4410 - val_top5-acc: 0.7486 |
Epoch 31/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.9135 - acc: 0.4806 - top5-acc: 0.7916 - val_loss: 2.1929 - val_acc: 0.4486 - val_top5-acc: 0.7514 |
Epoch 32/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.8890 - acc: 0.4844 - top5-acc: 0.7961 - val_loss: 2.2176 - val_acc: 0.4404 - val_top5-acc: 0.7494 |
Epoch 33/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.8844 - acc: 0.4872 - top5-acc: 0.7980 - val_loss: 2.2321 - val_acc: 0.4444 - val_top5-acc: 0.7460 |
Epoch 34/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.8588 - acc: 0.4912 - top5-acc: 0.8005 - val_loss: 2.1895 - val_acc: 0.4532 - val_top5-acc: 0.7510 |
Epoch 35/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.7259 - acc: 0.5232 - top5-acc: 0.8266 - val_loss: 2.1024 - val_acc: 0.4800 - val_top5-acc: 0.7726 |
Epoch 36/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.6262 - acc: 0.5488 - top5-acc: 0.8437 - val_loss: 2.0712 - val_acc: 0.4830 - val_top5-acc: 0.7754 |
Epoch 37/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.6164 - acc: 0.5481 - top5-acc: 0.8390 - val_loss: 2.1219 - val_acc: 0.4772 - val_top5-acc: 0.7678 |
Epoch 38/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.5850 - acc: 0.5568 - top5-acc: 0.8510 - val_loss: 2.0931 - val_acc: 0.4892 - val_top5-acc: 0.7732 |
Epoch 39/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.5741 - acc: 0.5589 - top5-acc: 0.8507 - val_loss: 2.0910 - val_acc: 0.4910 - val_top5-acc: 0.7700 |
Epoch 40/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.5546 - acc: 0.5675 - top5-acc: 0.8519 - val_loss: 2.1388 - val_acc: 0.4790 - val_top5-acc: 0.7742 |
Epoch 41/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.5464 - acc: 0.5684 - top5-acc: 0.8561 - val_loss: 2.1121 - val_acc: 0.4786 - val_top5-acc: 0.7718 |
Epoch 42/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.4494 - acc: 0.5890 - top5-acc: 0.8702 - val_loss: 2.1157 - val_acc: 0.4944 - val_top5-acc: 0.7802 |
Epoch 43/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.3847 - acc: 0.6069 - top5-acc: 0.8825 - val_loss: 2.1048 - val_acc: 0.4884 - val_top5-acc: 0.7752 |
Epoch 44/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.3724 - acc: 0.6087 - top5-acc: 0.8832 - val_loss: 2.0681 - val_acc: 0.4924 - val_top5-acc: 0.7868 |
Epoch 45/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.3643 - acc: 0.6116 - top5-acc: 0.8840 - val_loss: 2.0965 - val_acc: 0.4932 - val_top5-acc: 0.7752 |
Epoch 46/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.3517 - acc: 0.6184 - top5-acc: 0.8849 - val_loss: 2.0869 - val_acc: 0.4956 - val_top5-acc: 0.7778 |
Epoch 47/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.3377 - acc: 0.6211 - top5-acc: 0.8891 - val_loss: 2.1120 - val_acc: 0.4882 - val_top5-acc: 0.7764 |
Epoch 48/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.3369 - acc: 0.6186 - top5-acc: 0.8888 - val_loss: 2.1257 - val_acc: 0.4912 - val_top5-acc: 0.7752 |
Epoch 49/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.3266 - acc: 0.6190 - top5-acc: 0.8893 - val_loss: 2.0961 - val_acc: 0.4958 - val_top5-acc: 0.7828 |
Epoch 50/50 |
352/352 [==============================] - 9s 26ms/step - loss: 1.2731 - acc: 0.6352 - top5-acc: 0.8976 - val_loss: 2.0897 - val_acc: 0.4982 - val_top5-acc: 0.7788 |
313/313 [==============================] - 2s 7ms/step - loss: 2.0743 - acc: 0.5064 - top5-acc: 0.7828 |
Test accuracy: 50.64% |
Test top 5 accuracy: 78.28% |
As shown in the gMLP paper, better results can be achieved by increasing the embedding dimensions, increasing the number of gMLP blocks, and training the model for longer. You may also try to increase the size of the input images and use different patch sizes. Note that, the paper used advanced regularization strategie... |
Implementing the Perceiver model for image classification. |
Introduction |
This example implements the Perceiver: General Perception with Iterative Attention model by Andrew Jaegle et al. for image classification, and demonstrates it on the CIFAR-100 dataset. |
The Perceiver model leverages an asymmetric attention mechanism to iteratively distill inputs into a tight latent bottleneck, allowing it to scale to handle very large inputs. |
In other words: let's assume that your input data array (e.g. image) has M elements (i.e. patches), where M is large. In a standard Transformer model, a self-attention operation is performed for the M elements. The complexity of this operation is O(M^2). However, the Perceiver model creates a latent array of size N ele... |
Cross-attention Transformer between the latent array and the data array - The complexity of this operation is O(M.N). |
Self-attention Transformer on the latent array - The complexity of this operation is O(N^2). |
This example requires TensorFlow 2.4 or higher, as well as TensorFlow Addons, which can be installed using the following command: |
pip install -U tensorflow-addons |
Setup |
import numpy as np |
import tensorflow as tf |
from tensorflow import keras |
from tensorflow.keras import layers |
import tensorflow_addons as tfa |
Prepare the data |
num_classes = 100 |
input_shape = (32, 32, 3) |
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data() |
print(f\"x_train shape: {x_train.shape} - y_train shape: {y_train.shape}\") |
print(f\"x_test shape: {x_test.shape} - y_test shape: {y_test.shape}\") |
x_train shape: (50000, 32, 32, 3) - y_train shape: (50000, 1) |
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