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352/352 [==============================] - 8s 23ms/step - loss: 2.2029 - acc: 0.4180 - top5-acc: 0.7370 - val_loss: 2.3116 - val_acc: 0.4226 - val_top5-acc: 0.7268 |
Epoch 28/50 |
352/352 [==============================] - 8s 23ms/step - loss: 2.1959 - acc: 0.4234 - top5-acc: 0.7380 - val_loss: 2.4053 - val_acc: 0.4064 - val_top5-acc: 0.7168 |
Epoch 29/50 |
352/352 [==============================] - 8s 23ms/step - loss: 2.1815 - acc: 0.4227 - top5-acc: 0.7415 - val_loss: 2.4020 - val_acc: 0.4078 - val_top5-acc: 0.7192 |
Epoch 30/50 |
352/352 [==============================] - 8s 23ms/step - loss: 2.1783 - acc: 0.4245 - top5-acc: 0.7407 - val_loss: 2.4206 - val_acc: 0.3996 - val_top5-acc: 0.7234 |
Epoch 31/50 |
352/352 [==============================] - 8s 22ms/step - loss: 2.1686 - acc: 0.4248 - top5-acc: 0.7442 - val_loss: 2.3743 - val_acc: 0.4100 - val_top5-acc: 0.7162 |
Epoch 32/50 |
352/352 [==============================] - 8s 23ms/step - loss: 2.1487 - acc: 0.4317 - top5-acc: 0.7472 - val_loss: 2.3882 - val_acc: 0.4018 - val_top5-acc: 0.7266 |
Epoch 33/50 |
352/352 [==============================] - 8s 22ms/step - loss: 1.9836 - acc: 0.4644 - top5-acc: 0.7782 - val_loss: 2.1742 - val_acc: 0.4536 - val_top5-acc: 0.7506 |
Epoch 34/50 |
352/352 [==============================] - 8s 23ms/step - loss: 1.8723 - acc: 0.4950 - top5-acc: 0.7985 - val_loss: 2.1716 - val_acc: 0.4506 - val_top5-acc: 0.7546 |
Epoch 35/50 |
352/352 [==============================] - 8s 23ms/step - loss: 1.8461 - acc: 0.5009 - top5-acc: 0.8003 - val_loss: 2.1661 - val_acc: 0.4480 - val_top5-acc: 0.7542 |
Epoch 36/50 |
352/352 [==============================] - 8s 23ms/step - loss: 1.8499 - acc: 0.4944 - top5-acc: 0.8044 - val_loss: 2.1523 - val_acc: 0.4566 - val_top5-acc: 0.7628 |
Epoch 37/50 |
352/352 [==============================] - 8s 22ms/step - loss: 1.8322 - acc: 0.5000 - top5-acc: 0.8059 - val_loss: 2.1334 - val_acc: 0.4570 - val_top5-acc: 0.7560 |
Epoch 38/50 |
352/352 [==============================] - 8s 23ms/step - loss: 1.8269 - acc: 0.5027 - top5-acc: 0.8085 - val_loss: 2.1024 - val_acc: 0.4614 - val_top5-acc: 0.7674 |
Epoch 39/50 |
352/352 [==============================] - 8s 23ms/step - loss: 1.8242 - acc: 0.4990 - top5-acc: 0.8098 - val_loss: 2.0789 - val_acc: 0.4610 - val_top5-acc: 0.7792 |
Epoch 40/50 |
352/352 [==============================] - 8s 23ms/step - loss: 1.7983 - acc: 0.5067 - top5-acc: 0.8122 - val_loss: 2.1514 - val_acc: 0.4546 - val_top5-acc: 0.7628 |
Epoch 41/50 |
352/352 [==============================] - 8s 23ms/step - loss: 1.7974 - acc: 0.5112 - top5-acc: 0.8132 - val_loss: 2.1425 - val_acc: 0.4542 - val_top5-acc: 0.7630 |
Epoch 42/50 |
352/352 [==============================] - 8s 23ms/step - loss: 1.7972 - acc: 0.5128 - top5-acc: 0.8127 - val_loss: 2.0980 - val_acc: 0.4580 - val_top5-acc: 0.7724 |
Epoch 43/50 |
352/352 [==============================] - 8s 23ms/step - loss: 1.8026 - acc: 0.5066 - top5-acc: 0.8115 - val_loss: 2.0922 - val_acc: 0.4684 - val_top5-acc: 0.7678 |
Epoch 44/50 |
352/352 [==============================] - 8s 23ms/step - loss: 1.7924 - acc: 0.5092 - top5-acc: 0.8129 - val_loss: 2.0511 - val_acc: 0.4750 - val_top5-acc: 0.7726 |
Epoch 45/50 |
352/352 [==============================] - 8s 22ms/step - loss: 1.7695 - acc: 0.5106 - top5-acc: 0.8193 - val_loss: 2.0949 - val_acc: 0.4678 - val_top5-acc: 0.7708 |
Epoch 46/50 |
352/352 [==============================] - 8s 23ms/step - loss: 1.7784 - acc: 0.5106 - top5-acc: 0.8141 - val_loss: 2.1094 - val_acc: 0.4656 - val_top5-acc: 0.7704 |
Epoch 47/50 |
352/352 [==============================] - 8s 23ms/step - loss: 1.7625 - acc: 0.5155 - top5-acc: 0.8190 - val_loss: 2.0492 - val_acc: 0.4774 - val_top5-acc: 0.7744 |
Epoch 48/50 |
352/352 [==============================] - 8s 23ms/step - loss: 1.7441 - acc: 0.5217 - top5-acc: 0.8190 - val_loss: 2.0562 - val_acc: 0.4698 - val_top5-acc: 0.7828 |
Epoch 49/50 |
352/352 [==============================] - 8s 23ms/step - loss: 1.7665 - acc: 0.5113 - top5-acc: 0.8196 - val_loss: 2.0348 - val_acc: 0.4708 - val_top5-acc: 0.7730 |
Epoch 50/50 |
352/352 [==============================] - 8s 23ms/step - loss: 1.7392 - acc: 0.5201 - top5-acc: 0.8226 - val_loss: 2.0787 - val_acc: 0.4710 - val_top5-acc: 0.7734 |
313/313 [==============================] - 2s 8ms/step - loss: 2.0571 - acc: 0.4758 - top5-acc: 0.7718 |
Test accuracy: 47.58% |
Test top 5 accuracy: 77.18% |
The MLP-Mixer model tends to have much less number of parameters compared to convolutional and transformer-based models, which leads to less training and serving computational cost. |
As mentioned in the MLP-Mixer paper, when pre-trained on large datasets, or with modern regularization schemes, the MLP-Mixer attains competitive scores to state-of-the-art models. You can obtain better results by increasing the embedding dimensions, increasing, increasing the number of mixer blocks, and training the m... |
The FNet model |
The FNet uses a similar block to the Transformer block. However, FNet replaces the self-attention layer in the Transformer block with a parameter-free 2D Fourier transformation layer: |
One 1D Fourier Transform is applied along the patches. |
One 1D Fourier Transform is applied along the channels. |
Implement the FNet module |
class FNetLayer(layers.Layer): |
def __init__(self, num_patches, embedding_dim, dropout_rate, *args, **kwargs): |
super(FNetLayer, self).__init__(*args, **kwargs) |
self.ffn = keras.Sequential( |
[ |
layers.Dense(units=embedding_dim), |
tfa.layers.GELU(), |
layers.Dropout(rate=dropout_rate), |
layers.Dense(units=embedding_dim), |
] |
) |
self.normalize1 = layers.LayerNormalization(epsilon=1e-6) |
self.normalize2 = layers.LayerNormalization(epsilon=1e-6) |
def call(self, inputs): |
# Apply fourier transformations. |
x = tf.cast( |
tf.signal.fft2d(tf.cast(inputs, dtype=tf.dtypes.complex64)), |
dtype=tf.dtypes.float32, |
) |
# Add skip connection. |
x = x + inputs |
# Apply layer normalization. |
x = self.normalize1(x) |
# Apply Feedfowrad network. |
x_ffn = self.ffn(x) |
# Add skip connection. |
x = x + x_ffn |
# Apply layer normalization. |
return self.normalize2(x) |
Build, train, and evaluate the FNet model |
Note that training the model with the current settings on a V100 GPUs takes around 8 seconds per epoch. |
fnet_blocks = keras.Sequential( |
[FNetLayer(num_patches, embedding_dim, dropout_rate) for _ in range(num_blocks)] |
) |
learning_rate = 0.001 |
fnet_classifier = build_classifier(fnet_blocks, positional_encoding=True) |
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