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HenryAI
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KerasCodeExamples.txt

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4.99k
352/352 [==============================] - 8s 22ms/step - loss: 1.8179 - acc: 0.5089 - top5-acc: 0.8155 - val_loss: 2.0514 - val_acc: 0.4576 - val_top5-acc: 0.7566
313/313 [==============================] - 2s 6ms/step - loss: 2.0142 - acc: 0.4663 - top5-acc: 0.7647
Test accuracy: 46.63%
Test top 5 accuracy: 76.47%
As shown in the FNet paper, better results can be achieved by increasing the embedding dimensions, increasing the number of FNet blocks, and training the model for longer. You may also try to increase the size of the input images and use different patch sizes. The FNet scales very efficiently to long inputs, runs much ...
The gMLP model
The gMLP is a MLP architecture that features a Spatial Gating Unit (SGU). The SGU enables cross-patch interactions across the spatial (channel) dimension, by:
Transforming the input spatially by applying linear projection across patches (along channels).
Applying element-wise multiplication of the input and its spatial transformation.
Implement the gMLP module
class gMLPLayer(layers.Layer):
def __init__(self, num_patches, embedding_dim, dropout_rate, *args, **kwargs):
super(gMLPLayer, self).__init__(*args, **kwargs)
self.channel_projection1 = keras.Sequential(
[
layers.Dense(units=embedding_dim * 2),
tfa.layers.GELU(),
layers.Dropout(rate=dropout_rate),
]
)
self.channel_projection2 = layers.Dense(units=embedding_dim)
self.spatial_projection = layers.Dense(
units=num_patches, bias_initializer=\"Ones\"
)
self.normalize1 = layers.LayerNormalization(epsilon=1e-6)
self.normalize2 = layers.LayerNormalization(epsilon=1e-6)
def spatial_gating_unit(self, x):
# Split x along the channel dimensions.
# Tensors u and v will in th shape of [batch_size, num_patchs, embedding_dim].
u, v = tf.split(x, num_or_size_splits=2, axis=2)
# Apply layer normalization.
v = self.normalize2(v)
# Apply spatial projection.
v_channels = tf.linalg.matrix_transpose(v)
v_projected = self.spatial_projection(v_channels)
v_projected = tf.linalg.matrix_transpose(v_projected)
# Apply element-wise multiplication.
return u * v_projected
def call(self, inputs):
# Apply layer normalization.
x = self.normalize1(inputs)
# Apply the first channel projection. x_projected shape: [batch_size, num_patches, embedding_dim * 2].
x_projected = self.channel_projection1(x)
# Apply the spatial gating unit. x_spatial shape: [batch_size, num_patches, embedding_dim].
x_spatial = self.spatial_gating_unit(x_projected)
# Apply the second channel projection. x_projected shape: [batch_size, num_patches, embedding_dim].
x_projected = self.channel_projection2(x_spatial)
# Add skip connection.
return x + x_projected
Build, train, and evaluate the gMLP model
Note that training the model with the current settings on a V100 GPUs takes around 9 seconds per epoch.
gmlp_blocks = keras.Sequential(
[gMLPLayer(num_patches, embedding_dim, dropout_rate) for _ in range(num_blocks)]
)
learning_rate = 0.003
gmlp_classifier = build_classifier(gmlp_blocks)
history = run_experiment(gmlp_classifier)
Epoch 1/50
352/352 [==============================] - 13s 28ms/step - loss: 4.1713 - acc: 0.0704 - top5-acc: 0.2206 - val_loss: 3.5629 - val_acc: 0.1548 - val_top5-acc: 0.4086
Epoch 2/50
352/352 [==============================] - 9s 27ms/step - loss: 3.5146 - acc: 0.1633 - top5-acc: 0.4172 - val_loss: 3.2899 - val_acc: 0.2066 - val_top5-acc: 0.4900
Epoch 3/50
352/352 [==============================] - 9s 26ms/step - loss: 3.2588 - acc: 0.2017 - top5-acc: 0.4895 - val_loss: 3.1152 - val_acc: 0.2362 - val_top5-acc: 0.5278
Epoch 4/50
352/352 [==============================] - 9s 26ms/step - loss: 3.1037 - acc: 0.2331 - top5-acc: 0.5288 - val_loss: 2.9771 - val_acc: 0.2624 - val_top5-acc: 0.5646
Epoch 5/50
352/352 [==============================] - 9s 26ms/step - loss: 2.9483 - acc: 0.2637 - top5-acc: 0.5680 - val_loss: 2.8807 - val_acc: 0.2784 - val_top5-acc: 0.5840
Epoch 6/50
352/352 [==============================] - 9s 26ms/step - loss: 2.8411 - acc: 0.2821 - top5-acc: 0.5930 - val_loss: 2.7246 - val_acc: 0.3146 - val_top5-acc: 0.6256
Epoch 7/50
352/352 [==============================] - 9s 26ms/step - loss: 2.7221 - acc: 0.3085 - top5-acc: 0.6193 - val_loss: 2.7022 - val_acc: 0.3108 - val_top5-acc: 0.6270
Epoch 8/50
352/352 [==============================] - 9s 26ms/step - loss: 2.6296 - acc: 0.3334 - top5-acc: 0.6420 - val_loss: 2.6289 - val_acc: 0.3324 - val_top5-acc: 0.6494
Epoch 9/50
352/352 [==============================] - 9s 26ms/step - loss: 2.5691 - acc: 0.3413 - top5-acc: 0.6563 - val_loss: 2.5353 - val_acc: 0.3586 - val_top5-acc: 0.6746
Epoch 10/50
352/352 [==============================] - 9s 26ms/step - loss: 2.4854 - acc: 0.3575 - top5-acc: 0.6760 - val_loss: 2.5271 - val_acc: 0.3578 - val_top5-acc: 0.6720
Epoch 11/50
352/352 [==============================] - 9s 26ms/step - loss: 2.4252 - acc: 0.3722 - top5-acc: 0.6870 - val_loss: 2.4553 - val_acc: 0.3684 - val_top5-acc: 0.6850
Epoch 12/50
352/352 [==============================] - 9s 26ms/step - loss: 2.3814 - acc: 0.3822 - top5-acc: 0.6985 - val_loss: 2.3841 - val_acc: 0.3888 - val_top5-acc: 0.6966
Epoch 13/50
352/352 [==============================] - 9s 26ms/step - loss: 2.3119 - acc: 0.3950 - top5-acc: 0.7135 - val_loss: 2.4306 - val_acc: 0.3780 - val_top5-acc: 0.6894
Epoch 14/50
352/352 [==============================] - 9s 26ms/step - loss: 2.2886 - acc: 0.4033 - top5-acc: 0.7168 - val_loss: 2.4053 - val_acc: 0.3932 - val_top5-acc: 0.7010
Epoch 15/50
352/352 [==============================] - 9s 26ms/step - loss: 2.2455 - acc: 0.4080 - top5-acc: 0.7233 - val_loss: 2.3443 - val_acc: 0.4004 - val_top5-acc: 0.7128
Epoch 16/50
352/352 [==============================] - 9s 26ms/step - loss: 2.2128 - acc: 0.4152 - top5-acc: 0.7317 - val_loss: 2.3150 - val_acc: 0.4018 - val_top5-acc: 0.7174
Epoch 17/50
352/352 [==============================] - 9s 26ms/step - loss: 2.1990 - acc: 0.4206 - top5-acc: 0.7357 - val_loss: 2.3590 - val_acc: 0.3978 - val_top5-acc: 0.7086