Datasets:
HenryAI
/
KerasCodeExamples.txt

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history = model.fit(
train_dataset,
validation_data=val_dataset,
epochs=num_epochs,
callbacks=[checkpoint_callback],
)
model.load_weights(checkpoint_filepath)
_, accuracy = model.evaluate(test_dataset)
print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")
return history, model
Train and evaluate model
conv_mixer_model = get_conv_mixer_256_8()
history, conv_mixer_model = run_experiment(conv_mixer_model)
2021-10-17 03:44:01.291445: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
Epoch 1/10
2021-10-17 03:44:04.721186: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005
352/352 [==============================] - 29s 70ms/step - loss: 1.2272 - accuracy: 0.5592 - val_loss: 3.9422 - val_accuracy: 0.1196
Epoch 2/10
352/352 [==============================] - 24s 69ms/step - loss: 0.7813 - accuracy: 0.7278 - val_loss: 0.8860 - val_accuracy: 0.6898
Epoch 3/10
352/352 [==============================] - 24s 68ms/step - loss: 0.5947 - accuracy: 0.7943 - val_loss: 0.6175 - val_accuracy: 0.7856
Epoch 4/10
352/352 [==============================] - 24s 69ms/step - loss: 0.4801 - accuracy: 0.8330 - val_loss: 0.5634 - val_accuracy: 0.8064
Epoch 5/10
352/352 [==============================] - 24s 68ms/step - loss: 0.4065 - accuracy: 0.8599 - val_loss: 0.5359 - val_accuracy: 0.8166
Epoch 6/10
352/352 [==============================] - 24s 68ms/step - loss: 0.3473 - accuracy: 0.8804 - val_loss: 0.5257 - val_accuracy: 0.8228
Epoch 7/10
352/352 [==============================] - 24s 68ms/step - loss: 0.3071 - accuracy: 0.8944 - val_loss: 0.4982 - val_accuracy: 0.8264
Epoch 8/10
352/352 [==============================] - 24s 68ms/step - loss: 0.2655 - accuracy: 0.9083 - val_loss: 0.5032 - val_accuracy: 0.8346
Epoch 9/10
352/352 [==============================] - 24s 68ms/step - loss: 0.2328 - accuracy: 0.9194 - val_loss: 0.5225 - val_accuracy: 0.8326
Epoch 10/10
352/352 [==============================] - 24s 68ms/step - loss: 0.2115 - accuracy: 0.9278 - val_loss: 0.5063 - val_accuracy: 0.8372
79/79 [==============================] - 2s 19ms/step - loss: 0.5412 - accuracy: 0.8325
Test accuracy: 83.25%
The gap in training and validation performance can be mitigated by using additional regularization techniques. Nevertheless, being able to get to ~83% accuracy within 10 epochs with 0.8 million parameters is a strong result.
Visualizing the internals of ConvMixer
We can visualize the patch embeddings and the learned convolution filters. Recall that each patch embedding and intermediate feature map have the same number of channels (256 in this case). This will make our visualization utility easier to implement.
# Code reference: https://bit.ly/3awIRbP.
def visualization_plot(weights, idx=1):
# First, apply min-max normalization to the
# given weights to avoid isotrophic scaling.
p_min, p_max = weights.min(), weights.max()
weights = (weights - p_min) / (p_max - p_min)
# Visualize all the filters.
num_filters = 256
plt.figure(figsize=(8, 8))
for i in range(num_filters):
current_weight = weights[:, :, :, i]
if current_weight.shape[-1] == 1:
current_weight = current_weight.squeeze()
ax = plt.subplot(16, 16, idx)
ax.set_xticks([])
ax.set_yticks([])
plt.imshow(current_weight)
idx += 1
# We first visualize the learned patch embeddings.
patch_embeddings = conv_mixer_model.layers[2].get_weights()[0]
visualization_plot(patch_embeddings)
png
Even though we did not train the network to convergence, we can notice that different patches show different patterns. Some share similarity with others while some are very different. These visualizations are more salient with larger image sizes.
Similarly, we can visualize the raw convolution kernels. This can help us understand the patterns to which a given kernel is receptive.
# First, print the indices of the convolution layers that are not
# pointwise convolutions.
for i, layer in enumerate(conv_mixer_model.layers):
if isinstance(layer, layers.DepthwiseConv2D):
if layer.get_config()[\"kernel_size\"] == (5, 5):
print(i, layer)
idx = 26 # Taking a kernel from the middle of the network.
kernel = conv_mixer_model.layers[idx].get_weights()[0]
kernel = np.expand_dims(kernel.squeeze(), axis=2)
visualization_plot(kernel)
5 <keras.layers.convolutional.DepthwiseConv2D object at 0x7f9e74854990>
12 <keras.layers.convolutional.DepthwiseConv2D object at 0x7f9e747df910>
19 <keras.layers.convolutional.DepthwiseConv2D object at 0x7f9e6c5c9e10>
26 <keras.layers.convolutional.DepthwiseConv2D object at 0x7f9e74906750>
33 <keras.layers.convolutional.DepthwiseConv2D object at 0x7f9e74902390>
40 <keras.layers.convolutional.DepthwiseConv2D object at 0x7f9e748ee690>
47 <keras.layers.convolutional.DepthwiseConv2D object at 0x7f9e7493dfd0>
54 <keras.layers.convolutional.DepthwiseConv2D object at 0x7f9e6c4e8a10>