Datasets:
HenryAI
/
KerasCodeExamples.txt

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# Create a learning rate scheduler callback.
reduce_lr = keras.callbacks.ReduceLROnPlateau(
monitor=\"val_loss\", factor=0.2, patience=3
)
# Create an early stopping callback.
early_stopping = tf.keras.callbacks.EarlyStopping(
monitor=\"val_loss\", patience=15, restore_best_weights=True
)
# Fit the model.
history = model.fit(
x=x_train,
y=y_train,
batch_size=batch_size,
epochs=num_epochs,
validation_split=0.1,
callbacks=[early_stopping, reduce_lr],
)
_, accuracy, top_5_accuracy = model.evaluate(x_test, y_test)
print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")
print(f\"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%\")
# Return history to plot learning curves.
return history
Note that training the perceiver model with the current settings on a V100 GPUs takes around 200 seconds.
perceiver_classifier = Perceiver(
patch_size,
num_patches,
latent_dim,
projection_dim,
num_heads,
num_transformer_blocks,
ffn_units,
dropout_rate,
num_iterations,
classifier_units,
)
history = run_experiment(perceiver_classifier)
Epoch 1/100
704/704 [==============================] - 305s 405ms/step - loss: 4.4550 - acc: 0.0389 - top5-acc: 0.1407 - val_loss: 4.0544 - val_acc: 0.0802 - val_top5-acc: 0.2516
Epoch 2/100
704/704 [==============================] - 284s 403ms/step - loss: 4.0639 - acc: 0.0889 - top5-acc: 0.2576 - val_loss: 3.7379 - val_acc: 0.1272 - val_top5-acc: 0.3556
Epoch 3/100
704/704 [==============================] - 283s 402ms/step - loss: 3.8400 - acc: 0.1226 - top5-acc: 0.3326 - val_loss: 3.4527 - val_acc: 0.1750 - val_top5-acc: 0.4350
Epoch 4/100
704/704 [==============================] - 283s 402ms/step - loss: 3.5917 - acc: 0.1657 - top5-acc: 0.4063 - val_loss: 3.2160 - val_acc: 0.2176 - val_top5-acc: 0.5048
Epoch 5/100
704/704 [==============================] - 283s 403ms/step - loss: 3.3820 - acc: 0.2082 - top5-acc: 0.4638 - val_loss: 2.9947 - val_acc: 0.2584 - val_top5-acc: 0.5732
Epoch 6/100
704/704 [==============================] - 284s 403ms/step - loss: 3.2487 - acc: 0.2338 - top5-acc: 0.4991 - val_loss: 2.9179 - val_acc: 0.2770 - val_top5-acc: 0.5744
Epoch 7/100
704/704 [==============================] - 283s 402ms/step - loss: 3.1228 - acc: 0.2605 - top5-acc: 0.5295 - val_loss: 2.7958 - val_acc: 0.2994 - val_top5-acc: 0.6100
Epoch 8/100
704/704 [==============================] - 283s 402ms/step - loss: 2.9989 - acc: 0.2862 - top5-acc: 0.5588 - val_loss: 2.7117 - val_acc: 0.3208 - val_top5-acc: 0.6340
Epoch 9/100
704/704 [==============================] - 283s 402ms/step - loss: 2.9294 - acc: 0.3018 - top5-acc: 0.5763 - val_loss: 2.5933 - val_acc: 0.3390 - val_top5-acc: 0.6636
Epoch 10/100
704/704 [==============================] - 283s 402ms/step - loss: 2.8687 - acc: 0.3139 - top5-acc: 0.5934 - val_loss: 2.5030 - val_acc: 0.3614 - val_top5-acc: 0.6764
Epoch 11/100
704/704 [==============================] - 283s 402ms/step - loss: 2.7771 - acc: 0.3341 - top5-acc: 0.6098 - val_loss: 2.4657 - val_acc: 0.3704 - val_top5-acc: 0.6928
Epoch 12/100
704/704 [==============================] - 283s 402ms/step - loss: 2.7306 - acc: 0.3436 - top5-acc: 0.6229 - val_loss: 2.4441 - val_acc: 0.3738 - val_top5-acc: 0.6878
Epoch 13/100
704/704 [==============================] - 283s 402ms/step - loss: 2.6863 - acc: 0.3546 - top5-acc: 0.6346 - val_loss: 2.3508 - val_acc: 0.3892 - val_top5-acc: 0.7050
Epoch 14/100
704/704 [==============================] - 283s 402ms/step - loss: 2.6107 - acc: 0.3708 - top5-acc: 0.6537 - val_loss: 2.3219 - val_acc: 0.3996 - val_top5-acc: 0.7108
Epoch 15/100
704/704 [==============================] - 283s 402ms/step - loss: 2.5559 - acc: 0.3836 - top5-acc: 0.6664 - val_loss: 2.2748 - val_acc: 0.4140 - val_top5-acc: 0.7242
Epoch 16/100
704/704 [==============================] - 283s 402ms/step - loss: 2.5016 - acc: 0.3942 - top5-acc: 0.6761 - val_loss: 2.2364 - val_acc: 0.4238 - val_top5-acc: 0.7264
Epoch 17/100
704/704 [==============================] - 283s 402ms/step - loss: 2.4554 - acc: 0.4056 - top5-acc: 0.6897 - val_loss: 2.1684 - val_acc: 0.4418 - val_top5-acc: 0.7452
Epoch 18/100
704/704 [==============================] - 283s 402ms/step - loss: 2.3926 - acc: 0.4209 - top5-acc: 0.7024 - val_loss: 2.1614 - val_acc: 0.4372 - val_top5-acc: 0.7428
Epoch 19/100
704/704 [==============================] - 283s 402ms/step - loss: 2.3617 - acc: 0.4264 - top5-acc: 0.7119 - val_loss: 2.1595 - val_acc: 0.4382 - val_top5-acc: 0.7408
Epoch 20/100
704/704 [==============================] - 283s 402ms/step - loss: 2.3355 - acc: 0.4324 - top5-acc: 0.7133 - val_loss: 2.1187 - val_acc: 0.4462 - val_top5-acc: 0.7490
Epoch 21/100
704/704 [==============================] - 283s 402ms/step - loss: 2.2571 - acc: 0.4512 - top5-acc: 0.7299 - val_loss: 2.1095 - val_acc: 0.4424 - val_top5-acc: 0.7534
Epoch 22/100
704/704 [==============================] - 283s 402ms/step - loss: 2.2374 - acc: 0.4559 - top5-acc: 0.7357 - val_loss: 2.0997 - val_acc: 0.4398 - val_top5-acc: 0.7554
Epoch 23/100
704/704 [==============================] - 283s 402ms/step - loss: 2.2108 - acc: 0.4628 - top5-acc: 0.7452 - val_loss: 2.0662 - val_acc: 0.4574 - val_top5-acc: 0.7598
Epoch 24/100
704/704 [==============================] - 283s 402ms/step - loss: 2.1628 - acc: 0.4728 - top5-acc: 0.7555 - val_loss: 2.0564 - val_acc: 0.4564 - val_top5-acc: 0.7584
Epoch 25/100
704/704 [==============================] - 283s 402ms/step - loss: 2.1169 - acc: 0.4834 - top5-acc: 0.7616 - val_loss: 2.0793 - val_acc: 0.4600 - val_top5-acc: 0.7538
Epoch 26/100
704/704 [==============================] - 283s 402ms/step - loss: 2.0938 - acc: 0.4867 - top5-acc: 0.7743 - val_loss: 2.0835 - val_acc: 0.4566 - val_top5-acc: 0.7506
Epoch 27/100
704/704 [==============================] - 283s 402ms/step - loss: 2.0479 - acc: 0.4993 - top5-acc: 0.7816 - val_loss: 2.0790 - val_acc: 0.4610 - val_top5-acc: 0.7556
Epoch 28/100
704/704 [==============================] - 283s 402ms/step - loss: 1.8480 - acc: 0.5493 - top5-acc: 0.8159 - val_loss: 1.8846 - val_acc: 0.5046 - val_top5-acc: 0.7890