Datasets:
HenryAI
/
KerasBERTv1-Data

Dataset card Data Studio Files Community
text
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4.99k
lr_schedule = keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate, decay_steps=100000, decay_rate=0.96, staircase=True
)
model.compile(
loss=\"binary_crossentropy\",
optimizer=keras.optimizers.Adam(learning_rate=lr_schedule),
metrics=[\"acc\"],
)
# Define callbacks.
checkpoint_cb = keras.callbacks.ModelCheckpoint(
\"3d_image_classification.h5\", save_best_only=True
)
early_stopping_cb = keras.callbacks.EarlyStopping(monitor=\"val_acc\", patience=15)
# Train the model, doing validation at the end of each epoch
epochs = 100
model.fit(
train_dataset,
validation_data=validation_dataset,
epochs=epochs,
shuffle=True,
verbose=2,
callbacks=[checkpoint_cb, early_stopping_cb],
)
Epoch 1/100
70/70 - 12s - loss: 0.7031 - acc: 0.5286 - val_loss: 1.1421 - val_acc: 0.5000
Epoch 2/100
70/70 - 12s - loss: 0.6769 - acc: 0.5929 - val_loss: 1.3491 - val_acc: 0.5000
Epoch 3/100
70/70 - 12s - loss: 0.6543 - acc: 0.6286 - val_loss: 1.5108 - val_acc: 0.5000
Epoch 4/100
70/70 - 12s - loss: 0.6236 - acc: 0.6714 - val_loss: 2.5255 - val_acc: 0.5000
Epoch 5/100
70/70 - 12s - loss: 0.6628 - acc: 0.6000 - val_loss: 1.8446 - val_acc: 0.5000
Epoch 6/100
70/70 - 12s - loss: 0.6621 - acc: 0.6071 - val_loss: 1.9661 - val_acc: 0.5000
Epoch 7/100
70/70 - 12s - loss: 0.6346 - acc: 0.6571 - val_loss: 2.8997 - val_acc: 0.5000
Epoch 8/100
70/70 - 12s - loss: 0.6501 - acc: 0.6071 - val_loss: 1.6101 - val_acc: 0.5000
Epoch 9/100
70/70 - 12s - loss: 0.6065 - acc: 0.6571 - val_loss: 0.8688 - val_acc: 0.6167
Epoch 10/100
70/70 - 12s - loss: 0.5970 - acc: 0.6714 - val_loss: 0.8802 - val_acc: 0.5167
Epoch 11/100
70/70 - 12s - loss: 0.5910 - acc: 0.7143 - val_loss: 0.7282 - val_acc: 0.6333
Epoch 12/100
70/70 - 12s - loss: 0.6147 - acc: 0.6500 - val_loss: 0.5828 - val_acc: 0.7500
Epoch 13/100
70/70 - 12s - loss: 0.5641 - acc: 0.7214 - val_loss: 0.7080 - val_acc: 0.6667
Epoch 14/100
70/70 - 12s - loss: 0.5664 - acc: 0.6857 - val_loss: 0.5641 - val_acc: 0.7000
Epoch 15/100
70/70 - 12s - loss: 0.5924 - acc: 0.6929 - val_loss: 0.7595 - val_acc: 0.6000
Epoch 16/100
70/70 - 12s - loss: 0.5389 - acc: 0.7071 - val_loss: 0.5719 - val_acc: 0.7833
Epoch 17/100
70/70 - 12s - loss: 0.5493 - acc: 0.6714 - val_loss: 0.5234 - val_acc: 0.7500
Epoch 18/100
70/70 - 12s - loss: 0.5050 - acc: 0.7786 - val_loss: 0.7359 - val_acc: 0.6000
Epoch 19/100
70/70 - 12s - loss: 0.5152 - acc: 0.7286 - val_loss: 0.6469 - val_acc: 0.6500
Epoch 20/100
70/70 - 12s - loss: 0.5015 - acc: 0.7786 - val_loss: 0.5651 - val_acc: 0.7333
Epoch 21/100
70/70 - 12s - loss: 0.4975 - acc: 0.7786 - val_loss: 0.8707 - val_acc: 0.5500
Epoch 22/100
70/70 - 12s - loss: 0.4470 - acc: 0.7714 - val_loss: 0.5577 - val_acc: 0.7500
Epoch 23/100
70/70 - 12s - loss: 0.5489 - acc: 0.7071 - val_loss: 0.9929 - val_acc: 0.6500
Epoch 24/100
70/70 - 12s - loss: 0.5045 - acc: 0.7357 - val_loss: 0.5891 - val_acc: 0.7333
Epoch 25/100
70/70 - 12s - loss: 0.5598 - acc: 0.7500 - val_loss: 0.5703 - val_acc: 0.7667
Epoch 26/100
70/70 - 12s - loss: 0.4822 - acc: 0.7429 - val_loss: 0.5631 - val_acc: 0.7333
Epoch 27/100
70/70 - 12s - loss: 0.5572 - acc: 0.7000 - val_loss: 0.6255 - val_acc: 0.6500
Epoch 28/100
70/70 - 12s - loss: 0.4694 - acc: 0.7643 - val_loss: 0.7007 - val_acc: 0.6833
Epoch 29/100
70/70 - 12s - loss: 0.4870 - acc: 0.7571 - val_loss: 1.7148 - val_acc: 0.5667
Epoch 30/100
70/70 - 12s - loss: 0.4794 - acc: 0.7500 - val_loss: 0.5744 - val_acc: 0.7333
Epoch 31/100
70/70 - 12s - loss: 0.4632 - acc: 0.7857 - val_loss: 0.7787 - val_acc: 0.5833
<tensorflow.python.keras.callbacks.History at 0x7fea600ecef0>
It is important to note that the number of samples is very small (only 200) and we don't specify a random seed. As such, you can expect significant variance in the results. The full dataset which consists of over 1000 CT scans can be found here. Using the full dataset, an accuracy of 83% was achieved. A variability of 6-7% in the classification performance is observed in both cases.
Visualizing model performance
Here the model accuracy and loss for the training and the validation sets are plotted. Since the validation set is class-balanced, accuracy provides an unbiased representation of the model's performance.
fig, ax = plt.subplots(1, 2, figsize=(20, 3))
ax = ax.ravel()
for i, metric in enumerate([\"acc\", \"loss\"]):
ax[i].plot(model.history.history[metric])
ax[i].plot(model.history.history[\"val_\" + metric])