Datasets:
HenryAI
/
KerasBERTv1-Data

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keras.metrics.MeanAbsoluteError(),
],
[keras.metrics.CategoricalAccuracy()],
],
)
Since we gave names to our output layers, we could also specify per-output losses and metrics via a dict:
model.compile(
optimizer=keras.optimizers.RMSprop(1e-3),
loss={
"score_output": keras.losses.MeanSquaredError(),
"class_output": keras.losses.CategoricalCrossentropy(),
},
metrics={
"score_output": [
keras.metrics.MeanAbsolutePercentageError(),
keras.metrics.MeanAbsoluteError(),
],
"class_output": [keras.metrics.CategoricalAccuracy()],
},
)
We recommend the use of explicit names and dicts if you have more than 2 outputs.
It's possible to give different weights to different output-specific losses (for instance, one might wish to privilege the "score" loss in our example, by giving to 2x the importance of the class loss), using the loss_weights argument:
model.compile(
optimizer=keras.optimizers.RMSprop(1e-3),
loss={
"score_output": keras.losses.MeanSquaredError(),
"class_output": keras.losses.CategoricalCrossentropy(),
},
metrics={
"score_output": [
keras.metrics.MeanAbsolutePercentageError(),
keras.metrics.MeanAbsoluteError(),
],
"class_output": [keras.metrics.CategoricalAccuracy()],
},
loss_weights={"score_output": 2.0, "class_output": 1.0},
)
You could also choose not to compute a loss for certain outputs, if these outputs are meant for prediction but not for training:
# List loss version
model.compile(
optimizer=keras.optimizers.RMSprop(1e-3),
loss=[None, keras.losses.CategoricalCrossentropy()],
)
# Or dict loss version
model.compile(
optimizer=keras.optimizers.RMSprop(1e-3),
loss={"class_output": keras.losses.CategoricalCrossentropy()},
)
Passing data to a multi-input or multi-output model in fit() works in a similar way as specifying a loss function in compile: you can pass lists of NumPy arrays (with 1:1 mapping to the outputs that received a loss function) or dicts mapping output names to NumPy arrays.
model.compile(
optimizer=keras.optimizers.RMSprop(1e-3),
loss=[keras.losses.MeanSquaredError(), keras.losses.CategoricalCrossentropy()],
)
# Generate dummy NumPy data
img_data = np.random.random_sample(size=(100, 32, 32, 3))
ts_data = np.random.random_sample(size=(100, 20, 10))
score_targets = np.random.random_sample(size=(100, 1))
class_targets = np.random.random_sample(size=(100, 5))
# Fit on lists
model.fit([img_data, ts_data], [score_targets, class_targets], batch_size=32, epochs=1)
# Alternatively, fit on dicts
model.fit(
{"img_input": img_data, "ts_input": ts_data},
{"score_output": score_targets, "class_output": class_targets},
batch_size=32,
epochs=1,
)
4/4 [==============================] - 1s 5ms/step - loss: 13.0462 - score_output_loss: 2.7483 - class_output_loss: 10.2979
4/4 [==============================] - 0s 4ms/step - loss: 11.9004 - score_output_loss: 1.7583 - class_output_loss: 10.1420
<tensorflow.python.keras.callbacks.History at 0x14e67af50>
Here's the Dataset use case: similarly as what we did for NumPy arrays, the Dataset should return a tuple of dicts.
train_dataset = tf.data.Dataset.from_tensor_slices(
(
{"img_input": img_data, "ts_input": ts_data},
{"score_output": score_targets, "class_output": class_targets},
)
)
train_dataset = train_dataset.shuffle(buffer_size=1024).batch(64)
model.fit(train_dataset, epochs=1)
2/2 [==============================] - 0s 6ms/step - loss: 11.5102 - score_output_loss: 1.3747 - class_output_loss: 10.1355
<tensorflow.python.keras.callbacks.History at 0x14dc5ce90>
Using callbacks
Callbacks in Keras are objects that are called at different points during training (at the start of an epoch, at the end of a batch, at the end of an epoch, etc.). They can be used to implement certain behaviors, such as:
Doing validation at different points during training (beyond the built-in per-epoch validation)
Checkpointing the model at regular intervals or when it exceeds a certain accuracy threshold
Changing the learning rate of the model when training seems to be plateauing