Datasets:
HenryAI
/
KerasBERTv1-Data

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outputs = layers.Dense(10, name="predictions")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(
optimizer=keras.optimizers.RMSprop(learning_rate=1e-3),
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
)
# The displayed loss will be much higher than before
# due to the regularization component.
model.fit(x_train, y_train, batch_size=64, epochs=1)
782/782 [==============================] - 1s 828us/step - loss: 3.5361
<tensorflow.python.keras.callbacks.History at 0x14d6de210>
You can do the same for logging metric values, using add_metric():
class MetricLoggingLayer(layers.Layer):
def call(self, inputs):
# The `aggregation` argument defines
# how to aggregate the per-batch values
# over each epoch:
# in this case we simply average them.
self.add_metric(
keras.backend.std(inputs), name="std_of_activation", aggregation="mean"
)
return inputs # Pass-through layer.
inputs = keras.Input(shape=(784,), name="digits")
x = layers.Dense(64, activation="relu", name="dense_1")(inputs)
# Insert std logging as a layer.
x = MetricLoggingLayer()(x)
x = layers.Dense(64, activation="relu", name="dense_2")(x)
outputs = layers.Dense(10, name="predictions")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(
optimizer=keras.optimizers.RMSprop(learning_rate=1e-3),
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
)
model.fit(x_train, y_train, batch_size=64, epochs=1)
782/782 [==============================] - 1s 859us/step - loss: 0.5469 - std_of_activation: 0.9414
<tensorflow.python.keras.callbacks.History at 0x14d827ed0>
In the Functional API, you can also call model.add_loss(loss_tensor), or model.add_metric(metric_tensor, name, aggregation).
Here's a simple example:
inputs = keras.Input(shape=(784,), name="digits")
x1 = layers.Dense(64, activation="relu", name="dense_1")(inputs)
x2 = layers.Dense(64, activation="relu", name="dense_2")(x1)
outputs = layers.Dense(10, name="predictions")(x2)
model = keras.Model(inputs=inputs, outputs=outputs)
model.add_loss(tf.reduce_sum(x1) * 0.1)
model.add_metric(keras.backend.std(x1), name="std_of_activation", aggregation="mean")
model.compile(
optimizer=keras.optimizers.RMSprop(1e-3),
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
)
model.fit(x_train, y_train, batch_size=64, epochs=1)
782/782 [==============================] - 1s 875us/step - loss: 3.4905 - std_of_activation: 0.0019
<tensorflow.python.keras.callbacks.History at 0x14d944790>
Note that when you pass losses via add_loss(), it becomes possible to call compile() without a loss function, since the model already has a loss to minimize.
Consider the following LogisticEndpoint layer: it takes as inputs targets & logits, and it tracks a crossentropy loss via add_loss(). It also tracks classification accuracy via add_metric().
class LogisticEndpoint(keras.layers.Layer):
def __init__(self, name=None):
super(LogisticEndpoint, self).__init__(name=name)
self.loss_fn = keras.losses.BinaryCrossentropy(from_logits=True)
self.accuracy_fn = keras.metrics.BinaryAccuracy()
def call(self, targets, logits, sample_weights=None):
# Compute the training-time loss value and add it
# to the layer using `self.add_loss()`.
loss = self.loss_fn(targets, logits, sample_weights)
self.add_loss(loss)
# Log accuracy as a metric and add it
# to the layer using `self.add_metric()`.
acc = self.accuracy_fn(targets, logits, sample_weights)
self.add_metric(acc, name="accuracy")
# Return the inference-time prediction tensor (for `.predict()`).
return tf.nn.softmax(logits)
You can use it in a model with two inputs (input data & targets), compiled without a loss argument, like this:
import numpy as np
inputs = keras.Input(shape=(3,), name="inputs")
targets = keras.Input(shape=(10,), name="targets")
logits = keras.layers.Dense(10)(inputs)
predictions = LogisticEndpoint(name="predictions")(logits, targets)