HenryAI/KerasBERTv1-Data · Datasets at Hugging Face

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Note that this pattern does not prevent you from building models with the Functional API. You can do this whether you're building Sequential models, Functional API models, or subclassed models.

Let's see how that works.

Setup
Requires TensorFlow 2.2 or later.

import tensorflow as tf
from tensorflow import keras
A first simple example
Let's start from a simple example:

We create a new class that subclasses keras.Model.
We just override the method train_step(self, data).
We return a dictionary mapping metric names (including the loss) to their current value.
The input argument data is what gets passed to fit as training data:

If you pass Numpy arrays, by calling fit(x, y, ...), then data will be the tuple (x, y)
If you pass a tf.data.Dataset, by calling fit(dataset, ...), then data will be what gets yielded by dataset at each batch.
In the body of the train_step method, we implement a regular training update, similar to what you are already familiar with. Importantly, we compute the loss via self.compiled_loss, which wraps the loss(es) function(s) that were passed to compile().

Similarly, we call self.compiled_metrics.update_state(y, y_pred) to update the state of the metrics that were passed in compile(), and we query results from self.metrics at the end to retrieve their current value.

class CustomModel(keras.Model):
def train_step(self, data):
# Unpack the data. Its structure depends on your model and
# on what you pass to `fit()`.
x, y = data

with tf.GradientTape() as tape:
y_pred = self(x, training=True) # Forward pass
# Compute the loss value
# (the loss function is configured in `compile()`)
loss = self.compiled_loss(y, y_pred, regularization_losses=self.losses)

# Compute gradients
trainable_vars = self.trainable_variables
gradients = tape.gradient(loss, trainable_vars)
# Update weights
self.optimizer.apply_gradients(zip(gradients, trainable_vars))
# Update metrics (includes the metric that tracks the loss)
self.compiled_metrics.update_state(y, y_pred)
# Return a dict mapping metric names to current value
return {m.name: m.result() for m in self.metrics}
Let's try this out:

import numpy as np

# Construct and compile an instance of CustomModel
inputs = keras.Input(shape=(32,))
outputs = keras.layers.Dense(1)(inputs)
model = CustomModel(inputs, outputs)
model.compile(optimizer="adam", loss="mse", metrics=["mae"])

# Just use `fit` as usual
x = np.random.random((1000, 32))
y = np.random.random((1000, 1))
model.fit(x, y, epochs=3)
Epoch 1/3
32/32 [==============================] - 0s 721us/step - loss: 0.5791 - mae: 0.6232
Epoch 2/3
32/32 [==============================] - 0s 601us/step - loss: 0.2739 - mae: 0.4296
Epoch 3/3
32/32 [==============================] - 0s 576us/step - loss: 0.2547 - mae: 0.4078

<tensorflow.python.keras.callbacks.History at 0x1423856d0>
Going lower-level
Naturally, you could just skip passing a loss function in compile(), and instead do everything manually in train_step. Likewise for metrics.

Here's a lower-level example, that only uses compile() to configure the optimizer:

We start by creating Metric instances to track our loss and a MAE score.
We implement a custom train_step() that updates the state of these metrics (by calling update_state() on them), then query them (via result()) to return their current average value, to be displayed by the progress bar and to be pass to any callback.
Note that we would need to call reset_states() on our metrics between each epoch! Otherwise calling result() would return an average since the start of training, whereas we usually work with per-epoch averages. Thankfully, the framework can do that for us: just list any metric you want to reset in the metrics property of the model. The model will call reset_states() on any object listed here at the beginning of each fit() epoch or at the beginning of a call to evaluate().
loss_tracker = keras.metrics.Mean(name="loss")
mae_metric = keras.metrics.MeanAbsoluteError(name="mae")


class CustomModel(keras.Model):
def train_step(self, data):
x, y = data

with tf.GradientTape() as tape:
y_pred = self(x, training=True) # Forward pass
# Compute our own loss
loss = keras.losses.mean_squared_error(y, y_pred)

# Compute gradients
trainable_vars = self.trainable_variables
gradients = tape.gradient(loss, trainable_vars)

# Update weights
self.optimizer.apply_gradients(zip(gradients, trainable_vars))

# Compute our own metrics
loss_tracker.update_state(loss)
mae_metric.update_state(y, y_pred)
return {"loss": loss_tracker.result(), "mae": mae_metric.result()}

Note that this pattern does not prevent you from building models with the Functional API. You can do this whether you're building Sequential models, Functional API models, or subclassed models.

Let's see how that works.

Setup

Requires TensorFlow 2.2 or later.

import tensorflow as tf

from tensorflow import keras

A first simple example

Let's start from a simple example:

We create a new class that subclasses keras.Model.

We just override the method train_step(self, data).

We return a dictionary mapping metric names (including the loss) to their current value.

The input argument data is what gets passed to fit as training data:

If you pass Numpy arrays, by calling fit(x, y, ...), then data will be the tuple (x, y)

If you pass a tf.data.Dataset, by calling fit(dataset, ...), then data will be what gets yielded by dataset at each batch.

In the body of the train_step method, we implement a regular training update, similar to what you are already familiar with. Importantly, we compute the loss via self.compiled_loss, which wraps the loss(es) function(s) that were passed to compile().

Similarly, we call self.compiled_metrics.update_state(y, y_pred) to update the state of the metrics that were passed in compile(), and we query results from self.metrics at the end to retrieve their current value.

class CustomModel(keras.Model):

def train_step(self, data):

# Unpack the data. Its structure depends on your model and

# on what you pass to `fit()`.

x, y = data

with tf.GradientTape() as tape:

y_pred = self(x, training=True) # Forward pass

# Compute the loss value

# (the loss function is configured in `compile()`)

loss = self.compiled_loss(y, y_pred, regularization_losses=self.losses)

# Compute gradients

trainable_vars = self.trainable_variables

gradients = tape.gradient(loss, trainable_vars)

# Update weights

self.optimizer.apply_gradients(zip(gradients, trainable_vars))

# Update metrics (includes the metric that tracks the loss)

self.compiled_metrics.update_state(y, y_pred)

# Return a dict mapping metric names to current value

return {m.name: m.result() for m in self.metrics}

Let's try this out:

import numpy as np

# Construct and compile an instance of CustomModel

inputs = keras.Input(shape=(32,))

outputs = keras.layers.Dense(1)(inputs)

model = CustomModel(inputs, outputs)

model.compile(optimizer="adam", loss="mse", metrics=["mae"])

# Just use `fit` as usual

x = np.random.random((1000, 32))

y = np.random.random((1000, 1))

model.fit(x, y, epochs=3)

Epoch 1/3

32/32 [==============================] - 0s 721us/step - loss: 0.5791 - mae: 0.6232

Epoch 2/3

32/32 [==============================] - 0s 601us/step - loss: 0.2739 - mae: 0.4296

Epoch 3/3

32/32 [==============================] - 0s 576us/step - loss: 0.2547 - mae: 0.4078

<tensorflow.python.keras.callbacks.History at 0x1423856d0>

Going lower-level

Naturally, you could just skip passing a loss function in compile(), and instead do everything manually in train_step. Likewise for metrics.

Here's a lower-level example, that only uses compile() to configure the optimizer:

We start by creating Metric instances to track our loss and a MAE score.

We implement a custom train_step() that updates the state of these metrics (by calling update_state() on them), then query them (via result()) to return their current average value, to be displayed by the progress bar and to be pass to any callback.

Note that we would need to call reset_states() on our metrics between each epoch! Otherwise calling result() would return an average since the start of training, whereas we usually work with per-epoch averages. Thankfully, the framework can do that for us: just list any metric you want to reset in the metrics property of the model. The model will call reset_states() on any object listed here at the beginning of each fit() epoch or at the beginning of a call to evaluate().

loss_tracker = keras.metrics.Mean(name="loss")

mae_metric = keras.metrics.MeanAbsoluteError(name="mae")

class CustomModel(keras.Model):

def train_step(self, data):

x, y = data

with tf.GradientTape() as tape:

y_pred = self(x, training=True) # Forward pass

# Compute our own loss

loss = keras.losses.mean_squared_error(y, y_pred)

# Compute gradients

trainable_vars = self.trainable_variables

gradients = tape.gradient(loss, trainable_vars)

# Update weights

self.optimizer.apply_gradients(zip(gradients, trainable_vars))

# Compute our own metrics

loss_tracker.update_state(loss)

mae_metric.update_state(y, y_pred)

return {"loss": loss_tracker.result(), "mae": mae_metric.result()}