Datasets:
HenryAI
/
KerasBERTv1-Data

Dataset card Data Studio Files Community
text
stringlengths
0
4.99k
headers: Dictionary; optional custom HTTP headers.
send_as_json: Boolean; whether the request should be sent as "application/json".
Base Callback class
Callback class
tf.keras.callbacks.Callback()
Abstract base class used to build new callbacks.
Callbacks can be passed to keras methods such as fit, evaluate, and predict in order to hook into the various stages of the model training and inference lifecycle.
To create a custom callback, subclass keras.callbacks.Callback and override the method associated with the stage of interest. See https://www.tensorflow.org/guide/keras/custom_callback for more information.
Example
>>> training_finished = False
>>> class MyCallback(tf.keras.callbacks.Callback):
... def on_train_end(self, logs=None):
... global training_finished
... training_finished = True
>>> model = tf.keras.Sequential([tf.keras.layers.Dense(1, input_shape=(1,))])
>>> model.compile(loss='mean_squared_error')
>>> model.fit(tf.constant([[1.0]]), tf.constant([[1.0]]),
... callbacks=[MyCallback()])
>>> assert training_finished == True
Attributes
params: Dict. Training parameters (eg. verbosity, batch size, number of epochs...).
model: Instance of keras.models.Model. Reference of the model being trained.
The logs dictionary that callback methods take as argument will contain keys for quantities relevant to the current batch or epoch (see method-specific docstrings).Regression losses
MeanSquaredError class
tf.keras.losses.MeanSquaredError(reduction="auto", name="mean_squared_error")
Computes the mean of squares of errors between labels and predictions.
loss = square(y_true - y_pred)
Standalone usage:
>>> y_true = [[0., 1.], [0., 0.]]
>>> y_pred = [[1., 1.], [1., 0.]]
>>> # Using 'auto'/'sum_over_batch_size' reduction type.
>>> mse = tf.keras.losses.MeanSquaredError()
>>> mse(y_true, y_pred).numpy()
0.5
>>> # Calling with 'sample_weight'.
>>> mse(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy()
0.25
>>> # Using 'sum' reduction type.
>>> mse = tf.keras.losses.MeanSquaredError(
... reduction=tf.keras.losses.Reduction.SUM)
>>> mse(y_true, y_pred).numpy()
1.0
>>> # Using 'none' reduction type.
>>> mse = tf.keras.losses.MeanSquaredError(
... reduction=tf.keras.losses.Reduction.NONE)
>>> mse(y_true, y_pred).numpy()
array([0.5, 0.5], dtype=float32)
Usage with the compile() API:
model.compile(optimizer='sgd', loss=tf.keras.losses.MeanSquaredError())
MeanAbsoluteError class
tf.keras.losses.MeanAbsoluteError(
reduction="auto", name="mean_absolute_error"
)
Computes the mean of absolute difference between labels and predictions.
loss = abs(y_true - y_pred)
Standalone usage:
>>> y_true = [[0., 1.], [0., 0.]]
>>> y_pred = [[1., 1.], [1., 0.]]
>>> # Using 'auto'/'sum_over_batch_size' reduction type.
>>> mae = tf.keras.losses.MeanAbsoluteError()
>>> mae(y_true, y_pred).numpy()
0.5
>>> # Calling with 'sample_weight'.
>>> mae(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy()
0.25
>>> # Using 'sum' reduction type.
>>> mae = tf.keras.losses.MeanAbsoluteError(
... reduction=tf.keras.losses.Reduction.SUM)
>>> mae(y_true, y_pred).numpy()
1.0
>>> # Using 'none' reduction type.