Sequential provides training and inference features on this model.
Examples:
# Optionally, the first layer can receive an `input_shape` argument:model = tf.keras.Sequential()model.add(tf.keras.layers.Dense(8, input_shape=(16,)))# Afterwards, we do automatic shape inference:model.add(tf.keras.layers.Dense(4))
# This is identical to the following:model = tf.keras.Sequential()model.add(tf.keras.Input(shape=(16,)))model.add(tf.keras.layers.Dense(8))
# Note that you can also omit the `input_shape` argument.# In that case the model doesn't have any weights until the first call# to a training/evaluation method (since it isn't yet built):model = tf.keras.Sequential()model.add(tf.keras.layers.Dense(8))model.add(tf.keras.layers.Dense(4))# model.weights not created yet
# Whereas if you specify the input shape, the model gets built# continuously as you are adding layers:model = tf.keras.Sequential()model.add(tf.keras.layers.Dense(8, input_shape=(16,)))model.add(tf.keras.layers.Dense(4))len(model.weights)4
# When using the delayed-build pattern (no input shape specified), you can# choose to manually build your model by calling# `build(batch_input_shape)`:model = tf.keras.Sequential()model.add(tf.keras.layers.Dense(8))model.add(tf.keras.layers.Dense(4))model.build((None, 16))len(model.weights)4
# Note that when using the delayed-build pattern (no input shape specified),
# the model gets built the first time you call `fit`, `eval`, or `predict`,
# or the first time you call the model on some input data.
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(8))
model.add(tf.keras.layers.Dense(1))
model.compile(optimizer='sgd', loss='mse')
# This builds the model for the first time:
model.fit(x, y, batch_size=32, epochs=10)
Settable attribute indicating whether the model should run eagerly.
Running eagerly means that your model will be run step by step,
like Python code. Your model might run slower, but it should become easier
for you to debug it by stepping into individual layer calls.
By default, we will attempt to compile your model to a static graph to
deliver the best execution performance.
String (name of objective function), objective function or
tf.keras.losses.Loss instance. See tf.keras.losses. An objective
function is any callable with the signature loss = fn(y_true,
y_pred), where y_true = ground truth values with shape =
[batch_size, d0, .. dN], except sparse loss functions such as sparse
categorical crossentropy where shape = [batch_size, d0, .. dN-1].
y_pred = predicted values with shape = [batch_size, d0, .. dN]. It
returns a weighted loss float tensor. If a custom Loss instance is
used and reduction is set to NONE, return value has the shape
[batch_size, d0, .. dN-1] ie. per-sample or per-timestep loss values;
otherwise, it is a scalar. If the model has multiple outputs, you can
use a different loss on each output by passing a dictionary or a list
of losses. The loss value that will be minimized by the model will
then be the sum of all individual losses.
metrics
List of metrics to be evaluated by the model during training
and testing. Each of this can be a string (name of a built-in
function), function or a tf.keras.metrics.Metric instance. See
tf.keras.metrics. Typically you will use metrics=['accuracy']. A
function is any callable with the signature result = fn(y_true,
y_pred). To specify different metrics for different outputs of a
multi-output model, you could also pass a dictionary, such as
metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}.
You can also pass a list (len = len(outputs)) of lists of metrics
such as metrics=[['accuracy'], ['accuracy', 'mse']] or
metrics=['accuracy', ['accuracy', 'mse']]. When you pass the
strings 'accuracy' or 'acc', we convert this to one of
tf.keras.metrics.BinaryAccuracy,
tf.keras.metrics.CategoricalAccuracy,
tf.keras.metrics.SparseCategoricalAccuracy based on the loss
function used and the model output shape. We do a similar
conversion for the strings 'crossentropy' and 'ce' as well.
loss_weights
Optional list or dictionary specifying scalar coefficients
(Python floats) to weight the loss contributions of different model
outputs. The loss value that will be minimized by the model will then
be the weighted sum of all individual losses, weighted by the
loss_weights coefficients.
If a list, it is expected to have a 1:1 mapping to the model's
outputs. If a dict, it is expected to map output names (strings)
to scalar coefficients.
weighted_metrics
List of metrics to be evaluated and weighted by
sample_weight or class_weight during training and testing.
run_eagerly
Bool. Defaults to False. If True, this Model's
logic will not be wrapped in a tf.function. Recommended to leave
this as None unless your Model cannot be run inside a
tf.function.
**kwargs
Any additional arguments. Supported arguments:
experimental_steps_per_execution: Int. The number of batches to
run during each tf.function call. Running multiple batches
inside a single tf.function call can greatly improve performance
on TPUs or small models with a large Python overhead. Note that if
this value is set to N, Callback.on_batch methods will only be
called every N batches. This currently defaults to 1. At most,
one full epoch will be run each execution. If a number larger than
the size of the epoch is passed, the execution will be truncated
to the size of the epoch.
TensorFlow 1 version
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