tf.keras.Model

TensorFlow 1 version View source on GitHub

Model groups layers into an object with training and inference features.

Inherits From: Layer

tf.keras.Model(
    *args, **kwargs
)

Used in the notebooks

Used in the guide Used in the tutorials

inputs The input(s) of the model: a keras.Input object or list of keras.Input objects.
outputs The output(s) of the model. See Functional API example below.
name String, the name of the model.

There are two ways to instantiate a Model:

1 - With the "Functional API", where you start from Input, you chain layer calls to specify the model's forward pass, and finally you create your model from inputs and outputs:

import tensorflow as tf

inputs = tf.keras.Input(shape=(3,))
x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)

2 - By subclassing the Model class: in that case, you should define your layers in __init__ and you should implement the model's forward pass in call.

import tensorflow as tf

class MyModel(tf.keras.Model):

  def __init__(self):
    super(MyModel, self).__init__()
    self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
    self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)

  def call(self, inputs):
    x = self.dense1(inputs)
    return self.dense2(x)

model = MyModel()

If you subclass Model, you can optionally have a training argument (boolean) in call, which you can use to specify a different behavior in training and inference:

import tensorflow as tf

class MyModel(tf.keras.Model):

  def __init__(self):
    super(MyModel, self).__init__()
    self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
    self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
    self.dropout = tf.keras.layers.Dropout(0.5)

  def call(self, inputs, training=False):
    x = self.dense1(inputs)
    if training:
      x = self.dropout(x, training=training)
    return self.dense2(x)

model = MyModel()

Once the model is created, you can config the model with losses and metrics with model.compile(), train the model with model.fit(), or use the model to do prediction with model.predict().

distribute_strategy The tf.distribute.Strategy this model was created under.
layers

metrics_names Returns the model's display labels for all outputs.

inputs = tf.keras.layers.Input(shape=(3,))
outputs = tf.keras.layers.Dense(2)(inputs)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
model.metrics_names
[]

x = np.random.random((2, 3))
y = np.random.randint(0, 2, (2, 2))
model.fit(x, y)
model.metrics_names
['loss', 'mae']

inputs = tf.keras.layers.Input(shape=(3,))
d = tf.keras.layers.Dense(2, name='out')
output_1 = d(inputs)
output_2 = d(inputs)
model = tf.keras.models.Model(
   inputs=inputs, outputs=[output_1, output_2])
model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
model.fit(x, (y, y))
model.metrics_names
['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
'out_1_acc']

run_eagerly 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.

Methods

compile

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compile(
    optimizer='rmsprop', loss=None, metrics=None, loss_weights=None,
    weighted_metrics=None, run_eagerly=None, **kwargs
)

Configures the model for training.

Arguments
optimizer String (name of optimizer) or optimizer instance. See tf.keras.optimizers.
loss 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.
  • sample_weight_mode for backward compatibility.

Raises
ValueError In case of invalid arguments for optimizer, loss or metrics.

evaluate

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evaluate(
    x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None,
    callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False,
    return_dict=False
)

Returns the loss value & metrics values for the model in test mode.

Computation is done in batches (see the batch_size arg.)

Arguments
x Input data. It could be:

  • A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
  • A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
  • A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
  • A tf.data dataset. Should return a tuple of either (inputs, targets) or (inputs, targets, sample_weights).
  • A generator or keras.utils.Sequence returning (inputs, targets) or (inputs, targets, sample_weights). A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given in the Unpacking behavior for iterator-like inputs section of Model.fit.
y Target data. Like the input data x, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely). If x is a dataset, generator or keras.utils.Sequence instance, y should not be specified (since targets will be obtained from the iterator/dataset).
batch_size Integer or None. Number of samples per batch of computation. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of a dataset, generators, o