tf.keras.experimental.LinearModel

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Linear Model for regression and classification problems.

Inherits From: Model

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tf.compat.v1.keras.experimental.LinearModel

tf.keras.experimental.LinearModel(
    units=1, activation=None, use_bias=True, kernel_initializer='zeros',
    bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None,
    **kwargs
)

This model approximates the following function:

$$y = \beta + \sum_{i=1}^{N} w_{i} * x_{i}$$

where

$$\beta$$

is the bias and

$$w_{i}$$

is the weight for each feature.

Example:

model = LinearModel()
model.compile(optimizer='sgd', loss='mse')
model.fit(x, y, epochs=epochs)

This model accepts sparse float inputs as well:

Example:

model = LinearModel()
opt = tf.keras.optimizers.Adam()
loss_fn = tf.keras.losses.MeanSquaredError()
with tf.GradientTape() as tape:
  output = model(sparse_input)
  loss = tf.reduce_mean(loss_fn(target, output))
grads = tape.gradient(loss, model.weights)
opt.apply_gradients(zip(grads, model.weights))

Args
units	Positive integer, output dimension without the batch size.
activation	Activation function to use. If you don't specify anything, no activation is applied.
use_bias	whether to calculate the bias/intercept for this model. If set to False, no bias/intercept will be used in calculations, e.g., the data is already centered.
kernel_initializer	Initializer for the kernel weights matrices.
bias_initializer	Initializer for the bias vector.
kernel_regularizer	regularizer for kernel vectors.
bias_regularizer	regularizer for bias vector.
**kwargs	The keyword arguments that are passed on to BaseLayer.init.

Attributes
distribute_strategy	The tf.distribute.Strategy this model was created under.
layers
metrics_names	Returns the model's display labels for all outputs. Note: metrics_names are available only after a keras.Model has been trained/evaluated on actual data. 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