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tf.keras.Sequential

TensorFlow 1 version View source on GitHub

Sequential groups a linear stack of layers into a tf.keras.Model . 

tf.keras.Sequential(
    layers=None, name=None
)

Used in the notebooks

Used in the guide Used in the tutorials

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)

layers Optional list of layers to add to the model.
name Optional name for the model.

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

add

View source 

add(
    layer
)

Adds a layer instance on top of the layer stack.

Arguments
layer layer instance.

Raises
TypeError If layer is not a layer instance.
ValueError In case the layer argument does not know its input shape.
ValueError In case the layer argument has multiple output tensors, or is already connected somewhere else (forbidden in Sequential models).

compile

View source 

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&