# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # pylint: disable=protected-access """Home of the `Sequential` model. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import copy import warnings from tensorflow.python import tf2 from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import layers as layer_module from tensorflow.python.keras.engine import base_layer from tensorflow.python.keras.engine import functional from tensorflow.python.keras.engine import input_layer from tensorflow.python.keras.engine import training_utils from tensorflow.python.keras.saving.saved_model import model_serialization from tensorflow.python.keras.utils import generic_utils from tensorflow.python.keras.utils import layer_utils from tensorflow.python.keras.utils import tf_inspect from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops.numpy_ops import np_arrays from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training.tracking import base as trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export SINGLE_LAYER_OUTPUT_ERROR_MSG = ('All layers in a Sequential model should have ' 'a single output tensor. For multi-output ' 'layers, use the functional API.') @keras_export('keras.Sequential', 'keras.models.Sequential') class Sequential(functional.Functional): """`Sequential` groups a linear stack of layers into a `tf.keras.Model`. `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 ```python # 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) ``` """ @trackable.no_automatic_dependency_tracking def __init__(self, layers=None, name=None): """Creates a `Sequential` model instance. Args: layers: Optional list of layers to add to the model. name: Optional name for the model. """ # Skip the init in FunctionalModel since model doesn't have input/output yet super(functional.Functional, self).__init__( # pylint: disable=bad-super-call name=name, autocast=False) base_layer.keras_api_gauge.get_cell('Sequential').set(True) self.supports_masking = True self._compute_output_and_mask_jointly = True self._auto_track_sub_layers = False self._inferred_input_shape = None self._has_explicit_input_shape = False self._input_dtype = None self._layer_call_argspecs = {} self._created_nodes = set() # Flag that indicate whether the sequential network topology has been # created. It is false when there isn't any layer, or the layers doesn't # have input shape. self._graph_initialized = False # Unfortunately some Sequential models using custom layers or FeatureColumn # layers have multiple inputs. This is fundamentally incompatible with # most of the Sequential API, and we have to disable a number of features # for such models. self._use_legacy_deferred_behavior = False # Add to the model any layers passed to the constructor. if layers: if not isinstance(layers, (list, tuple)): layers = [layers] for layer in layers: self.add(layer) @property def layers(self): # Historically, `sequential.layers` only returns layers that were added # via `add`, and omits the auto-generated `InputLayer` that comes at the # bottom of the stack. # `Trackable` manages the `_layers` attributes and does filtering # over it. layers = super(Sequential, self).layers if layers and isinstance(layers[0], input_layer.InputLayer): return layers[1:] return layers[:] @trackable.no_automatic_dependency_tracking def add(self, 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). """ # If we are passed a Keras tensor created by keras.Input(), we can extract # the input layer from its keras history and use that without any loss of # generality. if hasattr(layer, '_keras_history'): origin_layer = layer._keras_history[0] if isinstance(origin_layer, input_layer.InputLayer): layer = origin_layer if not isinstance(layer, base_layer.Layer): raise TypeError('The added layer must be ' 'an instance of class Layer. ' 'Found: ' + str(layer)) tf_utils.assert_no_legacy_layers([layer]) if not self._is_layer_name_unique(layer): raise ValueError('All layers added to a Sequential model ' 'should have unique names. Name "%s" is already the name' ' of a layer in this model. Update the `name` argument ' 'to pass a unique name.' % (layer.name,)) self.built = False set_inputs = False if not self._layers: if isinstance(layer, input_layer.InputLayer): # Case where the user passes an Input or InputLayer layer via `add`. set_inputs = True else: batch_shape, dtype = training_utils.get_input_shape_and_dtype(layer) if batch_shape: # Instantiate an input layer. x = input_layer.Input( batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input') # This will build the current layer # and create the node connecting the current layer # to the input layer we just created. layer(x) set_inputs = True if set_inputs: outputs = nest.flatten(layer._inbound_nodes[-1].outputs) if len(outputs) != 1: raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG) self.outputs = outputs self.inputs = layer_utils.get_source_inputs(self.outputs[0]) self.built = True self._has_explicit_input_shape = True elif self.outputs: # If the model is being built continuously on top of an input layer: # refresh its output. output_tensor = layer(self.outputs[0]) if len(nest.flatten(output_tensor)) != 1: raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG) self.outputs = [output_tensor] self.built = True if set_inputs or self._graph_initialized: self._init_graph_network(self.inputs, self.outputs) self._graph_initialized = True else: self._layers.append(layer) self._handle_deferred_layer_dependencies([layer]) self._layer_call_argspecs[layer] = tf_inspect.getfullargspec(layer.call) @trackable.no_automatic_dependency_tracking def pop(self): """Removes the last layer in the model. Raises: TypeError: if there are no layers in the model. """ if not self.layers: raise TypeError('There are no layers in the model.') layer = self._layers.pop() self._layer_call_argspecs.pop(layer) if not self.layers: self.outputs = None self.inputs = None self.built = False self._inferred_input_shape = None self._has_explicit_input_shape = False self._graph_initialized = False elif self._graph_initialized: self.layers[-1]._outbound_nodes = [] self.outputs = [self.layers[-1].output] self._init_graph_network(self.inputs, self.outputs) self.built = True @trackable.no_automatic_dependency_tracking def _build_graph_network_for_inferred_shape(self, input_shape, input_dtype=None): if input_shape is None or not self.layers: return if not tf2.enabled() or not ops.executing_eagerly_outside_functions(): # This behavior is disabled in V1 or when eager execution is disabled. return if (not self._has_explicit_input_shape and not self._use_legacy_deferred_behavior): # Determine whether the input shape is novel, i.e. whether the model # should be rebuilt. input_shape = tuple(input_shape) if self._inferred_input_shape is None: new_shape = input_shape else: new_shape = relax_input_shape(self._inferred_input_shape, input_shape) if (new_shape is not None and new_shape != self._inferred_input_shape): # A novel shape has been received: we need to rebuild the model. # In case we are inside a graph function, we step out of it. with ops.init_scope(): inputs = input_layer.Input( batch_shape=new_shape, dtype=input_dtype, name=self.layers[0].name + '_input') layer_input = inputs created_nodes = set() for layer in self.layers: # Clear nodes previously created via this method. This prevents # node accumulation and ensures that e.g. `layer.output` is # always connected to `model.inputs` # (this is important e.g. for the feature extraction use case). # We don't just do `layer._inbound_nodes = []` in order # not to break shared layers added to Sequential models (which is # technically illegal as per the `add()` docstring, # but wasn't previously disabled). clear_previously_created_nodes(layer, self._created_nodes) try: # Create Functional API connection by calling the current layer layer_output = layer(layer_input) except: # pylint:disable=bare-except # Functional API calls may fail for a number of reasons: # 1) The layer may be buggy. In this case it will be easier for # the user to debug if we fail on the first call on concrete data, # instead of our own call on a symbolic input. # 2) The layer is dynamic (graph-incompatible) and hasn't # overridden `compute_output_shape`. In this case, it is # impossible to build a graph network. # 3) The layer is otherwise incompatible with the Functional API # (e.g. this is the case for some probabilistic layers that rely # on hacks and that do not return tensors). # In all these cases, we should avoid creating a graph network # (or we simply can't). self._use_legacy_deferred_behavior = True return if len(nest.flatten(layer_output)) != 1: raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG) # Keep track of nodes just created above track_nodes_created_by_last_call(layer, created_nodes) layer_input = layer_output outputs = layer_output self._created_nodes = created_nodes try: # Initialize a graph Network. This call will never fail for # a stack of valid Keras layers. # However some users have layers that are fundamentally incompatible # with the Functional API, which do not return tensors. In this # case, we fall back to the legacy deferred behavior. # TODO(fchollet): consider raising here, as we should not be # supporting such layers. self._init_graph_network(inputs, outputs) self._graph_initialized = True except: # pylint:disable=bare-except self._use_legacy_deferred_behavior = True self._inferred_input_shape = new_shape @generic_utils.default def build(self, input_shape=None): if self._graph_initialized: self._init_graph_network(self.inputs, self.outputs) else: if input_shape is None: raise ValueError('You must provide an `input_shape` argument.') self._build_graph_network_for_inferred_shape(input_shape) if not self.built: input_shape = tuple(input_shape) self._build_input_shape = input_shape super(Sequential, self).build(input_shape) self.built = True def call(self, inputs, training=None, mask=None): # pylint: disable=redefined-outer-name # If applicable, update the static input shape of the model. if not self._has_explicit_input_shape: if not tensor_util.is_tensor(inputs) and not isinstance( inputs, np_arrays.ndarray): # This is a Sequential with mutiple inputs. This is technically an # invalid use case of Sequential, but we tolerate it for backwards # compatibility. self._use_legacy_deferred_behavior = True self._build_input_shape = nest.map_structure(_get_shape_tuple, inputs) if tf2.enabled(): logging.warning('Layers in a Sequential model should only have a ' 'single input tensor, but we receive a %s input: %s' '\nConsider rewriting this model with the Functional ' 'API.' % (type(inputs), inputs)) else: self._build_graph_network_for_inferred_shape(inputs.shape, inputs.dtype) if self._graph_initialized: if not self.built: self._init_graph_network(self.inputs, self.outputs) return super(Sequential, self).call(inputs, training=training, mask=mask) outputs = inputs # handle the corner case where self.layers is empty for layer in self.layers: # During each iteration, `inputs` are the inputs to `layer`, and `outputs` # are the outputs of `layer` applied to `inputs`. At the end of each # iteration `inputs` is set to `outputs` to prepare for the next layer. kwargs = {} argspec = self._layer_call_argspecs[layer].args if 'mask' in argspec: kwargs['mask'] = mask if 'training' in argspec: kwargs['training'] = training outputs = layer(inputs, **kwargs) if len(nest.flatten(outputs)) != 1: raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG) # `outputs` will be the inputs to the next layer. inputs = outputs mask = getattr(outputs, '_keras_mask', None) return outputs def compute_output_shape(self, input_shape): shape = input_shape for layer in self.layers: shape = layer.compute_output_shape(shape) return shape def compute_mask(self, inputs, mask): # TODO(omalleyt): b/123540974 This function is not really safe to call # by itself because it will duplicate any updates and losses in graph # mode by `call`ing the Layers again. outputs = self.call(inputs, mask=mask) return getattr(outputs, '_keras_mask', None) def predict_proba(self, x, batch_size=32, verbose=0): """Generates class probability predictions for the input samples. The input samples are processed batch by batch. Arguments: x: input data, as a Numpy array or list of Numpy arrays (if the model has multiple inputs). batch_size: integer. verbose: verbosity mode, 0 or 1. Returns: A Numpy array of probability predictions. """ warnings.warn('`model.predict_proba()` is deprecated and ' 'will be removed after 2021-01-01. ' 'Please use `model.predict()` instead.') preds = self.predict(x, batch_size, verbose) if preds.min() < 0. or preds.max() > 1.: logging.warning('Network returning invalid probability values. ' 'The last layer might not normalize predictions ' 'into probabilities ' '(like softmax or sigmoid would).') return preds def predict_classes(self, x, batch_size=32, verbose=0): """Generate class predictions for the input samples. The input samples are processed batch by batch. Arguments: x: input data, as a Numpy array or list of Numpy arrays (if the model has multiple inputs). batch_size: integer. verbose: verbosity mode, 0 or 1. Returns: A numpy array of class predictions. """ warnings.warn('`model.predict_classes()` is deprecated and ' 'will be removed after 2021-01-01. ' 'Please use instead:' '* `np.argmax(model.predict(x), axis=-1)`, ' ' if your model does multi-class classification ' ' (e.g. if it uses a `softmax` last-layer activation).' '* `(model.predict(x) > 0.5).astype("int32")`, ' ' if your model does binary classification ' ' (e.g. if it uses a `sigmoid` last-layer activation).') proba = self.predict(x, batch_size=batch_size, verbose=verbose) if proba.shape[-1] > 1: return proba.argmax(axis=-1) else: return (proba > 0.5).astype('int32') def get_config(self): layer_configs = [] for layer in super(Sequential, self).layers: # `super().layers` include the InputLayer if available (it is filtered out # of `self.layers`). Note that `self._layers` is managed by the # tracking infrastructure and should not be used. layer_configs.append(generic_utils.serialize_keras_object(layer)) config = { 'name': self.name, 'layers': copy.deepcopy(layer_configs) } if not self._is_graph_network and self._build_input_shape is not None: config['build_input_shape'] = self._build_input_shape return config @classmethod def from_config(cls, config, custom_objects=None): if 'name' in config: name = config['name'] build_input_shape = config.get('build_input_shape') layer_configs = config['layers'] else: name = None build_input_shape = None layer_configs = config model = cls(name=name) for layer_config in layer_configs: layer = layer_module.deserialize(layer_config, custom_objects=custom_objects) model.add(layer) if (not model.inputs and build_input_shape and isinstance(build_input_shape, (tuple, list))): model.build(build_input_shape) return model @property def input_spec(self): if hasattr(self, '_manual_input_spec'): return self._manual_input_spec if self.layers and hasattr(self.layers[0], 'input_spec'): return self.layers[0].input_spec return None @input_spec.setter def input_spec(self, value): self._manual_input_spec = value @property def _trackable_saved_model_saver(self): return model_serialization.SequentialSavedModelSaver(self) def _is_layer_name_unique(self, layer): for ref_layer in self.layers: if layer.name == ref_layer.name and ref_layer is not layer: return False return True def _assert_weights_created(self): if self._graph_initialized: return # When the graph has not been initialized, use the Model's implementation to # to check if the weights has been created. super(functional.Functional, self)._assert_weights_created() # pylint: disable=bad-super-call def _get_shape_tuple(t): if hasattr(t, 'shape'): shape = t.shape if isinstance(shape, tuple): return shape if shape.rank is not None: return tuple(shape.as_list()) return None return None def relax_input_shape(shape_1, shape_2): if shape_1 is None or shape_2 is None: return None if len(shape_1) != len(shape_2): return None return tuple(None if d1 != d2 else d1 for d1, d2 in zip(shape_1, shape_2)) def clear_previously_created_nodes(layer, created_nodes): """Remove nodes from `created_nodes` from the layer's inbound_nodes.""" for node in layer._inbound_nodes: prev_layers = node.inbound_layers for prev_layer in nest.flatten(prev_layers): prev_layer._outbound_nodes = [ n for n in prev_layer._outbound_nodes if n not in created_nodes] layer._inbound_nodes = [ n for n in layer._inbound_nodes if n not in created_nodes] def track_nodes_created_by_last_call(layer, created_nodes): """Adds to `created_nodes` the nodes created by the last call to `layer`.""" if not layer._inbound_nodes: return created_nodes.add(layer._inbound_nodes[-1]) prev_layers = layer._inbound_nodes[-1].inbound_layers for prev_layer in nest.flatten(prev_layers): if prev_layer._outbound_nodes: created_nodes.add(prev_layer._outbound_nodes[-1])