# 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. # ============================================================================== """Input layer code (`Input` and `InputLayer`).""" import tensorflow.compat.v2 as tf from keras import backend from keras.distribute import distributed_training_utils from keras.engine import base_layer from keras.engine import keras_tensor from keras.engine import node as node_module from keras.saving.saved_model import layer_serialization from keras.utils import tf_utils from keras.utils import traceback_utils # isort: off from tensorflow.python.util.tf_export import keras_export def _assert_other_arg_none(arg_name, arg): if arg is not None: raise ValueError( "When `type_spec` is not None, all other args " "except `name` must be None, " "but %s is not None." % arg_name ) @keras_export("keras.layers.InputLayer") class InputLayer(base_layer.Layer): """Layer to be used as an entry point into a Network (a graph of layers). It can either wrap an existing tensor (pass an `input_tensor` argument) or create a placeholder tensor (pass arguments `input_shape`, and optionally, `dtype`). It is generally recommend to use the Keras Functional model via `Input`, (which creates an `InputLayer`) without directly using `InputLayer`. When using `InputLayer` with the Keras Sequential model, it can be skipped by moving the `input_shape` parameter to the first layer after the `InputLayer`. This class can create placeholders for `tf.Tensors`, `tf.SparseTensors`, and `tf.RaggedTensors` by choosing `sparse=True` or `ragged=True`. Note that `sparse` and `ragged` can't be configured to `True` at the same time. Usage: ```python # With explicit InputLayer. model = tf.keras.Sequential([ tf.keras.layers.InputLayer(input_shape=(4,)), tf.keras.layers.Dense(8)]) model.compile(tf.keras.optimizers.RMSprop(0.001), loss='mse') model.fit(np.zeros((10, 4)), np.ones((10, 8))) # Without InputLayer and let the first layer to have the input_shape. # Keras will add a input for the model behind the scene. model = tf.keras.Sequential([ tf.keras.layers.Dense(8, input_shape=(4,))]) model.compile(tf.keras.optimizers.RMSprop(0.001), loss='mse') model.fit(np.zeros((10, 4)), np.ones((10, 8))) ``` Args: input_shape: Shape tuple (not including the batch axis), or `TensorShape` instance (not including the batch axis). batch_size: Optional input batch size (integer or `None`). dtype: Optional datatype of the input. When not provided, the Keras default `float` type will be used. input_tensor: Optional tensor to use as layer input. If set, the layer will use the `tf.TypeSpec` of this tensor rather than creating a new placeholder tensor. sparse: Boolean, whether the placeholder created is meant to be sparse. Default to `False`. ragged: Boolean, whether the placeholder created is meant to be ragged. In this case, values of `None` in the `shape` argument represent ragged dimensions. For more information about `tf.RaggedTensor`, see [this guide](https://www.tensorflow.org/guide/ragged_tensor). Default to `False`. type_spec: A `tf.TypeSpec` object to create Input from. This `tf.TypeSpec` represents the entire batch. When provided, all other args except name must be `None`. name: Optional name of the layer (string). """ @traceback_utils.filter_traceback def __init__( self, input_shape=None, batch_size=None, dtype=None, input_tensor=None, sparse=None, name=None, ragged=None, type_spec=None, **kwargs, ): self._init_input_shape = input_shape self._init_batch_size = batch_size self._init_dtype = dtype self._init_sparse = sparse self._init_ragged = ragged self._init_type_spec = type_spec strategy = tf.distribute.get_strategy() if ( strategy and batch_size is not None and distributed_training_utils.global_batch_size_supported(strategy) ): if batch_size % strategy.num_replicas_in_sync != 0: raise ValueError( "The `batch_size` argument ({}) must be divisible by " "the number of replicas ({})".format( batch_size, strategy.num_replicas_in_sync ) ) batch_size = batch_size // strategy.num_replicas_in_sync if "batch_input_shape" in kwargs: batch_input_shape = kwargs.pop("batch_input_shape") if input_shape and batch_input_shape: raise ValueError( "Only provide the input_shape OR " "batch_input_shape argument to " "InputLayer, not both at the same time." ) # Set the input shape and batch size from the batch_input_shape. # Note that batch_input_shape can be None (unknown rank) or [] # (scalar), in which case the batch size must be None. if batch_input_shape: batch_size = batch_input_shape[0] input_shape = batch_input_shape[1:] if kwargs: raise ValueError( f"Unrecognized keyword arguments: {list(kwargs.keys())}" ) if sparse and ragged: raise ValueError( "Cannot set both sparse and ragged to True in a Keras input." ) if not name: prefix = "input" name = prefix + "_" + str(backend.get_uid(prefix)) if not dtype: if input_tensor is None: dtype = backend.floatx() else: dtype = backend.dtype(input_tensor) elif input_tensor is not None and input_tensor.dtype != dtype: raise ValueError( "`input_tensor.dtype` differs from `dtype`. Received: " f"input_tensor.dtype={input_tensor.dtype} " f"but expected dtype={dtype}" ) super().__init__(dtype=dtype, name=name) self.built = True self.sparse = True if sparse else False self.ragged = True if ragged else False self.batch_size = batch_size self.supports_masking = True if isinstance(input_shape, tf.TensorShape): input_shape = tuple(input_shape.as_list()) elif isinstance(input_shape, int): input_shape = (input_shape,) if type_spec is not None: args_that_must_be_none = [ ("(input_)shape", self._init_input_shape), ("batch_size", self._init_batch_size), ("dtype", self._init_dtype), ("input_tensor", input_tensor), ("sparse", self._init_sparse), ("ragged", self._init_ragged), ] for arg_name, arg in args_that_must_be_none: _assert_other_arg_none(arg_name, arg) if not tf.compat.v1.executing_eagerly_outside_functions(): raise ValueError( "Creating Keras inputs from a type_spec is only " "supported when eager execution is enabled." ) input_tensor = keras_tensor.keras_tensor_from_type_spec(type_spec) if isinstance(input_tensor, keras_tensor.SparseKerasTensor): self.sparse = True if isinstance(input_tensor, keras_tensor.RaggedKerasTensor): self.ragged = True self.is_placeholder = True try: self._batch_input_shape = tuple(input_tensor.shape.as_list()) except ValueError: # If the shape cannot be represented as a tuple (e.g. unknown # rank) self._batch_input_shape = None elif input_tensor is None: if input_shape is not None: batch_input_shape = (batch_size,) + tuple(input_shape) else: batch_input_shape = None graph = backend.get_graph() with graph.as_default(): input_tensor = backend.placeholder( shape=batch_input_shape, dtype=dtype, name=self.name, sparse=sparse, ragged=ragged, ) self.is_placeholder = True self._batch_input_shape = batch_input_shape else: if tf.compat.v1.executing_eagerly_outside_functions(): if not isinstance(input_tensor, keras_tensor.KerasTensor): input_tensor = keras_tensor.keras_tensor_from_tensor( input_tensor ) else: if not tf_utils.is_symbolic_tensor(input_tensor): raise ValueError( "You should not pass an EagerTensor to `Input`. " "For example, instead of creating an " "`InputLayer`, you should instantiate your model " "and directly call it on your input." ) self.is_placeholder = False try: self._batch_input_shape = tuple(input_tensor.shape.as_list()) except ValueError: # If the shape cannot be represented as a tuple (e.g. unknown # rank) self._batch_input_shape = None # Create an input node. input_tensor._keras_mask = None node_module.Node(layer=self, outputs=input_tensor) # Store type spec if isinstance(input_tensor, keras_tensor.KerasTensor) or ( tf_utils.is_extension_type(input_tensor) ): self._type_spec = input_tensor._type_spec else: self._type_spec = tf.TensorSpec( shape=input_tensor.shape, dtype=input_tensor.dtype, name=self.name, ) def get_config(self): if self._init_type_spec is not None: config = {"name": self.name, "type_spec": self._init_type_spec} else: config = { "batch_input_shape": self._batch_input_shape, "dtype": self.dtype, "sparse": self.sparse, "ragged": self.ragged, "name": self.name, } return config @property def _trackable_saved_model_saver(self): return layer_serialization.InputLayerSavedModelSaver(self) @keras_export("keras.Input", "keras.layers.Input") @traceback_utils.filter_traceback def Input( shape=None, batch_size=None, name=None, dtype=None, sparse=None, tensor=None, ragged=None, type_spec=None, **kwargs, ): """`Input()` is used to instantiate a Keras tensor. A Keras tensor is a symbolic tensor-like object, which we augment with certain attributes that allow us to build a Keras model just by knowing the inputs and outputs of the model. For instance, if `a`, `b` and `c` are Keras tensors, it becomes possible to do: `model = Model(input=[a, b], output=c)` Args: shape: A shape tuple (integers), not including the batch size. For instance, `shape=(32,)` indicates that the expected input will be batches of 32-dimensional vectors. Elements of this tuple can be None; 'None' elements represent dimensions where the shape is not known. batch_size: optional static batch size (integer). name: An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided. dtype: The data type expected by the input, as a string (`float32`, `float64`, `int32`...) sparse: A boolean specifying whether the placeholder to be created is sparse. Only one of 'ragged' and 'sparse' can be True. Note that, if `sparse` is False, sparse tensors can still be passed into the input - they will be densified with a default value of 0. tensor: Optional existing tensor to wrap into the `Input` layer. If set, the layer will use the `tf.TypeSpec` of this tensor rather than creating a new placeholder tensor. ragged: A boolean specifying whether the placeholder to be created is ragged. Only one of 'ragged' and 'sparse' can be True. In this case, values of 'None' in the 'shape' argument represent ragged dimensions. For more information about RaggedTensors, see [this guide](https://www.tensorflow.org/guide/ragged_tensor). type_spec: A `tf.TypeSpec` object to create the input placeholder from. When provided, all other args except name must be None. **kwargs: deprecated arguments support. Supports `batch_shape` and `batch_input_shape`. Returns: A `tensor`. Example: ```python # this is a logistic regression in Keras x = Input(shape=(32,)) y = Dense(16, activation='softmax')(x) model = Model(x, y) ``` Note that even if eager execution is enabled, `Input` produces a symbolic tensor-like object (i.e. a placeholder). This symbolic tensor-like object can be used with lower-level TensorFlow ops that take tensors as inputs, as such: ```python x = Input(shape=(32,)) y = tf.square(x) # This op will be treated like a layer model = Model(x, y) ``` (This behavior does not work for higher-order TensorFlow APIs such as control flow and being directly watched by a `tf.GradientTape`). However, the resulting model will not track any variables that were used as inputs to TensorFlow ops. All variable usages must happen within Keras layers to make sure they will be tracked by the model's weights. The Keras Input can also create a placeholder from an arbitrary `tf.TypeSpec`, e.g: ```python x = Input(type_spec=tf.RaggedTensorSpec(shape=[None, None], dtype=tf.float32, ragged_rank=1)) y = x.values model = Model(x, y) ``` When passing an arbitrary `tf.TypeSpec`, it must represent the signature of an entire batch instead of just one example. Raises: ValueError: If both `sparse` and `ragged` are provided. ValueError: If both `shape` and (`batch_input_shape` or `batch_shape`) are provided. ValueError: If `shape`, `tensor` and `type_spec` are None. ValueError: If arguments besides `type_spec` are non-None while `type_spec` is passed. ValueError: if any unrecognized parameters are provided. """ if sparse and ragged: raise ValueError( "Cannot set both `sparse` and `ragged` to `True` in a " "Keras `Input`." ) input_layer_config = { "name": name, "dtype": dtype, "sparse": sparse, "ragged": ragged, "input_tensor": tensor, "type_spec": type_spec, } batch_input_shape = kwargs.pop( "batch_input_shape", kwargs.pop("batch_shape", None) ) if shape is not None and batch_input_shape is not None: raise ValueError( "Only provide the `shape` OR `batch_input_shape` argument " "to Input, not both at the same time." ) if ( batch_input_shape is None and shape is None and tensor is None and type_spec is None ): raise ValueError( "Please provide to Input a `shape` " "or a `tensor` or a `type_spec` argument. Note that " "`shape` does not include the batch " "dimension." ) if kwargs: raise ValueError( f"Unrecognized keyword arguments: {list(kwargs.keys())}" ) if batch_input_shape: shape = batch_input_shape[1:] input_layer_config.update({"batch_input_shape": batch_input_shape}) else: input_layer_config.update( {"batch_size": batch_size, "input_shape": shape} ) input_layer = InputLayer(**input_layer_config) # Return tensor including `_keras_history`. # Note that in this case train_output and test_output are the same pointer. outputs = input_layer._inbound_nodes[0].outputs if isinstance(outputs, list) and len(outputs) == 1: return outputs[0] else: return outputs