a SicyD @sdZddlmmZddlmZddlmZddl m Z ddl m Z ddl m Z ddlmZdd lmZdd lmZdd lmZd d ZedGddde jZeddejdddZdS)z,Input layer code (`Input` and `InputLayer`).N)backend)distributed_training_utils) base_layer) keras_tensor)node)layer_serialization)tf_utils)traceback_utils) keras_exportcCs|durtd|dS)Nz\When `type_spec` is not None, all other args except `name` must be None, but %s is not None.) ValueError)arg_nameargrT/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/engine/input_layer.py_assert_other_arg_none!s rzkeras.layers.InputLayerc s<eZdZdZejd fdd ZddZeddZ Z S) InputLayera 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). Nc  s||_||_||_||_||_||_tj} | rn|durnt | rn|| j dkrdt d || j || j }d| vr| d} |r| rt d| r| d}| dd}| rt dt| |r|rt d|sd} | d tt| }|s|dur t}n t|}n,|durB|j|krBt d |jd |tj||d d |_|rbd nd|_|rrd nd|_||_d |_t|tjrt| }nt|t!r|f}|durd|jfd|jfd|jfd|fd|jfd|jfg} | D]\}}t"||qtj#j$%st dt&'|}t|t&j(rs2          v