o 2h1[@s&dZddlZddlZddlmZddlmZddlmZddlm Z ddl m Z ddl m Z dd l mZdd l mZdd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddlmZ ddl!m"Z"dZ#Gddde j$Z%ddZ&ddZ'ddZ(ddZ)dS) zHome of the `Sequential` model.N)tf2)ops) tensor_util)layers) base_layer) functional) input_layer)training_utils)model_serialization) generic_utils) layer_utils) tf_inspect)tf_utils)module) np_arrays) tf_logging)base)nestzuAll layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.cseZdZdZejd(fdd ZefddZejddZ ejd d Z ej d)d d Z e j d)fd d Zd(fdd ZddZddZd*ddZd*ddZfddZed)ddZedd Zejd!d Zed"d#Zd$d%Zfd&d'ZZS)+ Sequentiala`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) ``` Ncsttj|j|ddd|_d|_d|_d|_d|_d|_ i|_ t |_ d|_ d|_|r@t|ttfs6|g}|D] }||q8dSdS)zCreates a `Sequential` model instance. 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