Imininingwane Overview 
 
 
 
 
 
 Ukuhlobisa Ukwakhiwa kwe-Step Izakhiwo ze-Keras Ukuvikelwa Keras amamodeli Ukuhlolwa kwe-Keras Models Qaphela ukuthi kuze kube lokhu isikhathi, akukho ukuguqulwa kwe-Keras. Ungakwazi ukwakha i-API yakho ye-high-level phezulu Futhi abantu kukhona. tf.Module Kule isigaba, uzothola indlela Keras usebenzisa . Umhlahlandlela oluphelele we-user guide ku-Keras models ingatholakala ku- . tf.Module Umhlahlandlela I-Keras layers kanye nemodeli zihlanganisa izici eziningi ezengeziwe, kuhlanganise: 
 
 
 
 
 
 Izindleko optional Ukusekela Metrics Ukusekela ku-in-built-in for an optional training argument ukuze ukwahlukanisa phakathi kokufunda nokufunda ukusetshenziswa Ukubhalisa nokuguqulwa kwezinto ze-python kunokuba kuphela izicelo ze-black-box get_config kanye izindlela from_config ezivumela ukuvikela ngokunemba ngokunemba ukuze akwazi ukulungisa imodeli ku-Python Lezi zindlela zithumela amamodeli eziningana kakhulu ngokusebenzisa i-subclassing, njenge-custom GAN noma i-Variational AutoEncoder (VAE) model. I-custom layers kanye nemodeli. Umhlahlandlela Amamodeli we-Keras iyatholakala nokusebenza okwengeziwe okwenza kube lula ukuqeqeshwa, ukulawula, ukulanda, ukugcina, futhi ngisho ukuqeqeshwa kumakhasimende amaningi. Ukuhlobisa Isigaba se-base ye-Keras layers, futhi ivela kusuka ku- . tf.keras.layers.Layer tf.Module Ungayibhalisa i-module ku-Keras layer nje ngokubhalisa i-parent bese ukuguqulwa ikhaya : __call__ call class MyDense(tf.keras.layers.Layer):
  # Adding **kwargs to support base Keras layer arguments
  def __init__(self, in_features, out_features, **kwargs):
    super().__init__(**kwargs)

    # This will soon move to the build step; see below
    self.w = tf.Variable(
      tf.random.normal([in_features, out_features]), name='w')
    self.b = tf.Variable(tf.zeros([out_features]), name='b')
  def call(self, x):
    y = tf.matmul(x, self.w) + self.b
    return tf.nn.relu(y)

simple_layer = MyDense(name="simple", in_features=3, out_features=3)
 I-Keras layers has its own Kuyinto ezinye accounting ebhalwe ku-section elandelayo futhi ke amaphuzu . Ufuna ukuba akufanele ukuphazamiseka umsebenzi. __call__ call() simple_layer([[2.0, 2.0, 2.0]])
 <tf.Tensor: shape=(1, 3), dtype=float32, numpy=array([[1.1688161, 0.       , 0.       ]], dtype=float32)>
 Waze Ngena ngemvume build Njengoba kubhalwe, kulula kwezimo eziningi ukujabulela ukuvelisa izinguquko kuze kube ngempumelelo yokufaka. Izingubo ze-Keras zihlanganisa isinyathelo se-life-cycle engaphezulu okuvumela ukunikezela ukunambitheka okwengeziwe ngokufanisa izingubo zakho. Lokhu kubhalwe ku- Ukusebenza build is a name exactly once, and it is called with the shape of the input. It is usually used to create variables (izinga). build Ngaba ushiye layer phezulu ukuba flexible ubukhulu ingxubevange yayo: MyDense class FlexibleDense(tf.keras.layers.Layer):
  # Note the added `**kwargs`, as Keras supports many arguments
  def __init__(self, out_features, **kwargs):
    super().__init__(**kwargs)
    self.out_features = out_features

  def build(self, input_shape):  # Create the state of the layer (weights)
    self.w = tf.Variable(
      tf.random.normal([input_shape[-1], self.out_features]), name='w')
    self.b = tf.Variable(tf.zeros([self.out_features]), name='b')

  def call(self, inputs):  # Defines the computation from inputs to outputs
    return tf.matmul(inputs, self.w) + self.b

# Create the instance of the layer
flexible_dense = FlexibleDense(out_features=3)
 Okwamanje, imodeli ayizakhiwa, ngakho-ke akukho izinguquko: flexible_dense.variables
 []
 Ukubalwa ifomula i-allocates ama-variables emangalisayo: # Call it, with predictably random results
print("Model results:", flexible_dense(tf.constant([[2.0, 2.0, 2.0], [3.0, 3.0, 3.0]])))
 Model results: tf.Tensor(
[[-2.531786  -5.5550847 -0.4248762]
 [-3.7976792 -8.332626  -0.6373143]], shape=(2, 3), dtype=float32)
 flexible_dense.variables
 [<tf.Variable 'flexible_dense/w:0' shape=(3, 3) dtype=float32, numpy=
 array([[-0.77719826, -1.9281565 ,  0.82326293],
        [ 0.85628736, -0.31845194,  0.10916236],
        [-1.3449821 , -0.5309338 , -1.1448634 ]], dtype=float32)>,
 <tf.Variable 'flexible_dense/b:0' shape=(3,) dtype=float32, numpy=array([0., 0., 0.], dtype=float32)>]
 ukususela ifayilishwe kuphela, inguqulo izikhwama ifakwe uma isakhiwo inguqulo akufanele izinguquko ze-layer: build try:
  print("Model results:", flexible_dense(tf.constant([[2.0, 2.0, 2.0, 2.0]])))
except tf.errors.InvalidArgumentError as e:
  print("Failed:", e)
 Failed: Exception encountered when calling layer 'flexible_dense' (type FlexibleDense).

{ {function_node __wrapped__MatMul_device_/job:localhost/replica:0/task:0/device:CPU:0} } Matrix size-incompatible: In[0]: [1,4], In[1]: [3,3] [Op:MatMul] name: 

Call arguments received by layer 'flexible_dense' (type FlexibleDense):
  • inputs=tf.Tensor(shape=(1, 4), dtype=float32)
 Izakhiwo ze-Keras Ungahambisa imodeli yakho njengezingxubevange ze-Keras. Nangona kunjalo, i-Keras inikeza isakhiwo se-model ephelele ebizwa ngokuthi Ukulungiswa kusuka ku- , ukuze imodeli ye-Keras ingasetshenziselwa nokuhlanganiswa ngokufanayo ne-Keras layers. Amamodeli we-Keras iboniswa ne-functionality engaphezulu okwenza kube lula ukuqeqeshwa, ukubuyekeza, ukubuyekeza, futhi ngisho ukuqeqeshwa kumakhasimende amaningi. tf.keras.Model tf.keras.layers.Layer Uyakwazi ukucacisa ukusuka emaphaketheni nge-coding efanayo, bese ukuguqulwa ikhaya Ukuguqulwa kwe-parent SequentialModule __call__ call() @keras.saving.register_keras_serializable()
class MySequentialModel(tf.keras.Model):
  def __init__(self, name=None, **kwargs):
    super().__init__(**kwargs)

    self.dense_1 = FlexibleDense(out_features=3)
    self.dense_2 = FlexibleDense(out_features=2)
  def call(self, x):
    x = self.dense_1(x)
    return self.dense_2(x)

# You have made a Keras model!
my_sequential_model = MySequentialModel(name="the_model")

# Call it on a tensor, with random results
print("Model results:", my_sequential_model(tf.constant([[2.0, 2.0, 2.0]])))
 Model results: tf.Tensor([[ 0.26034355 16.431221  ]], shape=(1, 2), dtype=float32)
 Zonke izici ezifanayo zitholakala, kuhlanganise ukucubungula ama-variables kanye nama-submodules. 
 
 Qaphela: I-tf.Module ebonakalayo ebonakalayo ngaphakathi kwe-Keras layer noma imodeli ayikwazi ukuthatha izinguquko zayo zihlanganiswe ukuze zihlanganiswe noma ukugcina. Ngaphandle kwalokho, izinguquko ze-Keras zihlanganisa ngaphakathi kwezinguquko ze-Keras. Qaphela: I-tf.Module ebonakalayo ebonakalayo ngaphakathi kwe-Keras layer noma imodeli ayikwazi ukuthatha izinguquko zayo zihlanganiswe ukuze zihlanganiswe noma ukugcina. Ngaphandle kwalokho, izinguquko ze-Keras zihlanganisa ngaphakathi kwezinguquko ze-Keras. my_sequential_model.variables
 [<tf.Variable 'my_sequential_model/flexible_dense_1/w:0' shape=(3, 3) dtype=float32, numpy=
 array([[ 1.4749854 ,  0.16090827,  2.2669017 ],
        [ 1.6850946 ,  1.1545411 ,  0.1707306 ],
        [ 0.8753734 , -0.13549292,  0.08751986]], dtype=float32)>,
 <tf.Variable 'my_sequential_model/flexible_dense_1/b:0' shape=(3,) dtype=float32, numpy=array([0., 0., 0.], dtype=float32)>,
 <tf.Variable 'my_sequential_model/flexible_dense_2/w:0' shape=(3, 2) dtype=float32, numpy=
 array([[-0.8022977 ,  1.9773549 ],
        [-0.76657015, -0.8485579 ],
        [ 1.6919082 ,  0.49000967]], dtype=float32)>,
 <tf.Variable 'my_sequential_model/flexible_dense_2/b:0' shape=(2,) dtype=float32, numpy=array([0., 0.], dtype=float32)>]
 my_sequential_model.submodules
 (<__main__.FlexibleDense at 0x7f790c7e0e80>,
 <__main__.FlexibleDense at 0x7f790c7e6940>)
 Ukuhlobisa is a very Pythonic approach to building TensorFlow models. Uma ungahambisa amamodeli evela ku-frameworks, lokhu kungenziwa kulula kakhulu. tf.keras.Model Uma uye ukwakha amamodeli okuzenzakalelayo ama-assemblies ye-layers kanye nama-inputs eyenziwe, ungakwazi ukucubungula isikhathi futhi indawo ngokusetshenziswa kwe-Digital , okuyinto kuhlanganisa izici ezengeziwe ezivela ku-model reconstruction kanye ne-architecture. Iphrofayili Ngiyazi i-model efanayo ne-API yokusebenza: inputs = tf.keras.Input(shape=[3,])

x = FlexibleDense(3)(inputs)
x = FlexibleDense(2)(x)

my_functional_model = tf.keras.Model(inputs=inputs, outputs=x)

my_functional_model.summary()
 Model: "model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_1 (InputLayer)        [(None, 3)]               0         
                                                                 
 flexible_dense_3 (Flexible  (None, 3)                 12        
 Dense)                                                          
                                                                 
 flexible_dense_4 (Flexible  (None, 2)                 8         
 Dense)                                                          
                                                                 
=================================================================
Total params: 20 (80.00 Byte)
Trainable params: 20 (80.00 Byte)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
 my_functional_model(tf.constant([[2.0, 2.0, 2.0]]))
 <tf.Tensor: shape=(1, 2), dtype=float32, numpy=array([[3.4276495, 2.937252 ]], dtype=float32)>
 Umbala wesikhulu lapha kuyinto ukuthi umbala wokufaka ufakwe ngaphambi njengesikhathi se-functional construction process. The inguqulo kulesi kungekho kufuneka zihlanganiswe ngokuphelele; ungahambisa ezinye izigaba njengoba . input_shape None 
 
 Qaphela: Unemibuzo ye-input_shape noma i-InputLayer ku-model e-subclassed, ama-arguments kanye nama-layers ahlabayo. Qaphela: Unemibuzo ye-input_shape noma i-InputLayer ku-model e-subclassed, ama-arguments kanye nama-layers ahlabayo. Ukuvikelwa Keras amamodeli I-Keras models has its own specialized zip archive saving format, eyenziwe ngu: ekupheleni. Xa ushiye Thumela A isixazululo se-filename. Ngokwesibonelo: .keras tf.keras.Model.save .keras my_sequential_model.save("exname_of_file.keras")
 Ngaphezu kwalokho, kungenziwa ngokushesha ku: reconstructed_model = tf.keras.models.load_model("exname_of_file.keras")
 Izixhobo zeZip - amafayela - futhi ukugcina izimo ze-metric, i-loss, ne-optimizer. .keras Uhlobo le-reconstructed ingasetshenziswa futhi uzothola imiphumela efanayo uma usebenzisa idatha efanayo: reconstructed_model(tf.constant([[2.0, 2.0, 2.0]]))
 <tf.Tensor: shape=(1, 2), dtype=float32, numpy=array([[ 0.26034355, 16.431221  ]], dtype=float32)>
 Ukuhlolwa kwe-Keras Models Keras amamodeli futhi kungenziwa checkpointed, futhi kuya kubona efanayo . tf.Module Kuhlolwe kulungile ku-Save and serialization ye-Keras models, kuhlanganise ukunikela izindlela zokufaka izindandatho ze-custom layers yokusekela izici. . Umhlahlandlela we-Save and Serialization Yini elandelayo Uma ufuna ukwazi okwengeziwe mayelana ne-Keras, ungakwazi ukuqondisa izifundo ze-Keras ezivamile . Ngiya Olunye isibonelo ye-API ephezulu eyenziwe is Sonnet kusukela DeepMind, okuyinto ifakwe ku . tf.module Site Yakho 
 
 Okwakhiwa kwebhizinisi le-TensorFlow, le ncwadi ifumaneke lapha ngaphansi kwebhizinisi elisha futhi isetshenziswe ngaphansi kwe-CC BY 4.0. Isampula se-codes ifakwe ngaphansi kwe-Apache 2.0 License. Okwakhiwa kwebhizinisi le-TensorFlow, le ncwadi ifumaneke lapha ngaphansi kwebhizinisi elisha futhi isetshenziswe ngaphansi kwe-CC BY 4.0. Isampula se-codes ifakwe ngaphansi kwe-Apache 2.0 License. Ukuhlobisa

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Ukwakhiwa, Ukuqeqesha, nokugcina imodeli Usebenzisa i-Keras ne-tf.Module

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