already use model.save(). not use the load weight. the question is : self.b1 = BatchNormalization(), in my code, the BatchNormalization(), do not take any parameter, why Layer 'batch_normalization_20' expected 4 variables?
Sent by you: already use model.save(). not use the load weight. the question is : self.b1 = BatchNormalization(), in my code, the BatchNormalization(), do not take any parameter, why Layer 'batch_normalization_20' expected 4 variables?文章来源:https://www.toymoban.com/news/detail-858296.html
change to 2.15.0 , it is ok. but a lot of change on 文章来源地址https://www.toymoban.com/news/detail-858296.html
model_res_net.compile(optimizer=optimizer, # loss=[tf.keras.losses.CategoricalCrossentropy(from_logits=False)] * 4, # # metrics=['categorical_accuracy'] * 4, # metrics=[tf.keras.metrics.CategoricalAccuracy(name='categorical_accuracy'), # tf.keras.metrics.CategoricalAccuracy(name='categorical_accuracy_1'), # tf.keras.metrics.CategoricalAccuracy(name='categorical_accuracy_2'), # tf.keras.metrics.CategoricalAccuracy(name='categorical_accuracy_3')], loss={'output_1': tf.keras.losses.CategoricalCrossentropy(from_logits=False), 'output_2': tf.keras.losses.CategoricalCrossentropy(from_logits=False), 'output_3': tf.keras.losses.CategoricalCrossentropy(from_logits=False), 'output_4': tf.keras.losses.CategoricalCrossentropy(from_logits=False)}, metrics={ 'output_1': tf.keras.metrics.CategoricalAccuracy(name='acc'), 'output_2': tf.keras.metrics.CategoricalAccuracy(name='acc'), 'output_3': tf.keras.metrics.CategoricalAccuracy(name='acc'), 'output_4': tf.keras.metrics.CategoricalAccuracy(name='acc')}, loss_weights=[1.0, 1.0, 1.0, 1.0] )
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