[英]How Can I apply Dense layer after keras ResNet?
在 ResNet50 之后如何应用 Dense 层? 这是我的代码
def build_model():
x = tf.keras.applications.ResNet50(input_shape=(IMG_WIDTH, IMG_HEIGHT, 3), weights=None,
include_top=False, pooling='avg')
model = tf.keras.layers.Dense(196)(x)
model.summary()
return model
但我收到了这个错误:
TypeError: Inputs to a layer should be tensors.
You can access the output of the model with the property output
of the model, if you are willing to recreate a model using the functional API. 在这种情况下,使用 Sequential API 可能会更容易:
new_model = tf.keras.Sequential(
[
tf.keras.applications.ResNet50(input_shape=(IMG_WIDTH, IMG_HEIGHT, 3), weights=None, include_top=False, pooling='avg'),
tf.keras.layers.Dense(196)
]
)
new_model.summary()
resnet = tf.keras.applications.ResNet50(input_shape=(IMG_WIDTH, IMG_HEIGHT, 3), weights=None, include_top=False, pooling='avg')
out = tf.keras.layers.Dense(196)(resnet.output)
new_model = tf.keras.models.Model(inputs=resnet.input, outputs=out)
new_model.summary()
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