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ro-hit81 avatar ro-hit81 commented on July 24, 2024

You can do as:

x = keras.layers.Input(shape)

resnet_arch= keras_resnet.models.ResNet50(x, classes=classes)
layer_1 = (keras.layers.Dense(11, activation='softmax', name="Dense_1"))(resnet_arch.output)
model = keras.models.Model(inputs=resnet_arch.input, outputs=layer_1)
model.compile(Adam(lr=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
model.summary() ```

from keras-resnet.

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