The First Column | The Second Column |
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sqeue = sqeue(Input=sqeue.input, output=predictions) | bug ![]() |
bug:cv2.imshow() | ![]() |
categorical_crossentropy | ![]() |
categorical_crossentropy | ![]() |
weight_decay:权衰量,用于防止过拟合 | ![]() |
include_top=False | ![]() |
keras densenet中的 bottleneck | ![]() |
keras densenet中的 bottleneck | ![]() |
keras densenet中的 bottleneck | 通过上述简单论述我们看到使用 Bottleneck设计也可以有效减少全连接数量, 让神经网络更高效地前向传播计算。 |
keras densenet中的 reduction | ![]() |
from keras.layers import Flatten | bug:x = Flatten()(x) |
fine-tuning | url |
input_tensor | input_tensor: 可选的Keras张量 (即, layers.Input() 的输出), 用作模型的图像输入 |
bug :TypeError: call() missing 1 required positional argument: 'inputs' |
position argument:位置参数 |
bug : from keras.models import Model as Model_keras sqeue = net_densenet.DenseNetImageNet161 sqeue = sqeue (inputs=sqeue.input, outputs=predictions) |
debug : from keras.models import Model as Model_keras sqeue = net_densenet.DenseNetImageNet161 sqeue = Model_keras (inputs=sqeue.input, outputs=predictions) |
bug :from keras.models import Model as Model | debug :from keras.models import Model as Model_keras 原工程有model 文件 from model import Model |
sqeue.compile(loss='categorical_crossentropy' ) | ![]() |
常见的损失函数之 MSEBinary_crossentropycategorical_crossentropy |
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