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SJ-Keras/Proj-JetImage/models/.ipynb_checkpoints/nb_dev_capsnet-checkpoint.ipynb
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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,9 +1,7 @@ | ||
from __future__ import absolute_import | ||
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from keras4jet import losses | ||
from keras4jet import metrics | ||
from keras4jet import layers | ||
from keras4jet import models | ||
from . import layers | ||
from . import models | ||
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from keras4jet import meters | ||
from keras4jet import utils | ||
from . import meters | ||
from . import utils |
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Original file line number | Diff line number | Diff line change |
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@@ -1 +1,5 @@ | ||
from __future__ import absolute_import | ||
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from . import resnet | ||
# from . import light_resnet | ||
from . import cnn |
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Original file line number | Diff line number | Diff line change |
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@@ -0,0 +1,26 @@ | ||
from __future__ import absolute_import | ||
from __future__ import division | ||
from __future__ import print_function | ||
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import keras | ||
import keras.backend as K | ||
import tensorflow as tf | ||
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from keras.layers import Input | ||
from keras.layers import ( | ||
Conv2D, | ||
GlobalAveragePooling2D, | ||
Activation, | ||
Dense, | ||
Flatten | ||
) | ||
from keras.models import Model, Sequential | ||
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def build_a_model(input_shape, num_classes=1): | ||
model = Sequential() | ||
model.add(Flatten(input_shape=input_shape)) | ||
model.add(Dense(100, activation='relu')) | ||
model.add(Dense(1, activation='sigmoid')) | ||
return model | ||
|
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