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https://github.com/jung-geun/PSO.git
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23-10-18
setup 파일 조정
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@@ -32,13 +32,6 @@ def get_data():
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return x_train, y_train, x_test, y_test
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return x_train, y_train, x_test, y_test
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def get_data_test():
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(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
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x_test = x_test.reshape((10000, 28, 28, 1))
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return x_test, y_test
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class _batch_generator:
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class _batch_generator:
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def __init__(self, x, y, batch_size: int = 32):
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def __init__(self, x, y, batch_size: int = 32):
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self.batch_size = batch_size
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self.batch_size = batch_size
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@@ -54,21 +47,13 @@ class _batch_generator:
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return self.dataset[self.index][0], self.dataset[self.index][1]
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return self.dataset[self.index][0], self.dataset[self.index][1]
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def make_model():
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# BEGIN: 5f8d9bcejpp
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model = Sequential()
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from keras.applications.resnet50 import ResNet50
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model.add(
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Conv2D(32, kernel_size=(5, 5), activation="relu",
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input_shape=(28, 28, 1))
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)
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model.add(MaxPooling2D(pool_size=(3, 3)))
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model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Flatten())
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model.add(Dense(128, activation="relu"))
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model.add(Dense(10, activation="softmax"))
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def make_model():
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model = ResNet50(weights=None, input_shape=(28, 28, 1), classes=10)
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return model
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return model
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# END: 5f8d9bcejpp
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model = make_model()
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model = make_model()
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2
setup.py
2
setup.py
@@ -32,7 +32,7 @@ setup(
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"Programming Language :: Python :: 3 :: Only",
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"Programming Language :: Python :: 3 :: Only",
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"Programming Language :: Python :: 3.7",
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"Programming Language :: Python :: 3.7",
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"Programming Language :: Python :: 3.8",
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"Programming Language :: Python :: 3.8",
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"programming Language :: Python :: 3.9",
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"Programming Language :: Python :: 3.9",
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"Programming Language :: Python :: 3.10",
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"Programming Language :: Python :: 3.10",
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],
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],
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)
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)
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