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시계열수치입력 수치 예측 모델 레시피(상태유지 스택 순환신경망 모델)Keras 2018. 1. 3. 22:09
# 1. 사용할 패키지 불러오기import keras import numpy as np from keras.models import Sequential from keras.layers import Dense, LSTM, Dropout from sklearn.preprocessing import MinMaxScaler import matplotlib.pyplot as plt %matplotlib inline # 2.함수와 클래스 만들기 def create_dataset(signal_data, look_back=1): dataX, dataY = [], [] for i in range(len(signal_data)-look_back): dataX.append(signal_data[i:(i+look_back),..
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시계열 수치입력 예측모델 레시피(상태유지 순환신경망 모델)카테고리 없음 2018. 1. 2. 23:01
# 1. 사용할 패키지 불러오기 import numpy as npfrom keras.models import Sequentialfrom keras.layers import Dense, LSTM, Dropoutfrom sklearn.preprocessing import MinMaxScalerimport matplotlib.pyplot as plt%matplotlib inline # 2. 함수와 클래스 만들기 def create_dataset(signal_data, look_back=1): dataX, dataY = [], [] for i in range(len(signal_data)-look_back): dataX.append(signal_data[i:(i+look_back), 0]) dataY.appen..
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시계열 수치입력 예측모델 레시피(순환신경망 모델)Keras 2018. 1. 2. 16:55
# 1. 사용할 패키지 불러오기 import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from keras.utils import np_utils from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, LSTM, Dropout %matplotlib inline # 2. 함수 만들기 def create_dataset(signal_data, look_back = 1): dataX, dataY = [], [] for i in range(len(signal_data) -..
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시계열 수치입력 예측모델 레시피(다층퍼셉트론 신경망 모델)Keras 2018. 1. 2. 16:25
# 1. 사용할 패키지 불러오기import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from keras.utils import np_utils from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, LSTM, Dropout %matplotlib inline # 2. 함수 만들기 def create_dataset(signal_data, look_back = 1): dataX, dataY = [], [] for i in range(len(signal_data) - ..
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영상입력 다중클래스분류모델(깊은 컨볼루션 신경망 모델)Keras 2018. 1. 2. 15:25
# 1. 사용할 패키지 불러오기import numpy as npimport matplotlib.pyplot as pltfrom keras.utils import np_utilsfrom keras.datasets import mnistfrom keras.models import Sequentialfrom keras.layers import Dense, Activationfrom keras.layers import Conv2D, MaxPooling2D, Flattenfrom keras.layers import Dropout%matplotlib inline # 2. 데이터 생성하기 width = 28 height = 28 # 훈련셋과 시험셋 불러오기 (x_train, y_train), (x_test, y_te..
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영상입력 다중클래스분류모델 레시피(컨볼루션 신경망 모델)Keras 2018. 1. 2. 12:11
# 1. 사용할 패키지 불러오기import numpy as npimport matplotlib.pyplot as pltfrom keras.utils import np_utilsfrom keras.datasets import mnistfrom keras.models import Sequentialfrom keras.layers import Dense, Activationfrom keras.layers import Conv2D, MaxPooling2D, Flatten%matplotlib inline # 2. 데이터 생성하기 width = 28 height = 28 # 훈련셋과 시험셋 불러오기 (x_train, y_train), (x_test, y_test) = mnist.load_data()x_train =..
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영상입력 다중클래스분류모델 레시피(다층퍼셉트론 신경망 모델)Keras 2018. 1. 2. 11:02
# 1. 사용할 패키지 불러오기import numpy as np import matplotlib.pyplot as plt from keras.utils import np_utils from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Activation %matplotlib inline # 2. 데이터 생성하기 width = 28 height = 28 # 훈련셋과 시험셋 불러오기 (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train.reshape(60000, width*height).astype('f..
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영상입력 이진분류모델 레시피(깊은 컨볼루션 신경망 모델)Keras 2018. 1. 2. 09:46
# 1. 사용할 패키지 불러오기import matplotlib.pyplot as plt from keras.utils import np_utils from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Activation from keras.layers import Conv2D, MaxPooling2D, Flatten from keras.layers import Dropout %matplotlib inline # 2. 데이터 생성하기 width = 28 height = 28 # 훈련셋과 시험셋 불러오기 (x_train, y_train), (x_test, y_test) = mnist..