여기서는 Autoencoder를 구현한다.
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from sklearn.datasets import fetch_olivetti_facesolivetti = fetch_olivetti_faces() imgs = olivetti.images
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x = 26 j = 1 for i in range(x, x+8 ): plt.subplot(2 , 4 , j) plt.imshow(imgs[i], cmap='gray' ) plt.title(i) plt.axis('off' ) j += 1 plt.tight_layout() plt.show()
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j = 1 for i in np.random.randint(len(imgs), size=8 ): plt.subplot(2 , 4 , j) plt.imshow(imgs[i], cmap='gray' ) plt.title(i) plt.axis('off' ) j += 1 plt.tight_layout() plt.show()
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X_train = imgs[:, :, :, np.newaxis] X_train.shape
(400, 64, 64, 1)
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input_shape = X_train.shape[1 :] latent_dim = 64
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from keras.layers import Input, Dropout, concatenate, Activation, Reshape, Flatten, Densefrom keras.models import Sequential, Modelfrom keras.optimizers import Adamimport keras.backend as K
Using TensorFlow backend.
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encoder = Sequential() encoder.add(Flatten(input_shape=input_shape)) encoder.add(Dense(1024 , activation="relu" )) encoder.add(Dense(latent_dim, activation="relu" )) encoder.summary() decoder = Sequential() decoder.add(Dense(1024 , activation="relu" , input_shape=(latent_dim,))) decoder.add(Dense(4096 , activation="sigmoid" )) decoder.add(Reshape(input_shape)) decoder.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten_1 (Flatten) (None, 4096) 0
_________________________________________________________________
dense_1 (Dense) (None, 1024) 4195328
_________________________________________________________________
dense_2 (Dense) (None, 64) 65600
=================================================================
Total params: 4,260,928
Trainable params: 4,260,928
Non-trainable params: 0
_________________________________________________________________
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_3 (Dense) (None, 1024) 66560
_________________________________________________________________
dense_4 (Dense) (None, 4096) 4198400
_________________________________________________________________
reshape_1 (Reshape) (None, 64, 64, 1) 0
=================================================================
Total params: 4,264,960
Trainable params: 4,264,960
Non-trainable params: 0
_________________________________________________________________
학습 모델
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inputs = Input(input_shape) encoded = encoder(inputs) decoded = decoder(encoded) model = Model(inputs, decoded) model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 64, 64, 1) 0
_________________________________________________________________
sequential_1 (Sequential) (None, 64) 4260928
_________________________________________________________________
sequential_2 (Sequential) (None, 64, 64, 1) 4264960
=================================================================
Total params: 8,525,888
Trainable params: 8,525,888
Non-trainable params: 0
_________________________________________________________________
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op = Adam(lr=0.001 , amsgrad=True ) model.compile(optimizer=op, loss='binary_crossentropy' )
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history = model.fit(X_train, X_train, batch_size=64 , epochs=100 , verbose=0 )
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plt.subplot(2 , 2 , 1 ) plt.imshow(X_train[127 ].reshape(64 , 64 ), cmap="gray" ) plt.title("원본이미지" ) plt.axis("off" ) plt.subplot(2 , 2 , 2 ) plt.imshow(model.predict( X_train[127 ][np.newaxis, :, :, :]).reshape(64 , 64 ), cmap="gray" ) plt.title("디코딩된 이미지" ) plt.axis("off" ) plt.subplot(2 , 2 , 3 ) plt.imshow(X_train[91 ].reshape(64 , 64 ), cmap="gray" ) plt.axis("off" ) plt.subplot(2 , 2 , 4 ) plt.imshow(model.predict( X_train[91 ][np.newaxis, :, :, :]).reshape(64 , 64 ), cmap="gray" ) plt.axis("off" ) plt.tight_layout() plt.show()
Vector 연산 265 - 376 + 77 ?
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X_train[[31 , 33 , 77 ]].shape
(3, 64, 64, 1)
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[a, b, c] = encoder.predict(X_train[[31 , 33 , 77 ]])
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re = (a - b) /10 + c re.shape
(64,)
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plt.subplot(221 ) plt.imshow(X_train[30 ].reshape(64 ,64 ), cmap='gray' ) plt.axis('off' ) plt.subplot(222 ) plt.imshow(X_train[33 ].reshape(64 ,64 ), cmap='gray' ) plt.axis('off' ) plt.subplot(223 ) plt.imshow(X_train[77 ].reshape(64 ,64 ), cmap='gray' ) plt.axis('off' ) plt.subplot(224 ) plt.imshow(decoder.predict(re[np.newaxis, :]).reshape(64 ,64 ), cmap='gray' ) plt.axis('off' ) plt.tight_layout() plt.show()
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plt.subplot(1 ,3 ,1 ) re_ = (b - a)/5 + c plt.imshow(decoder.predict(re_[np.newaxis, :]).reshape(64 ,64 ), cmap='gray' ) plt.title("5 devided" ) plt.axis('off' ) plt.subplot(1 ,3 ,2 ) re_ = (b - a)/2 + c plt.imshow(decoder.predict(re_[np.newaxis, :]).reshape(64 ,64 ), cmap='gray' ) plt.title("2 devided" ) plt.axis('off' ) plt.subplot(1 ,3 ,3 ) re_ = (b - a) + c plt.imshow(decoder.predict(re_[np.newaxis, :]).reshape(64 ,64 ), cmap='gray' ) plt.title("no devided" ) plt.axis('off' ) plt.tight_layout() plt.show()
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from sklearn.manifold import TSNEtsne = TSNE() code = encoder.predict(X_train) code_manifold = tsne.fit_transform(code)
encoded image Visualization
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from matplotlib.offsetbox import OffsetImage, AnnotationBboxdef (x, y, image, zoom=0.3 , ax=None) : im = OffsetImage(image, zoom=zoom, cmap='gray' ) artists = [] for x0, y0 in zip(x, y): ab = AnnotationBbox(im, (x0, y0), xycoords='data' , frameon=False ) artists.append(ax.add_artist(ab)) ax.update_datalim(np.column_stack([x, y])) ax.autoscale() return artists fig, ax = plt.subplots() for i in np.random.randint(X_train.shape[0 ], size=20 ): encoded = code_manifold[i] imscatter([encoded[0 ]], [encoded[1 ]], X_train[i].reshape(64 , 64 ), zoom=0.5 , ax=ax) plt.title("2차원으로 차원축소한 이미지" ) plt.show()
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