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https://github.com/prise6/smart-iss-posts
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adam optimizer
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@ -2,7 +2,7 @@
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from iss.models.AbstractModel import AbstractModel
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from iss.models.AbstractModel import AbstractModel
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from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Reshape, Flatten
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from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Reshape, Flatten
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from keras.optimizers import Adadelta
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from keras.optimizers import Adadelta, Adam
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from keras.models import Model
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from keras.models import Model
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import numpy as np
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import numpy as np
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@ -26,11 +26,11 @@ class SimpleAutoEncoder(AbstractModel):
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picture = Input(shape = input_shape)
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picture = Input(shape = input_shape)
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x = Flatten()(picture)
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x = Flatten()(picture)
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layer_1 = Dense(2000, activation = 'relu', name = 'enc_1')(x)
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layer_1 = Dense(1000, activation = 'relu', name = 'enc_1')(x)
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layer_2 = Dense(100, activation = 'relu', name = 'enc_2')(layer_1)
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layer_2 = Dense(100, activation = 'relu', name = 'enc_2')(layer_1)
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layer_3 = Dense(30, activation = 'relu', name = 'enc_3')(layer_2)
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layer_3 = Dense(50, activation = 'relu', name = 'enc_3')(layer_2)
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layer_4 = Dense(100, activation = 'relu', name = 'dec_1')(layer_3)
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layer_4 = Dense(100, activation = 'relu', name = 'dec_1')(layer_3)
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layer_5 = Dense(2000, activation = 'relu', name = 'dec_2')(layer_4)
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layer_5 = Dense(1000, activation = 'relu', name = 'dec_2')(layer_4)
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# encoded network
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# encoded network
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# x = Conv2D(1, (3, 3), activation = 'relu', padding = 'same', name = 'enc_conv_1')(picture)
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# x = Conv2D(1, (3, 3), activation = 'relu', padding = 'same', name = 'enc_conv_1')(picture)
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@ -45,6 +45,7 @@ class SimpleAutoEncoder(AbstractModel):
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self.model = Model(picture, decoded)
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self.model = Model(picture, decoded)
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optimizer = Adadelta(lr = self.lr, rho = 0.95, epsilon = None, decay = 0.0)
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# optimizer = Adadelta(lr = self.lr, rho = 0.95, epsilon = None, decay = 0.0)
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optimizer = Adam(lr = 0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
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self.model.compile(optimizer = optimizer, loss = 'binary_crossentropy')
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self.model.compile(optimizer = optimizer, loss = 'binary_crossentropy')
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