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Code Issues Releases Wiki Activity

adam optimizer

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prise6 2019-03-10 19:36:42 +01:00
parent 809c271a74
commit fe33b892bd
1 changed files with 6 additions and 5 deletions
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11
iss/models/SimpleAutoEncoder.py
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@ -2,7 +2,7 @@
from iss.models.AbstractModel import AbstractModel from iss.models.AbstractModel import AbstractModel
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Reshape, Flatten from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Reshape, Flatten
from keras.optimizers import Adadelta from keras.optimizers import Adadelta, Adam
from keras.models import Model from keras.models import Model
import numpy as np import numpy as np
@ -26,11 +26,11 @@ class SimpleAutoEncoder(AbstractModel):
picture = Input(shape = input_shape) picture = Input(shape = input_shape)
x = Flatten()(picture) x = Flatten()(picture)
layer_1 = Dense(2000, activation = 'relu', name = 'enc_1')(x) layer_1 = Dense(1000, activation = 'relu', name = 'enc_1')(x)
layer_2 = Dense(100, activation = 'relu', name = 'enc_2')(layer_1) layer_2 = Dense(100, activation = 'relu', name = 'enc_2')(layer_1)
layer_3 = Dense(30, activation = 'relu', name = 'enc_3')(layer_2) layer_3 = Dense(50, activation = 'relu', name = 'enc_3')(layer_2)
layer_4 = Dense(100, activation = 'relu', name = 'dec_1')(layer_3) layer_4 = Dense(100, activation = 'relu', name = 'dec_1')(layer_3)
layer_5 = Dense(2000, activation = 'relu', name = 'dec_2')(layer_4) layer_5 = Dense(1000, activation = 'relu', name = 'dec_2')(layer_4)
# encoded network # encoded network
# x = Conv2D(1, (3, 3), activation = 'relu', padding = 'same', name = 'enc_conv_1')(picture) # x = Conv2D(1, (3, 3), activation = 'relu', padding = 'same', name = 'enc_conv_1')(picture)
@ -45,6 +45,7 @@ class SimpleAutoEncoder(AbstractModel):
self.model = Model(picture, decoded) self.model = Model(picture, decoded)
optimizer = Adadelta(lr = self.lr, rho = 0.95, epsilon = None, decay = 0.0) # optimizer = Adadelta(lr = self.lr, rho = 0.95, epsilon = None, decay = 0.0)
optimizer = Adam(lr = 0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
self.model.compile(optimizer = optimizer, loss = 'binary_crossentropy') self.model.compile(optimizer = optimizer, loss = 'binary_crossentropy')