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Created 4 years ago
from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, Dropout, Conv2DTranspose, UpSampling2D, add
from tensorflow.keras.models import Model
from tensorflow.keras import regularizersinput_img = Input(shape=(256, 256, 3))
l1 = Conv2D(64, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(input_img)
l2 = Conv2D(64, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l1)
l3 = MaxPooling2D(padding='same')(l2)
l3 = Dropout(0.3)(l3)
l4 = Conv2D(128, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l3)
l5 = Conv2D(128, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l4)
l6 = MaxPooling2D(padding='same')(l5)
l7 = Conv2D(256, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l6)
encoder = Model(input_img, l7)encoder.summary()Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 256, 256, 3)] 0
_________________________________________________________________
conv2d (Conv2D) (None, 256, 256, 64) 1792
_________________________________________________________________
conv2d_1 (Conv2D) (None, 256, 256, 64) 36928
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 128, 128, 64) 0
_________________________________________________________________
dropout (Dropout) (None, 128, 128, 64) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 128, 128, 128) 73856
_________________________________________________________________
conv2d_3 (Conv2D) (None, 128, 128, 128) 147584
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 64, 64, 128) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 64, 64, 256) 295168
=================================================================
Total params: 555,328
Trainable params: 555,328
Non-trainable params: 0
_________________________________________________________________
from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, Dropout, Conv2DTranspose, UpSampling2D, add
from tensorflow.keras.models import Model
from tensorflow.keras import regularizers
input_img = Input(shape=(256, 256, 3))
l1 = Conv2D(64, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(input_img)
l2 = Conv2D(64, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l1)
l3 = MaxPooling2D(padding='same')(l2)
l3 = Dropout(0.3)(l3)
l4 = Conv2D(128, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l3)
l5 = Conv2D(128, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l4)
l6 = MaxPooling2D(padding='same')(l5)
l7 = Conv2D(256, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l6)
l8 = UpSampling2D()(l7)
l9 = Conv2D(128, (3, 3), padding='same', activation='relu',
activity_regularizer=regularizers.l1(10e-10))(l8)
l10 = Conv2D(128, (3, 3), padding='same', activation='relu',
activity_regularizer=regularizers.l1(10e-10))(l9)
l11 = add([l5, l10])
l12 = UpSampling2D()(l11)
l13 = Conv2D(64, (3, 3), padding='same', activation='relu',
activity_regularizer=regularizers.l1(10e-10))(l12)
l14 = Conv2D(64, (3, 3), padding='same', activation='relu',
activity_regularizer=regularizers.l1(10e-10))(l13)
l15 = add([l14, l2])
decoded = Conv2D(3, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l15)
autoencoder = Model(input_img, decoded)
autoencoder_hfenn = Model(input_img, decoded)