File size: 2,569 Bytes
f6a67e3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
__author__ = 'Ferdinand John Briones, attempt at pix2code2 through pretrained autoencoders'
from keras.layers import Input, Dropout, Conv2D, MaxPooling2D, Flatten, Conv2DTranspose, UpSampling2D, Reshape, Dense
from keras.models import Sequential, Model
# from keras.optimizers import RMSprop
from tensorflow.keras.optimizers import RMSprop
from keras import *
from .Config import *
from .AModel import *
class autoencoder_image(AModel):
def __init__(self, input_shape, output_size, output_path):
AModel.__init__(self, input_shape, output_size, output_path)
self.name = 'autoencoder'
input_image = Input(shape=input_shape)
encoder = Conv2D(32, 3, padding='same', activation='relu')(input_image)
encoder = Conv2D(32, 3, padding='same', activation='relu')(encoder)
encoder = MaxPooling2D()(encoder)
encoder = Dropout(0.25)(encoder)
encoder = Conv2D(64, 3, padding='same', activation='relu')(encoder)
encoder = Conv2D(64, 3, padding='same', activation='relu')(encoder)
encoder = MaxPooling2D()(encoder)
encoder = Dropout(0.25)(encoder)
encoder = Conv2D(128, 3, padding='same', activation='relu')(encoder)
encoder = Conv2D(128, 3, padding='same', activation='relu')(encoder)
encoder = MaxPooling2D()(encoder)
encoded = Dropout(0.25, name='encoded_layer')(encoder)
decoder = Conv2DTranspose(128, 3, padding='same', activation='relu')(encoded)
decoder = Conv2DTranspose(128, 3, padding='same', activation='relu')(decoder)
decoder = UpSampling2D()(decoder)
decoder = Dropout(0.25)(decoder)
decoder = Conv2DTranspose(64, 3, padding='same', activation='relu')(decoder)
decoder = Conv2DTranspose(64, 3, padding='same', activation='relu')(decoder)
decoder = UpSampling2D()(decoder)
decoder = Dropout(0.25)(decoder)
decoder = Conv2DTranspose(32, 3, padding='same', activation='relu')(decoder)
decoder = Conv2DTranspose(3, 3, padding='same', activation='relu')(decoder)
decoder = UpSampling2D()(decoder)
decoded = Dropout(0.25)(decoder)
# decoder = Dense(256*256*3)(decoder)
# decoded = Reshape(target_shape=input_shape)(decoder)
self.model = Model(input_image, decoded)
self.model.compile(optimizer='adadelta', loss='binary_crossentropy')
self.model.summary()
def fit_generator(self, generator, steps_per_epoch):
self.model.fit_generator(generator, steps_per_epoch=steps_per_epoch, epochs=EPOCHS, verbose=1)
self.save()
def predict_hidden(self, images):
hidden_layer_model = Model(inputs = self.input, outputs = self.get_layer('encoded_layer').output)
return hidden_layer_model.predict(images)
|