def build(self, height, width, depth, filters=(32, 64), latentDim=16):
inputShape = (height, width, depth)
inputs = Input(shape=inputShape)
# loading existing model
print("Loading existing autoencoders...")
file_path = '.\models\convnetDenoisingAEgaussian.h5'
if os.path.isfile(file_path):
cae = ConvAutoencoder()
autoencoder_gaussian = cae.load_model(file_path)
file_path = '.\models\convnetDenoisingAEspeckle.h5'
if os.path.isfile(file_path):
cae = ConvAutoencoder()
autoencoder_speckle = cae.load_model(file_path)
file_path = '.\models\convnetDenoisingAEsaltAndPepper.h5'
if os.path.isfile(file_path):
cae = ConvAutoencoder()
autoencoder_saltAndPepper = cae.load_model(file_path)
file_path = '.\models\convnetDenoisingAEblock.h5'
if os.path.isfile(file_path):
cae = ConvAutoencoder()
autoencoder_block = cae.load_model(file_path)
file_path = '.\models\convnetDenoisingAEborder.h5'
if os.path.isfile(file_path):
cae = ConvAutoencoder()
autoencoder_border = cae.load_model(file_path)
file_path = '.\models\convnetDenoisingAEnoNoise.h5'
if os.path.isfile(file_path):
cae = ConvAutoencoder()
autoencoder_no_noise = cae.load_model(file_path)
#
x_gaussian = autoencoder_gaussian(inputs)
x_speckle = autoencoder_speckle(inputs)
x_saltAndPepper = autoencoder_saltAndPepper(inputs)
x_block = autoencoder_block(inputs)
x_border = autoencoder_border(inputs)
x_noNoise = autoencoder_no_noise(inputs)
# concatinating autoencoders
combined = concatenate([
x_gaussian, x_speckle, x_saltAndPepper, x_block, x_border,
x_noNoise
])
# reshaping to fit input/output
combined = Dense(1, input_shape=(6, ))(combined)
combined = Reshape((28, 28, 1))(combined)
# apply RELU => LINEAR (Don't know if this is needed)
x = Activation("relu")(combined)
x = Activation("linear")(x)
# out models is the combined autoencoders
self.model = Model(inputs=inputs, outputs=x)
return self.model
trainXNoisyBorder, testXNoisyBorder = Noises.border(
trainX[numberOfTrainX * 4:numberOfTrainX * 5],
testX[numberOfTestX * 4:numberOfTestX * 5])
trainXNoisyNoNoise, testXNoisyNoNoise = trainX[numberOfTrainX *
5:], testX[numberOfTestX *
5:]
trainXNoisy = np.concatenate(
(trainXNoisyGaussian, trainXNoisySpeckle, trainXNoisySaltAndPepper,
trainXNoisyBlock, trainXNoisyBorder, trainXNoisyNoNoise))
testXNoisy = np.concatenate(
(testXNoisyGaussian, testXNoisySpeckle, testXNoisySaltAndPepper,
testXNoisyBlock, testXNoisyBorder, testXNoisyNoNoise))
# initialize convolutional autoencoder
cae = ConvAutoencoder()
file_path = '.\models\convnetDenoisingAE' + args["noise"] + '.h5'
if os.path.isfile(file_path):
# loading existing model
print("Loading existing model...")
autoencoder = cae.load_model(file_path)
else:
# construct our convolutional autoencoder
print("[INFO] building autoencoder...")
autoencoder = cae.build(28, 28, 1, args["noise"])
opt = Adam(lr=1e-3)
autoencoder.compile(loss="mse", optimizer=opt)
# train the convolutional autoencoder
H = autoencoder.fit(trainXNoisy,
trainX,
validation_data=(testXNoisy, testX),