def __call__(self, D):
with tf.variable_scope(self.name, reuse=self.reuse):
D_norm = D
G_conv1 = utils.conv2d_basic(D_norm, self.Param['G_W1'], self.Param['G_b1'])
G_relu1 = tf.nn.relu(G_conv1, name="G_relu1")
G_conv2 = utils.conv2d_basic(G_relu1, self.Param['G_W2'], self.Param['G_b2'])
G_relu2 = tf.nn.relu(G_conv2, name="G_relu2")
G_pool1 = utils.max_pool_2x2(G_relu2)
G_conv3 = utils.conv2d_basic(G_pool1, self.Param['G_W3'], self.Param['G_b3'])
G_relu3 = tf.nn.relu(G_conv3, name="G_relu3")
G_conv4 = utils.conv2d_basic(G_relu3, self.Param['G_W4'], self.Param['G_b4'])
G_relu4 = tf.nn.relu(G_conv4, name="G_relu4")
G_pool2 = utils.max_pool_2x2(G_relu4)
G_conv5 = utils.conv2d_basic(G_pool2, self.Param['G_W5'], self.Param['G_b5'])
G_relu5 = tf.nn.relu(G_conv5, name="G_relu5")
output_shape = G_relu5.get_shape().as_list()
output_shape[1] *= 2
output_shape[2] *= 2
output_shape[3] = self.Param['G_W6'].get_shape().as_list()[2]
G_rs6 = tf.image.resize_images(G_relu5, output_shape[1:3], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_conv6 = utils.conv2d_basic(G_rs6, self.Param['G_W6'], self.Param['G_b6'])
G_relu6 = tf.nn.relu(G_conv6, name="G_rs6")
output_shape = G_relu6.get_shape().as_list()
output_shape[1] *= 2
output_shape[2] *= 2
output_shape[3] = self.Param['G_W7'].get_shape().as_list()[2]
G_rs7 = tf.image.resize_images(G_relu6, output_shape[1:3], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_conv7 = utils.conv2d_basic(G_rs7, self.Param['G_W7'], self.Param['G_b7'])
G_relu7 = tf.nn.relu(G_conv7, name="G_rs7")
G_conv8 = utils.conv2d_basic(G_relu7, self.Param['G_W8'], self.Param['G_b8'])
G_relu8 = tf.nn.relu(G_conv8, name="G_relu8")
G_conv9 = utils.conv2d_basic(G_relu8, self.Param['G_W9'], self.Param['G_b9'])
Gama = tf.nn.softmax(G_conv9, name="G_latent_softmax")
return Gama
def __call__(self, D):
with tf.variable_scope(self.name, reuse=self.reuse):
D_norm = D
G_conv1 = utils.conv2d_basic(D_norm, self.Param['G_W1'], self.Param['G_b1'])
G_relu1 = tf.nn.relu(G_conv1, name="G_relu1")
G_conv2 = utils.conv2d_basic(G_relu1, self.Param['G_W2'], self.Param['G_b2'])
G_relu2 = tf.nn.relu(G_conv2, name="G_relu2")
G_pool1 = utils.max_pool_2x2(G_relu2)
G_conv3 = utils.conv2d_basic(G_pool1, self.Param['G_W3'], self.Param['G_b3'])
G_relu3 = tf.nn.relu(G_conv3, name="G_relu3")
G_conv4 = utils.conv2d_basic(G_relu3, self.Param['G_W4'], self.Param['G_b4'])
G_relu4 = tf.nn.relu(G_conv4, name="G_relu4")
G_pool2 = utils.max_pool_2x2(G_relu4)
G_conv5 = utils.conv2d_basic(G_pool2, self.Param['G_W5'], self.Param['G_b5'])
G_relu5 = tf.nn.relu(G_conv5, name="G_relu5")
output_shape = G_relu5.get_shape().as_list()
output_shape[1] *= 2
output_shape[2] *= 2
output_shape[3] = self.Param['G_W6'].get_shape().as_list()[2]
G_rs6 = tf.image.resize_images(G_relu5, output_shape[1:3], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_conv6 = utils.conv2d_basic(G_rs6, self.Param['G_W6'], self.Param['G_b6'])
G_relu6 = tf.nn.relu(G_conv6, name="G_rs6")
output_shape = G_relu6.get_shape().as_list()
output_shape[1] *= 2
output_shape[2] *= 2
output_shape[3] = self.Param['G_W7'].get_shape().as_list()[2]
G_rs7 = tf.image.resize_images(G_relu6, output_shape[1:3], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_conv7 = utils.conv2d_basic(G_rs7, self.Param['G_W7'], self.Param['G_b7'])
G_relu7 = tf.nn.relu(G_conv7, name="G_rs7")
G_conv8 = utils.conv2d_basic(G_relu7, self.Param['G_W8'], self.Param['G_b8'])
G_relu8 = tf.nn.relu(G_conv8, name="G_relu8")
G_conv9 = utils.conv2d_basic(G_relu8, self.Param['G_W9'], self.Param['G_b9'])
Gama = tf.nn.softmax(G_conv9, name="G_latent_softmax")
return Gama
def __call__(self, D):
with tf.variable_scope(self.name, reuse=self.reuse):
D_norm = D
G_conv1 = utils.conv2d_basic(D_norm, self.Param['G_W1'],
self.Param['G_b1'])
G_relu1 = tf.nn.relu(G_conv1, name="G_relu1")
G_conv2 = utils.conv2d_basic(G_relu1, self.Param['G_W2'],
self.Param['G_b2'])
G_relu2 = tf.nn.relu(G_conv2, name="G_relu2")
G_pool1 = utils.max_pool_2x2(G_relu2)
G_conv3 = utils.conv2d_basic(G_pool1, self.Param['G_W3'],
self.Param['G_b3'])
G_relu3 = tf.nn.relu(G_conv3, name="G_relu3")
G_conv4 = utils.conv2d_basic(G_relu3, self.Param['G_W4'],
self.Param['G_b4'])
G_relu4 = tf.nn.relu(G_conv4, name="G_relu4")
G_pool2 = utils.max_pool_2x2(G_relu4)
G_conv5 = utils.conv2d_basic(G_pool2, self.Param['G_W5'],
self.Param['G_b5'])
G_relu5 = tf.nn.relu(G_conv5, name="G_relu5")
G_conv6 = utils.conv2d_basic(G_relu5, self.Param['G_W6'],
self.Param['G_b6'])
G_relu6 = tf.nn.relu(G_conv6, name="G_relu6")
G_conv7 = utils.conv2d_basic(G_relu6, self.Param['G_W7'],
self.Param['G_b7'])
G_relu7 = tf.nn.relu(G_conv7, name="G_relu7")
G_pool3 = utils.max_pool_2x2(G_relu7)
G_conv8 = utils.conv2d_basic(G_pool3, self.Param['G_W8'],
self.Param['G_b8'])
G_relu8 = tf.nn.relu(G_conv8, name="G_relu8")
G_conv9 = utils.conv2d_basic(G_relu8, self.Param['G_W9'],
self.Param['G_b9'])
G_relu9 = tf.nn.relu(G_conv9, name="G_relu9")
G_conv10 = utils.conv2d_basic(G_relu9, self.Param['G_W10'],
self.Param['G_b10'])
G_relu10 = tf.nn.relu(G_conv10, name="G_relu10")
G_pool4 = utils.max_pool_2x2(G_relu10)
G_conv11 = utils.conv2d_basic(G_pool4, self.Param['G_W11'],
self.Param['G_b11'])
G_relu11 = tf.nn.relu(G_conv11, name="G_relu11")
G_conv12 = utils.conv2d_basic(G_relu11, self.Param['G_W12'],
self.Param['G_b12'])
G_relu12 = tf.nn.relu(G_conv12, name="G_relu12")
G_conv13 = utils.conv2d_basic(G_relu12, self.Param['G_W13'],
self.Param['G_b13'])
G_relu13 = tf.nn.relu(G_conv13, name="G_relu13")
G_rs14 = tf.image.resize_images(
G_relu13, [63, 63],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
#G_rs14 = tf.image.resize_images(G_relu13, [64, 64], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_cc14 = tf.concat([G_rs14, G_relu10], 3, name="G_cc14")
G_conv14 = utils.conv2d_basic(G_cc14, self.Param['G_W14'],
self.Param['G_b14'])
G_relu14 = tf.nn.relu(G_conv14, name="G_rs14")
G_rs15 = tf.image.resize_images(
G_relu14, [125, 125],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
#G_rs15 = tf.image.resize_images(G_relu14, [128, 128], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_cc15 = tf.concat([G_rs15, G_relu7], 3, name="G_cc15")
G_conv15 = utils.conv2d_basic(G_cc15, self.Param['G_W15'],
self.Param['G_b15'])
G_relu15 = tf.nn.relu(G_conv15, name="G_rs15")
G_rs16 = tf.image.resize_images(
G_relu15, [250, 250],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
#G_rs16 = tf.image.resize_images(G_relu15, [256, 256], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_cc16 = tf.concat([G_rs16, G_relu4], 3, name="G_cc16")
G_conv16 = utils.conv2d_basic(G_cc16, self.Param['G_W16'],
self.Param['G_b16'])
G_relu16 = tf.nn.relu(G_conv16, name="G_rs16")
G_rs17 = tf.image.resize_images(
G_relu16, [500, 500],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
#G_rs17 = tf.image.resize_images(G_relu16, [512, 512], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_cc17 = tf.concat([G_rs17, G_relu2], 3, name="G_cc17")
G_conv17 = utils.conv2d_basic(G_cc17, self.Param['G_W17'],
self.Param['G_b17'])
G_relu17 = tf.nn.relu(G_conv17, name="G_rs17")
G_conv18 = utils.conv2d_basic(G_relu17, self.Param['G_W18'],
self.Param['G_b18'])
G_relu18 = tf.nn.relu(G_conv18, name="G_relu18")
G_conv19 = utils.conv2d_basic(G_relu18, self.Param['G_W19'],
self.Param['G_b19'])
Gama = tf.nn.softmax(G_conv19, name="G_latent_softmax")
return Gama