def CNN_decoder(z, tsamples, nsamples, reuse=False):
with tf.variable_scope("CNN_decoder", reuse=reuse):
x = tflearn.fully_connected(z, 38 * 32, activation='tanh')
x = tf.reshape(x, shape=[-1, 1, 38, 32])
decoder = tflearn.conv_2d(x, 32, [3, 3], activation='tanh')
print(decoder.get_shape())
#decoder = tflearn.batch_normalization(decoder)
decoder = tflearn.upsample_2d(decoder, [2, 2])
print(decoder.get_shape())
decoder = tflearn.conv_2d(decoder, 64, [3, 3], activation='tanh')
print(decoder.get_shape())
#decoder = tflearn.batch_normalization(decoder)
#decoder= tflearn.upsample_2d(decoder, [3,2])
decoder = tflearn.upsample_2d(decoder, [3, 2])
print(decoder.get_shape())
decoder = tflearn.conv_2d(decoder, 64, [3, 3], activation='tanh')
print(decoder.get_shape())
#decoder = tflearn.batch_normalization(decoder)
decoder = tflearn.upsample_2d(decoder, [1, 2])
print(decoder.get_shape())
y = tflearn.conv_2d(decoder,
1, [3, 3],
activation='tanh',
name="sigout")
print(y.get_shape())
#y = tflearn.conv_2d(x, 1, [2,2], activation='relu', name="sigout0")
y = tflearn.fully_connected(y,
tsamples * nsamples,
activation="linear",
name="sigout")
#y = tf.reshape(y,[-1,tsamples,nsamples])
#x = tf.sigmoid(y)
x = y
x = tflearn.reshape(x, [-1, tsamples, nsamples], name="reshaped")
return x, y
def generator(x, reuse=False):
with tf.variable_scope('Generator', reuse=reuse):
x = tflearn.fully_connected(x, n_units=7 * 7 * 128)
x = tflearn.batch_normalization(x)
x = tf.nn.tanh(x)
x = tf.reshape(x, shape=[-1, 7, 7, 128])
x = tflearn.upsample_2d(x, 2)
x = tflearn.conv_2d(x, 64, 5, activation='tanh')
x = tflearn.upsample_2d(x, 2)
x = tflearn.conv_2d(x, 1, 5, activation='sigmoid')
return x
def net(input_image):
ratios = [32, 16, 8, 4, 2, 1]
conv_num = 8
network = []
for i in range(len(ratios)):
network.append(avg_pool_2d(input_image, ratios[i]))
# block_i_0, block_i_1, block_i_2
for block in range(3):
with tf.name_scope('block_%d_%d' % (i, block)):
filter_size = 1 if (block + 1) % 3 == 0 else 3
network[i] = tflearn.conv_2d(network[i],
nb_filter=conv_num,
filter_size=filter_size,
weights_init='xavier',
name='Conv_%d_%d' % (i, block))
network[i] = tf.nn.batch_normalization(network[i], 0, 1.0, 0.0,
1.0, 1e-5, 'BatchNorm')
network[i] = tflearn.activations.leaky_relu(network[i])
if i == 0:
network[i] = tflearn.upsample_2d(network[i], 2)
else:
upnet = tf.nn.batch_normalization(network[i - 1], 0, 1.0, 0.0, 1.0,
1e-5, 'BatchNorm')
downnet = tf.nn.batch_normalization(network[i], 0, 1.0, 0.0, 1.0,
1e-5, 'BatchNorm')
# join_i
network[i] = tflearn.merge([upnet, downnet], 'concat', axis=3)
# block_i_3, block_i_4, block_i_5
for block in range(3, 6):
with tf.name_scope('block_%d_%d' % (i, block)):
filter_size = 1 if (block + 1) % 3 == 0 else 3
network[i] = tflearn.conv_2d(network[i],
nb_filter=conv_num * i,
filter_size=filter_size,
weights_init='xavier',
name='Conv_%d_%d' %
(i, block))
network[i] = tf.nn.batch_normalization(
network[i], 0, 1.0, 0.0, 1.0, 1e-5, 'BatchNorm')
network[i] = tflearn.activations.leaky_relu(network[i])
if i != len(ratios) - 1:
network[i] = tflearn.upsample_2d(network[i], 2)
network[len(ratios) - 1] = tflearn.conv_2d(network[len(ratios) - 1],
nb_filter=3,
filter_size=1,
weights_init='xavier',
name='Conv2d_out')
return network[len(ratios) - 1]
def generator(input_image):
ratios = [16, 8, 4, 2, 1]
n_filter = 8
net = []
for i in range(len(ratios)):
net.append(tflearn.max_pool_2d(input_image, ratios[i], ratios[i]))
# block_i_0, block_i_1, block_i_2
for block in range(3):
ksize = 1 if (block + 1) % 3 == 0 else 3
net[i] = relu(batch_norm(conv2d(net[i], n_filter, ksize)))
if i != 0:
# concat with net[i-1]
upnet = batch_norm(net[i - 1])
downnet = batch_norm(net[i])
net[i] = tf.concat(3, [upnet, downnet])
# block_i_3, block_i_4, block_i_5
for block in range(3, 6):
ksize = 1 if (block + 1) % 3 == 0 else 3
net[i] = conv2d(net[i], n_filter * (i + 1), ksize)
net[i] = relu(batch_norm(net[i]))
if i != len(ratios) - 1:
net[i] = tflearn.upsample_2d(net[i], 2)
return conv2d(net[len(ratios) - 1], 3, 1)
def basic_netunit_tf(inputs, No, channel, kernel, wd, drop, down=True):
network = tflearn.conv_2d(inputs,
channel, [3, 3],
activation='relu',
regularizer='L2',
weight_decay=wd,
scope="Conv" + No + '_1')
network = tflearn.conv_2d(inputs,
channel, [3, 3],
activation='relu',
regularizer='L2',
weight_decay=wd,
scope="Conv" + No + '_2')
#conv = network
conv = tflearn.batch_normalization(network)
#conv = tf.contrib.layers.batch_norm(network, is_training=training_phase,
# decay=0.99, center=True, scale=True,
# scope='BN'+No, reuse=reuse)
if down:
pool = tflearn.max_pool_2d(conv, 2)
# pool=BatchNormalization(name='BN'+No)(pool)
else:
pool = tflearn.upsample_2d(conv, 2)
# pool= BatchNormalization(name='BN'+No)(pool)
return [conv, pool]
def generator(x, reuse=False):
with tf.variable_scope('Generator', reuse=reuse):
x = tflearn.fully_connected(x, n_units=7 * 7 * 128)
x = tf.reshape(x, shape=[-1, 7, 7, 128])
x = tflearn.upsample_2d(x, 4)
#x = tflearn.upsample_2d(x, 2)
x = tf.cast(x, tf.float32)
x = tflearn.conv_2d(x, 3, 1, activation='sigmoid')
return x
def create_network(x,y):
print('input: ',x.get_shape())
x = tf.reshape(x, [tf.shape(x)[0], height, width, 1], name='reshape_image1')
print(x)
x = tf.to_float(x)/max_dist
print(x)
print('x: ',x.get_shape())
conv1 = tflearn.conv_2d(x,n_features,patch_size,strides, padding = 'same', activation = 'leaky_relu', name='conv1')
print('conv1: ', conv1.get_shape())
maxPool1 = tflearn.layers.conv.max_pool_2d (conv1, 2, padding='same')
print('mPool1:', maxPool1.get_shape())
conv2 = tflearn.conv_2d(maxPool1,n_features,patch_size,strides, padding = 'same', activation = 'leaky_relu', name='conv2')
print('conv2: ', conv2.get_shape())
maxPool2 = tflearn.layers.conv.max_pool_2d (conv2, 2, padding='same')
print('mPool2:', maxPool2.get_shape())
conv3 = tflearn.conv_2d(maxPool2,n_features,patch_size,strides, padding = 'same', activation = 'leaky_relu', name='conv3')
print('conv3: ', conv3.get_shape())
maxPool3 = tflearn.layers.conv.max_pool_2d (conv3, 2, padding='same')
print('mPool3:', maxPool3.get_shape())
conv4 = tflearn.conv_2d(maxPool3,n_features,patch_size,strides, padding = 'same', activation = 'leaky_relu')
# print('conv4: ', conv4.get_shape())
# maxPool4 = tflearn.layers.conv.max_pool_2d (conv4, 2, padding='same')
# print('mpool4:', conv4.get_shape())
#
#
# fc1 = tflearn.fully_connected(conv3, 225*4*n_features, activation = 'leaky_relu')
fc1 = tflearn.fully_connected(conv4, 5000, activation = 'leaky_relu')
tfc1 = tflearn.fully_connected(fc1, 225*4*n_features, activation = 'leaky_relu')
print('fc1: ', fc1.get_shape())
tfc1 = tf.reshape(tfc1, [-1, 225, 4, n_features])
print('tfc1: ', tfc1.get_shape())
# last = fully_connected(tfc2, tf.transpose(weights['wfc1']), biases['b3_dec'])
# # tfc2 = tf.reshape(tfc2, [-1, 1160*2, 1, n_features])
# last = tf.reshape(last, [-1, 1160, 1, n_features])
# print('tfc3: ', last.get_shape())
# upsample1 = tflearn.upsample_2d(maxPool4,2)
# print('usamp1:', upsample1.get_shape())
# tconv1 = tflearn.conv_2d_transpose(fc1,n_features*4,patch_size,maxPool3.get_shape().as_list()[1:4], padding = 'same', activation = 'leaky_relu')
# print('tconv1:', tconv1.get_shape())
# upsample2 = tflearn.upsample_2d(tconv1,2)
# print('usamp2:', upsample2.get_shape())
tconv2 = tflearn.conv_2d_transpose(tfc1,n_features,patch_size,maxPool2.get_shape().as_list()[1:4], padding = 'same', activation = 'leaky_relu', name='deconv2')
print('tconv2:', tconv2.get_shape())
upsample3 = tflearn.upsample_2d(tconv2,2)
print('usamp3:', upsample3.get_shape())
tconv3 = tflearn.conv_2d_transpose(upsample3,n_features,patch_size,maxPool1.get_shape().as_list()[1:4], padding = 'same', activation = 'leaky_relu', name='deconv3')
print('tconv3:', tconv3.get_shape())
upsample4 = tflearn.upsample_2d(tconv3,2)
print('usamp4:', upsample4.get_shape())
tconv4 = tflearn.conv_2d_transpose(upsample4,2,patch_size,y.get_shape().as_list()[1:4], padding = 'same', activation = 'leaky_relu', name='deconv4')
print('tconv4:', tconv4.get_shape())
output = tf.nn.softmax(tf.reshape(tconv4,[-1,height,width,2]), name="softmax_tensor")
print('output:', output.get_shape())
return output
