How to use the ops.deconv2d function in ops
To help you get started, we’ve selected a few ops examples, based on popular ways it is used in public projects.
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tolstikhin / adagan / pot.py View on Github 
opts, layer_x, _out_shape, d_h=1, d_w=1,
scope='h%d_deconv_1x1' % i)
layer_x = tf.nn.relu(layer_x)
_out_shape = [batch_size, height * scale, width * scale, num_units / scale]
layer_x = ops.deconv2d(opts, layer_x, _out_shape, scope='h%d_deconv' % i)
if opts['batch_norm']:
layer_x = ops.batch_norm(opts, layer_x, is_training, reuse, scope='bn%d' % i)
layer_x = tf.nn.relu(layer_x)
if opts['dropout']:
_keep_prob = tf.minimum(
1., 0.9 - (0.9 - keep_prob) * float(i + 1) / (num_layers - 1))
layer_x = tf.nn.dropout(layer_x, _keep_prob)
_out_shape = [batch_size] + list(output_shape)
if opts['g_arch'] == 'dcgan':
last_h = ops.deconv2d(
opts, layer_x, _out_shape, scope='hlast_deconv')
elif opts['g_arch'] == 'dcgan_mod':
last_h = ops.deconv2d(
opts, layer_x, _out_shape, d_h=1, d_w=1, scope='hlast_deconv')
else:
assert False
if opts['input_normalize_sym']:
return tf.nn.tanh(last_h)
else:
return tf.nn.sigmoid(last_h)width = output_shape[1] / 2**(num_layers-1)
else:
assert False
h0 = ops.linear(
opts, noise, num_units * height * width, scope='h0_lin')
h0 = tf.reshape(h0, [-1, height, width, num_units])
h0 = tf.nn.relu(h0)
layer_x = h0
for i in xrange(num_layers-1):
scale = 2**(i+1)
if opts['g_stride1_deconv']:
# Sylvain, I'm worried about this part!
_out_shape = [batch_size, height * scale / 2,
width * scale / 2, num_units / scale * 2]
layer_x = ops.deconv2d(
opts, layer_x, _out_shape, d_h=1, d_w=1,
scope='h%d_deconv_1x1' % i)
layer_x = tf.nn.relu(layer_x)
_out_shape = [batch_size, height * scale, width * scale, num_units / scale]
layer_x = ops.deconv2d(opts, layer_x, _out_shape, scope='h%d_deconv' % i)
if opts['batch_norm']:
layer_x = ops.batch_norm(opts, layer_x, is_training, reuse, scope='bn%d' % i)
layer_x = tf.nn.relu(layer_x)
if opts['dropout']:
_keep_prob = tf.minimum(
1., 0.9 - (0.9 - keep_prob) * float(i + 1) / (num_layers - 1))
layer_x = tf.nn.dropout(layer_x, _keep_prob)
_out_shape = [batch_size] + list(output_shape)
if opts['g_arch'] == 'dcgan':
last_h = ops.deconv2d(def G(z, scope='Generator'):
with tf.variable_scope(scope) as scope:
log.warn(scope.name)
z = tf.reshape(z, [self.batch_size, 1, 1, -1])
g_1 = deconv2d(z, deconv_info[0], is_train, name='g_1_deconv')
log.info('{} {}'.format(scope.name, g_1))
g_2 = deconv2d(g_1, deconv_info[1], is_train, name='g_2_deconv')
log.info('{} {}'.format(scope.name, g_2))
g_3 = deconv2d(g_2, deconv_info[2], is_train, name='g_3_deconv')
log.info('{} {}'.format(scope.name, g_3))
g_4 = deconv2d(g_3, deconv_info[3], is_train, name='g_4_deconv', activation_fn=tf.tanh)
log.info('{} {}'.format(scope.name, g_4))
#g_5 = deconv2d(g_4, deconv_info[4], is_train, name='g_5_deconv')
#log.info('{} {}'.format(scope.name, g_5))
#g_6 = deconv2d(g_5, deconv_info[5], is_train, name='g_6_deconv', activation_fn=tf.tanh)
#log.info('{} {}'.format(scope.name, g_6))
output = g_4
assert output.get_shape().as_list() == self.image.get_shape().as_list(), output.get_shape().as_list()
return outputdef G(z, scope='Generator'):
with tf.variable_scope(scope) as scope:
log.warn(scope.name)
z = tf.reshape(z, [self.batch_size, 1, 1, -1])
g_1 = deconv2d(z, deconv_info[0], is_train, name='g_1_deconv')
log.info('{} {}'.format(scope.name, g_1))
g_2 = deconv2d(g_1, deconv_info[1], is_train, name='g_2_deconv')
log.info('{} {}'.format(scope.name, g_2))
g_3 = deconv2d(g_2, deconv_info[2], is_train, name='g_3_deconv')
log.info('{} {}'.format(scope.name, g_3))
g_4 = deconv2d(g_3, deconv_info[3], is_train, name='g_4_deconv', activation_fn=tf.tanh)
log.info('{} {}'.format(scope.name, g_4))
#g_5 = deconv2d(g_4, deconv_info[4], is_train, name='g_5_deconv')
#log.info('{} {}'.format(scope.name, g_5))
#g_6 = deconv2d(g_5, deconv_info[5], is_train, name='g_6_deconv', activation_fn=tf.tanh)
#log.info('{} {}'.format(scope.name, g_6))
output = g_4
assert output.get_shape().as_list() == self.image.get_shape().as_list(), output.get_shape().as_list()
return outputh0 = tf.nn.relu(
self.g_bn0(linear(z, self.gfc_dim, 'g_h0_lin')))
h0 = concat([h0, y], 1)
h1 = tf.nn.relu(self.g_bn1(
linear(h0, self.gf_dim * 2 * s_h4 * s_w4, 'g_h1_lin')))
h1 = tf.reshape(h1, [self.batch_size, s_h4, s_w4, self.gf_dim * 2])
h1 = conv_cond_concat(h1, yb)
h2 = tf.nn.relu(self.g_bn2(deconv2d(h1,
[self.batch_size, s_h2, s_w2, self.gf_dim * 2], name='g_h2')))
h2 = conv_cond_concat(h2, yb)
return tf.nn.sigmoid(
deconv2d(h2, [self.batch_size, s_h, s_w, self.c_dim], name='g_h3'))def G(z, scope='Generator'):
with tf.variable_scope(scope) as scope:
log.warn(scope.name)
z = tf.reshape(z, [self.batch_size, 1, 1, -1])
g_1 = deconv2d(z, deconv_info[0], is_train, name='g_1_deconv')
log.info('{} {}'.format(scope.name, g_1))
g_2 = deconv2d(g_1, deconv_info[1], is_train, name='g_2_deconv')
log.info('{} {}'.format(scope.name, g_2))
g_3 = deconv2d(g_2, deconv_info[2], is_train, name='g_3_deconv')
log.info('{} {}'.format(scope.name, g_3))
g_4 = deconv2d(g_3, deconv_info[3], is_train, name='g_4_deconv', activation_fn=tf.tanh)
log.info('{} {}'.format(scope.name, g_4))
#g_5 = deconv2d(g_4, deconv_info[4], is_train, name='g_5_deconv')
#log.info('{} {}'.format(scope.name, g_5))
#g_6 = deconv2d(g_5, deconv_info[5], is_train, name='g_6_deconv', activation_fn=tf.tanh)
#log.info('{} {}'.format(scope.name, g_6))
output = g_4
assert output.get_shape().as_list() == self.image.get_shape().as_list(), output.get_shape().as_list()
return outputdef generator(self, z, y=None, is_train=True, reuse=False):
if reuse:
tf.get_variable_scope().reuse_variables()
s = self.output_size
if np.mod(s, 16) == 0:
s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16)
# project `z` and reshape
self.z_, self.h0_w, self.h0_b = linear(z, self.gf_dim*8*s16*s16, 'g_h0_lin', with_w=True)
self.h0 = tf.reshape(self.z_, [-1, s16, s16, self.gf_dim * 8])
h0 = tf.nn.relu(self.g_bn0(self.h0, train=is_train))
self.h1, self.h1_w, self.h1_b = deconv2d(h0,
[self.batch_size, s8, s8, self.gf_dim*4], name='g_h1', with_w=True)
h1 = tf.nn.relu(self.g_bn1(self.h1, train=is_train))
h2, self.h2_w, self.h2_b = deconv2d(h1,
[self.batch_size, s4, s4, self.gf_dim*2], name='g_h2', with_w=True)
h2 = tf.nn.relu(self.g_bn2(h2, train=is_train))
h3, self.h3_w, self.h3_b = deconv2d(h2,
[self.batch_size, s2, s2, self.gf_dim*1], name='g_h3', with_w=True)
h3 = tf.nn.relu(self.g_bn3(h3, train=is_train))
h4, self.h4_w, self.h4_b = deconv2d(h3,
[self.batch_size, s, s, self.c_dim], name='g_h4', with_w=True)
return tf.nn.tanh(h4)
else:
s = self.output_size# project `z` and reshape
h0 = tf.reshape(
linear(z, self.gf_dim * 8 * s_h16 * s_w16, 'g_h0_lin'),
[-1, s_h16, s_w16, self.gf_dim * 8])
h0 = tf.nn.relu(self.g_bn0(h0, train=False))
h1 = deconv2d(h0, [self.batch_size, s_h8, s_w8, self.gf_dim * 4], name='g_h1')
h1 = tf.nn.relu(self.g_bn1(h1, train=False))
h2 = deconv2d(h1, [self.batch_size, s_h4, s_w4, self.gf_dim * 2], name='g_h2')
h2 = tf.nn.relu(self.g_bn2(h2, train=False))
h3 = deconv2d(h2, [self.batch_size, s_h2, s_w2, self.gf_dim * 1], name='g_h3')
h3 = tf.nn.relu(self.g_bn3(h3, train=False))
h4 = deconv2d(h3, [self.batch_size, s_h, s_w, self.c_dim], name='g_h4')
return tf.nn.tanh(h4)
else:
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4 = int(s_h / 2), int(s_h / 4)
s_w2, s_w4 = int(s_w / 2), int(s_w / 4)
# yb = tf.reshape(y, [-1, 1, 1, self.y_dim])
yb = tf.reshape(y, [self.batch_size, 1, 1, self.y_dim])
z = concat([z, y], 1)
h0 = tf.nn.relu(self.g_bn0(linear(z, self.gfc_dim, 'g_h0_lin'), train=False))
h0 = concat([h0, y], 1)
h1 = tf.nn.relu(self.g_bn1(
linear(h0, self.gf_dim * 2 * s_h4 * s_w4, 'g_h1_lin'), train=False))
