def 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
s2, s4 = int(s/2), int(s/4)
self.z_, self.h0_w, self.h0_b = linear(z, self.gf_dim*2*s4*s4, 'g_h0_lin', with_w=True)
self.h0 = tf.reshape(self.z_, [-1, s4, s4, self.gf_dim * 2])
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, s2, s2, self.gf_dim*1], 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, s, s, self.c_dim], name='g_h2', with_w=True)
return tf.nn.tanh(h2)
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