def noise_to_image(sz, ch, rand_tensor, with_conditioning):
    batch_size = rand_tensor.shape.as_list()[0]
    output_shape = (([batch_size] + sz) + [ch])
    with tf.name_scope('noise_to_image'):
        g_no_0 = (np.prod(sz) * ch)
        fc_layer = FullyConnectedLayer(n_output_chns=g_no_0, feature_normalization=None, with_bias=True, w_initializer=self.initializers['w'], b_initializer=self.initializers['b'])
        g_h1p = fc_layer(rand_tensor, keep_prob=keep_prob_ph)
        g_h1p = tf.reshape(g_h1p, output_shape)
        g_h1p = concat_cond(g_h1p, with_conditioning)
        return conv((ch + conditioning_channels), g_h1p)
def noise_to_image(sz, ch, rand_tensor, with_conditioning):
    batch_size = rand_tensor.shape.as_list()[0]
    output_shape = (([batch_size] + sz) + [ch])
    with tf.name_scope('noise_to_image'):
        g_no_0 = (np.prod(sz) * ch)
        fc_layer = FullyConnectedLayer(n_output_chns=g_no_0, feature_normalization=None, with_bias=True, w_initializer=self.initializers['w'], b_initializer=self.initializers['b'])
        g_h1p = fc_layer(rand_tensor, keep_prob=keep_prob_ph)
        g_h1p = tf.reshape(g_h1p, output_shape)
        g_h1p = concat_cond(g_h1p, with_conditioning)
        return conv((ch + conditioning_channels), g_h1p)