Python ops.deconv2d() Examples

def __call__(self, input):
        if self._deconv_type == 'bilinear':
            from ops import bilinear_deconv2d as deconv2d
        elif self._deconv_type == 'nn':
            from ops import nn_deconv2d as deconv2d
        elif self._deconv_type == 'transpose':
            from ops import deconv2d
        else:
            raise NotImplementedError
        with tf.variable_scope(self.name, reuse=self._reuse):
            if not self._reuse:
                print('\033[93m'+self.name+'\033[0m')
            _ = tf.reshape(input, [input.get_shape().as_list()[0], 1, 1, -1])
            _ = fc(_, 1024, self._is_train, info=not self._reuse, norm='None', name='fc')
            for i in range(int(np.ceil(np.log2(max(self._h, self._w))))):
                _ = deconv2d(_, max(self._c, int(_.get_shape().as_list()[-1]/2)), 
                             self._is_train, info=not self._reuse, norm=self._norm_type,
                             name='deconv{}'.format(i+1))
            _ = deconv2d(_, self._c, self._is_train, k=1, s=1, info=not self._reuse,
                         activation_fn=tf.tanh, norm='None',
                         name='deconv{}'.format(i+2))
            _ = tf.image.resize_bilinear(_, [self._h, self._w])

            self._reuse = True
            self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.name)
            return _ 

def __init__(self, z_size, channel, resnet = False, output_size = 32):
        super(Generator, self).__init__()
        s = 4
        self.output_size = output_size
        if self.output_size == 32:
            s = 4
        if self.output_size == 48:
            s = 6
        self.s = s
        self.z_size = z_size
        self.resnet = resnet
        self.fully_connect = nn.Linear(z_size, s*s*256)
        self.relu = nn.ReLU()
        self.tanh = nn.Tanh()

        self.deconv1 = deconv2d(256, 256, padding = 0)
        self.bn1 = nn.BatchNorm2d(256)
        self.deconv2 = deconv2d(256, 128, padding = 0) 
        self.bn2 = nn.BatchNorm2d(128)
        self.deconv3 = deconv2d(128, 64, padding = 0)
        self.bn3 = nn.BatchNorm2d(64)
        self.conv4 = conv2d(64, channel, padding = 1, kernel_size = 3, stride = 1)
        self.conv_res4 = conv2d(256,channel, padding = 1, kernel_size = 3, stride = 1)

        self.re1 = Residual_G(256, 256, up_sampling = True)
        self.re2 = Residual_G(256, 256, up_sampling = True)
        self.re3 = Residual_G(256, 256, up_sampling = True)
        self.bn = nn.BatchNorm2d(256) 

def generator(hparams, z, scope_name, train, reuse):

    with tf.variable_scope(scope_name) as scope:
        if reuse:
            scope.reuse_variables()

        output_size = 64
        s = output_size
        s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16)

        g_bn0 = ops.batch_norm(name='g_bn0')
        g_bn1 = ops.batch_norm(name='g_bn1')
        g_bn2 = ops.batch_norm(name='g_bn2')
        g_bn3 = ops.batch_norm(name='g_bn3')

        # project `z` and reshape
        h0 = tf.reshape(ops.linear(z, hparams.gf_dim*8*s16*s16, 'g_h0_lin'), [-1, s16, s16, hparams.gf_dim * 8])
        h0 = tf.nn.relu(g_bn0(h0, train=train))

        h1 = ops.deconv2d(h0, [hparams.batch_size, s8, s8, hparams.gf_dim*4], name='g_h1')
        h1 = tf.nn.relu(g_bn1(h1, train=train))

        h2 = ops.deconv2d(h1, [hparams.batch_size, s4, s4, hparams.gf_dim*2], name='g_h2')
        h2 = tf.nn.relu(g_bn2(h2, train=train))

        h3 = ops.deconv2d(h2, [hparams.batch_size, s2, s2, hparams.gf_dim*1], name='g_h3')
        h3 = tf.nn.relu(g_bn3(h3, train=train))

        h4 = ops.deconv2d(h3, [hparams.batch_size, s, s, hparams.c_dim], name='g_h4')
        x_gen = tf.nn.tanh(h4)

    return x_gen 

def generator(hparams, z, train, reuse):

    if reuse:
        tf.get_variable_scope().reuse_variables()

    output_size = 64
    s = output_size
    s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16)

    g_bn0 = ops.batch_norm(name='g_bn0')
    g_bn1 = ops.batch_norm(name='g_bn1')
    g_bn2 = ops.batch_norm(name='g_bn2')
    g_bn3 = ops.batch_norm(name='g_bn3')

    # project `z` and reshape
    h0 = tf.reshape(ops.linear(z, hparams.gf_dim*8*s16*s16, 'g_h0_lin'), [-1, s16, s16, hparams.gf_dim * 8])
    h0 = tf.nn.relu(g_bn0(h0, train=train))

    h1 = ops.deconv2d(h0, [hparams.batch_size, s8, s8, hparams.gf_dim*4], name='g_h1')
    h1 = tf.nn.relu(g_bn1(h1, train=train))

    h2 = ops.deconv2d(h1, [hparams.batch_size, s4, s4, hparams.gf_dim*2], name='g_h2')
    h2 = tf.nn.relu(g_bn2(h2, train=train))

    h3 = ops.deconv2d(h2, [hparams.batch_size, s2, s2, hparams.gf_dim*1], name='g_h3')
    h3 = tf.nn.relu(g_bn3(h3, train=train))

    h4 = ops.deconv2d(h3, [hparams.batch_size, s, s, hparams.c_dim], name='g_h4')
    x_gen = tf.nn.tanh(h4)

    return x_gen 

def dcgan_decoder(opts, noise, is_training=False, reuse=False):
    output_shape = datashapes[opts['dataset']]
    num_units = opts['g_num_filters']
    batch_size = tf.shape(noise)[0]
    num_layers = opts['g_num_layers']
    if opts['g_arch'] == 'dcgan':
        height = output_shape[0] / 2**num_layers
        width = output_shape[1] / 2**num_layers
    elif opts['g_arch'] == 'dcgan_mod':
        height = output_shape[0] / 2**(num_layers - 1)
        width = output_shape[1] / 2**(num_layers - 1)

    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)
        _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='h%d_bn' % i)
        layer_x = tf.nn.relu(layer_x)
    _out_shape = [batch_size] + list(output_shape)
    if opts['g_arch'] == 'dcgan':
        last_h = ops.deconv2d(
            opts, layer_x, _out_shape, scope='hfinal_deconv')
    elif opts['g_arch'] == 'dcgan_mod':
        last_h = ops.deconv2d(
            opts, layer_x, _out_shape, d_h=1, d_w=1, scope='hfinal_deconv')
    if opts['input_normalize_sym']:
        return tf.nn.tanh(last_h), last_h
    else:
        return tf.nn.sigmoid(last_h), last_h 

def __call__(self, input):
        if self._deconv_type == 'bilinear':
            from ops import bilinear_deconv2d as deconv2d
        elif self._deconv_type == 'nn':
            from ops import nn_deconv2d as deconv2d
        elif self._deconv_type == 'transpose':
            from ops import deconv2d
        else:
            raise NotImplementedError
        with tf.variable_scope(self.name, reuse=self._reuse):
            if not self._reuse:
                log.warn(self.name)
            _ = fc(input, self.start_dim_x * self.start_dim_y * self.start_dim_ch,
                   self._is_train, info=not self._reuse, norm='none', name='fc')
            _ = tf.reshape(_, [_.shape.as_list()[0], self.start_dim_y,
                               self.start_dim_x, self.start_dim_ch])
            if not self._reuse:
                log.info('reshape {} '.format(_.shape.as_list()))
            num_deconv_layer = int(np.ceil(np.log2(
                max(float(self._h/self.start_dim_y), float(self._w/self.start_dim_x)))))
            for i in range(num_deconv_layer):
                _ = deconv2d(_, max(self._c, int(_.get_shape().as_list()[-1]/2)),
                             self._is_train, info=not self._reuse, norm=self._norm_type,
                             name='deconv{}'.format(i+1))
                if num_deconv_layer - i <= self._num_res_block:
                    _ = conv2d_res(
                            _, self._is_train, info=not self._reuse,
                            name='res_block{}'.format(self._num_res_block - num_deconv_layer + i + 1))
            _ = deconv2d(_, self._c, self._is_train, k=1, s=1, info=not self._reuse,
                         activation_fn=tf.tanh, norm='none',
                         name='deconv{}'.format(i+2))
            _ = tf.image.resize_bilinear(_, [self._h, self._w])

            self._reuse = True
            self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.name)
            return _ 

def generator(hparams, z, train, reuse):

    if reuse:
        tf.get_variable_scope().reuse_variables()

    output_size = 64
    s = output_size
    s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16)

    g_bn0 = ops.batch_norm(name='g_bn0')
    g_bn1 = ops.batch_norm(name='g_bn1')
    g_bn2 = ops.batch_norm(name='g_bn2')
    g_bn3 = ops.batch_norm(name='g_bn3')

    # project `z` and reshape
    h0 = tf.reshape(ops.linear(z, hparams.gf_dim*8*s16*s16, 'g_h0_lin'), [-1, s16, s16, hparams.gf_dim * 8])
    h0 = tf.nn.relu(g_bn0(h0, train=train))

    h1 = ops.deconv2d(h0, [hparams.batch_size, s8, s8, hparams.gf_dim*4], name='g_h1')
    h1 = tf.nn.relu(g_bn1(h1, train=train))

    h2 = ops.deconv2d(h1, [hparams.batch_size, s4, s4, hparams.gf_dim*2], name='g_h2')
    h2 = tf.nn.relu(g_bn2(h2, train=train))

    h3 = ops.deconv2d(h2, [hparams.batch_size, s2, s2, hparams.gf_dim*1], name='g_h3')
    h3 = tf.nn.relu(g_bn3(h3, train=train))

    h4 = ops.deconv2d(h3, [hparams.batch_size, s, s, hparams.c_dim], name='g_h4')
    x_gen = tf.nn.tanh(h4)

    return x_gen 

def generator(self, opts, noise, is_training, reuse=False):
        """Generator function, suitable for simple picture experiments.

        Args:
            noise: [num_points, dim] array, where dim is dimensionality of the
                latent noise space.
            is_training: bool, defines whether to use batch_norm in the train
                or test mode.
        Returns:
            [num_points, dim1, dim2, dim3] array, where the first coordinate
            indexes the points, which all are of the shape (dim1, dim2, dim3).
        """

        output_shape = self._data.data_shape # (dim1, dim2, dim3)
        # Computing the number of noise vectors on-the-go
        dim1 = tf.shape(noise)[0]
        num_filters = opts['g_num_filters']

        with tf.variable_scope("GENERATOR", reuse=reuse):

            height = output_shape[0] / 4
            width = output_shape[1] / 4
            h0 = ops.linear(opts, noise, num_filters * height * width,
                            scope='h0_lin')
            h0 = tf.reshape(h0, [-1, height, width, num_filters])
            h0 = ops.batch_norm(opts, h0, is_training, reuse, scope='bn_layer1')
            # h0 = tf.nn.relu(h0)
            h0 = ops.lrelu(h0)
            _out_shape = [dim1, height * 2, width * 2, num_filters / 2]
            # for 28 x 28 does 7 x 7 --> 14 x 14
            h1 = ops.deconv2d(opts, h0, _out_shape, scope='h1_deconv')
            h1 = ops.batch_norm(opts, h1, is_training, reuse, scope='bn_layer2')
            # h1 = tf.nn.relu(h1)
            h1 = ops.lrelu(h1)
            _out_shape = [dim1, height * 4, width * 4, num_filters / 4]
            # for 28 x 28 does 14 x 14 --> 28 x 28
            h2 = ops.deconv2d(opts, h1, _out_shape, scope='h2_deconv')
            h2 = ops.batch_norm(opts, h2, is_training, reuse, scope='bn_layer3')
            # h2 = tf.nn.relu(h2)
            h2 = ops.lrelu(h2)
            _out_shape = [dim1] + list(output_shape)
            # data_shape[0] x data_shape[1] x ? -> data_shape
            h3 = ops.deconv2d(opts, h2, _out_shape,
                              d_h=1, d_w=1, scope='h3_deconv')
            h3 = ops.batch_norm(opts, h3, is_training, reuse, scope='bn_layer4')

        if opts['input_normalize_sym']:
            return tf.nn.tanh(h3)
        else:
            return tf.nn.sigmoid(h3) 

def generator(self, opts, noise, is_training, reuse=False):
        """Generator function, suitable for bigger simple pictures.

        Args:
            noise: [num_points, dim] array, where dim is dimensionality of the
                latent noise space.
            is_training: bool, defines whether to use batch_norm in the train
                or test mode.
        Returns:
            [num_points, dim1, dim2, dim3] array, where the first coordinate
            indexes the points, which all are of the shape (dim1, dim2, dim3).
        """

        output_shape = self._data.data_shape # (dim1, dim2, dim3)
        # Computing the number of noise vectors on-the-go
        dim1 = tf.shape(noise)[0]
        num_filters = opts['g_num_filters']

        with tf.variable_scope("GENERATOR", reuse=reuse):

            height = output_shape[0] / 16
            width = output_shape[1] / 16
            h0 = ops.linear(opts, noise, num_filters * height * width,
                            scope='h0_lin')
            h0 = tf.reshape(h0, [-1, height, width, num_filters])
            h0 = ops.batch_norm(opts, h0, is_training, reuse, scope='bn_layer1')
            h0 = tf.nn.relu(h0)
            _out_shape = [dim1, height * 2, width * 2, num_filters / 2]
            # for 128 x 128 does 8 x 8 --> 16 x 16
            h1 = ops.deconv2d(opts, h0, _out_shape, scope='h1_deconv')
            h1 = ops.batch_norm(opts, h1, is_training, reuse, scope='bn_layer2')
            h1 = tf.nn.relu(h1)
            _out_shape = [dim1, height * 4, width * 4, num_filters / 4]
            # for 128 x 128 does 16 x 16 --> 32 x 32 
            h2 = ops.deconv2d(opts, h1, _out_shape, scope='h2_deconv')
            h2 = ops.batch_norm(opts, h2, is_training, reuse, scope='bn_layer3')
            h2 = tf.nn.relu(h2)
            _out_shape = [dim1, height * 8, width * 8, num_filters / 8]
            # for 128 x 128 does 32 x 32 --> 64 x 64 
            h3 = ops.deconv2d(opts, h2, _out_shape, scope='h3_deconv')
            h3 = ops.batch_norm(opts, h3, is_training, reuse, scope='bn_layer4')
            h3 = tf.nn.relu(h3)
            _out_shape = [dim1, height * 16, width * 16, num_filters / 16]
            # for 128 x 128 does 64 x 64 --> 128 x 128 
            h4 = ops.deconv2d(opts, h3, _out_shape, scope='h4_deconv')
            h4 = ops.batch_norm(opts, h4, is_training, reuse, scope='bn_layer5')
            h4 = tf.nn.relu(h4)
            _out_shape = [dim1] + list(output_shape)
            # data_shape[0] x data_shape[1] x ? -> data_shape
            h5 = ops.deconv2d(opts, h4, _out_shape,
                              d_h=1, d_w=1, scope='h5_deconv')
            h5 = ops.batch_norm(opts, h5, is_training, reuse, scope='bn_layer6')

        if opts['input_normalize_sym']:
            return tf.nn.tanh(h5)
        else:
            return tf.nn.sigmoid(h5) 

def dcgan_like_arch(self, opts, noise, is_training, reuse, keep_prob):
        output_shape = self._data.data_shape
        num_units = opts['g_num_filters']

        batch_size = tf.shape(noise)[0]
        num_layers = opts['g_num_layers']
        if opts['g_arch'] == 'dcgan':
            height = output_shape[0] / 2**num_layers
            width = output_shape[1] / 2**num_layers
        elif opts['g_arch'] == 'dcgan_mod':
            height = output_shape[0] / 2**(num_layers-1)
            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(
                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) 

def ali_deconv(self, opts, noise, is_training, reuse, keep_prob):
        output_shape = self._data.data_shape

        batch_size = tf.shape(noise)[0]
        noise_size = int(noise.get_shape()[1])
        data_height = output_shape[0]
        data_width = output_shape[1]
        data_channels = output_shape[2]

        noise = tf.reshape(noise, [-1, 1, 1, noise_size])

        num_units = opts['g_num_filters']
        layer_params = []
        layer_params.append([4, 1, num_units])
        layer_params.append([4, 2, num_units / 2])
        layer_params.append([4, 1, num_units / 4])
        layer_params.append([4, 2, num_units / 8])
        layer_params.append([5, 1, num_units / 8])
        # For convolution: (n - k) / stride + 1 = s
        # For transposed: (s - 1) * stride + k = n
        layer_x = noise
        height = 1
        width = 1
        for i, (kernel, stride, channels) in enumerate(layer_params):
            height = (height - 1) * stride + kernel
            width = height
            layer_x = ops.deconv2d(
                opts, layer_x, [batch_size, height, width, channels], d_h=stride, d_w=stride,
                scope='h%d_deconv' % i, conv_filters_dim=kernel, padding='VALID')
            if opts['batch_norm']:
                layer_x = ops.batch_norm(opts, layer_x, is_training, reuse, scope='bn%d' % i)
            layer_x = ops.lrelu(layer_x, 0.1)
        assert height == data_height
        assert width == data_width

        # Then two 1x1 convolutions.
        layer_x = ops.conv2d(opts, layer_x, num_units / 8, d_h=1, d_w=1, scope='conv2d_1x1', conv_filters_dim=1)
        if opts['batch_norm']:
            layer_x = ops.batch_norm(opts, layer_x, is_training, reuse, scope='bnlast')
        layer_x = ops.lrelu(layer_x, 0.1)
        layer_x = ops.conv2d(opts, layer_x, data_channels, d_h=1, d_w=1, scope='conv2d_1x1_2', conv_filters_dim=1)

        if opts['input_normalize_sym']:
            return tf.nn.tanh(layer_x)
        else:
            return tf.nn.sigmoid(layer_x) 

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) 

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) 

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) 

def GeneratorCNN( z, config, reuse=None):
    '''
    maps z to a 64x64 images with values in [-1,1]
    uses batch normalization internally
    '''

    #trying to get around batch_size like this:
    batch_size=tf.shape(z)[0]
    #batch_size=tf.placeholder_with_default(64,[],'bs')

    with tf.variable_scope("generator",reuse=reuse) as vs:
        g_bn0 = batch_norm(name='g_bn0')
        g_bn1 = batch_norm(name='g_bn1')
        g_bn2 = batch_norm(name='g_bn2')
        g_bn3 = batch_norm(name='g_bn3')

        s_h, s_w = config.gf_dim, config.gf_dim#64,64
        s_h2, s_w2 = conv_out_size_same(s_h, 2), conv_out_size_same(s_w, 2)
        s_h4, s_w4 = conv_out_size_same(s_h2, 2), conv_out_size_same(s_w2, 2)
        s_h8, s_w8 = conv_out_size_same(s_h4, 2), conv_out_size_same(s_w4, 2)
        s_h16, s_w16 = conv_out_size_same(s_h8, 2), conv_out_size_same(s_w8, 2)



        # project `z` and reshape
        z_, self_h0_w, self_h0_b = linear(
            z, config.gf_dim*8*s_h16*s_w16, 'g_h0_lin', with_w=True)

        self_h0 = tf.reshape(
            z_, [-1, s_h16, s_w16, config.gf_dim * 8])
        h0 = tf.nn.relu(g_bn0(self_h0))

        h1, h1_w, h1_b = deconv2d(
            h0, [batch_size, s_h8, s_w8, config.gf_dim*4], name='g_h1', with_w=True)
        h1 = tf.nn.relu(g_bn1(h1))

        h2, h2_w, h2_b = deconv2d(
            h1, [batch_size, s_h4, s_w4, config.gf_dim*2], name='g_h2', with_w=True)
        h2 = tf.nn.relu(g_bn2(h2))

        h3, h3_w, h3_b = deconv2d(
            h2, [batch_size, s_h2, s_w2, config.gf_dim*1], name='g_h3', with_w=True)
        h3 = tf.nn.relu(g_bn3(h3))

        h4, h4_w, h4_b = deconv2d(
            h3, [batch_size, s_h, s_w, config.c_dim], name='g_h4', with_w=True)
        out=tf.nn.tanh(h4)

    variables = tf.contrib.framework.get_variables(vs)
    return out, variables 

def ali_decoder(opts, noise, is_training=False, reuse=False):
    output_shape = datashapes[opts['dataset']]
    batch_size = tf.shape(noise)[0]
    noise_size = int(noise.get_shape()[1])
    data_height = output_shape[0]
    data_width = output_shape[1]
    data_channels = output_shape[2]
    noise = tf.reshape(noise, [-1, 1, 1, noise_size])
    num_units = opts['g_num_filters']
    layer_params = []
    layer_params.append([4, 1, num_units])
    layer_params.append([4, 2, num_units / 2])
    layer_params.append([4, 1, num_units / 4])
    layer_params.append([4, 2, num_units / 8])
    layer_params.append([5, 1, num_units / 8])
    # For convolution: (n - k) / stride + 1 = s
    # For transposed: (s - 1) * stride + k = n
    layer_x = noise
    height = 1
    width = 1
    for i, (kernel, stride, channels) in enumerate(layer_params):
        height = (height - 1) * stride + kernel
        width = height
        layer_x = ops.deconv2d(
            opts, layer_x, [batch_size, height, width, channels],
            d_h=stride, d_w=stride, scope='h%d_deconv' % i,
            conv_filters_dim=kernel, padding='VALID')
        if opts['batch_norm']:
            layer_x = ops.batch_norm(opts, layer_x, is_training,
                                     reuse, scope='h%d_bn' % i)
        layer_x = ops.lrelu(layer_x, 0.1)
    assert height == data_height
    assert width == data_width

    # Then two 1x1 convolutions.
    layer_x = ops.conv2d(opts, layer_x, num_units / 8, d_h=1, d_w=1,
                         scope='conv2d_1x1', conv_filters_dim=1)
    if opts['batch_norm']:
        layer_x = ops.batch_norm(opts, layer_x,
                                 is_training, reuse, scope='hfinal_bn')
    layer_x = ops.lrelu(layer_x, 0.1)
    layer_x = ops.conv2d(opts, layer_x, data_channels, d_h=1, d_w=1,
                         scope='conv2d_1x1_2', conv_filters_dim=1)
    if opts['input_normalize_sym']:
        return tf.nn.tanh(layer_x), layer_x
    else:
        return tf.nn.sigmoid(layer_x), layer_x