Python ops.batch_norm方法代码示例

# 需要导入模块: import ops [as 别名]
# 或者: from ops import batch_norm [as 别名]
def discriminator(self, opts, input_, is_training,
                      prefix='DISCRIMINATOR', reuse=False):
        """Discriminator function, suitable for simple toy experiments.

        """
        num_filters = opts['d_num_filters']

        with tf.variable_scope(prefix, reuse=reuse):
            h0 = ops.conv2d(opts, input_, num_filters, scope='h0_conv')
            h0 = ops.batch_norm(opts, h0, is_training, reuse, scope='bn_layer1')
            h0 = ops.lrelu(h0)
            h1 = ops.conv2d(opts, h0, num_filters * 2, scope='h1_conv')
            h1 = ops.batch_norm(opts, h1, is_training, reuse, scope='bn_layer2')
            h1 = ops.lrelu(h1)
            h2 = ops.conv2d(opts, h1, num_filters * 4, scope='h2_conv')
            h2 = ops.batch_norm(opts, h2, is_training, reuse, scope='bn_layer3')
            h2 = ops.lrelu(h2)
            h3 = ops.linear(opts, h2, 1, scope='h3_lin')

        return h3 

# 需要导入模块: import ops [as 别名]
# 或者: from ops import batch_norm [as 别名]
def generator(self, opts, noise, is_training, reuse=False):

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

            h0 = ops.linear(opts, noise, 100, scope='h0_lin')
            h0 = ops.batch_norm(opts, h0, is_training, reuse, scope='bn_layer1', scale=False)
            h0 = tf.nn.softplus(h0)
            h1 = ops.linear(opts, h0, 100, scope='h1_lin')
            h1 = ops.batch_norm(opts, h1, is_training, reuse, scope='bn_layer2', scale=False)
            h1 = tf.nn.softplus(h1)
            h2 = ops.linear(opts, h1, 28 * 28, scope='h2_lin')
            # h2 = ops.batch_norm(opts, h2, is_training, reuse, scope='bn_layer3')
            h2 = tf.reshape(h2, [-1, 28, 28, 1])

        if opts['input_normalize_sym']:
            return tf.nn.tanh(h2)
        else:
            return tf.nn.sigmoid(h2) 

# 需要导入模块: import ops [as 别名]
# 或者: from ops import batch_norm [as 别名]
def discriminator(self, opts, input_, is_training,
                      prefix='DISCRIMINATOR', reuse=False):
        """Encoder function, suitable for simple toy experiments.

        """
        num_filters = opts['d_num_filters']

        with tf.variable_scope(prefix, reuse=reuse):
            h0 = ops.conv2d(opts, input_, num_filters / 8, scope='h0_conv')
            h0 = ops.batch_norm(opts, h0, is_training, reuse, scope='bn_layer1')
            h0 = tf.nn.relu(h0)
            h1 = ops.conv2d(opts, h0, num_filters / 4, scope='h1_conv')
            h1 = ops.batch_norm(opts, h1, is_training, reuse, scope='bn_layer2')
            h1 = tf.nn.relu(h1)
            h2 = ops.conv2d(opts, h1, num_filters / 2, scope='h2_conv')
            h2 = ops.batch_norm(opts, h2, is_training, reuse, scope='bn_layer3')
            h2 = tf.nn.relu(h2)
            h3 = ops.conv2d(opts, h2, num_filters, scope='h3_conv')
            h3 = ops.batch_norm(opts, h3, is_training, reuse, scope='bn_layer4')
            h3 = tf.nn.relu(h3)
            # Already has NaNs!!
            latent_mean = ops.linear(opts, h3, opts['latent_space_dim'], scope='h3_lin')
            log_latent_sigmas = ops.linear(opts, h3, opts['latent_space_dim'], scope='h3_lin_sigma')

        return latent_mean, log_latent_sigmas 

# 需要导入模块: import ops [as 别名]
# 或者: from ops import batch_norm [as 别名]
def discriminator_labeler(image, output_dim, config, reuse=None):
    batch_size=tf.shape(image)[0]
    with tf.variable_scope("disc_labeler",reuse=reuse) as vs:
        dl_bn1 = batch_norm(name='dl_bn1')
        dl_bn2 = batch_norm(name='dl_bn2')
        dl_bn3 = batch_norm(name='dl_bn3')

        h0 = lrelu(conv2d(image, config.df_dim, name='dl_h0_conv'))#16,32,32,64
        h1 = lrelu(dl_bn1(conv2d(h0, config.df_dim*2, name='dl_h1_conv')))#16,16,16,128
        h2 = lrelu(dl_bn2(conv2d(h1, config.df_dim*4, name='dl_h2_conv')))#16,16,16,248
        h3 = lrelu(dl_bn3(conv2d(h2, config.df_dim*8, name='dl_h3_conv')))
        dim3=np.prod(h3.get_shape().as_list()[1:])
        h3_flat=tf.reshape(h3, [-1,dim3])
        D_labels_logits = linear(h3_flat, output_dim, 'dl_h3_Label')
        D_labels = tf.nn.sigmoid(D_labels_logits)
        variables = tf.contrib.framework.get_variables(vs)
    return D_labels, D_labels_logits, variables 

# 需要导入模块: import ops [as 别名]
# 或者: from ops import batch_norm [as 别名]
def discriminator_gen_labeler(image, output_dim, config, reuse=None):
    batch_size=tf.shape(image)[0]
    with tf.variable_scope("disc_gen_labeler",reuse=reuse) as vs:
        dl_bn1 = batch_norm(name='dl_bn1')
        dl_bn2 = batch_norm(name='dl_bn2')
        dl_bn3 = batch_norm(name='dl_bn3')

        h0 = lrelu(conv2d(image, config.df_dim, name='dgl_h0_conv'))#16,32,32,64
        h1 = lrelu(dl_bn1(conv2d(h0, config.df_dim*2, name='dgl_h1_conv')))#16,16,16,128
        h2 = lrelu(dl_bn2(conv2d(h1, config.df_dim*4, name='dgl_h2_conv')))#16,16,16,248
        h3 = lrelu(dl_bn3(conv2d(h2, config.df_dim*8, name='dgl_h3_conv')))
        dim3=np.prod(h3.get_shape().as_list()[1:])
        h3_flat=tf.reshape(h3, [-1,dim3])
        D_labels_logits = linear(h3_flat, output_dim, 'dgl_h3_Label')
        D_labels = tf.nn.sigmoid(D_labels_logits)
        variables = tf.contrib.framework.get_variables(vs)
    return D_labels, D_labels_logits,variables 

# 需要导入模块: import ops [as 别名]
# 或者: from ops import batch_norm [as 别名]
def discriminator_on_z(image, config, reuse=None):
    batch_size=tf.shape(image)[0]
    with tf.variable_scope("disc_z_labeler",reuse=reuse) as vs:
        dl_bn1 = batch_norm(name='dl_bn1')
        dl_bn2 = batch_norm(name='dl_bn2')
        dl_bn3 = batch_norm(name='dl_bn3')

        h0 = lrelu(conv2d(image, config.df_dim, name='dzl_h0_conv'))#16,32,32,64
        h1 = lrelu(dl_bn1(conv2d(h0, config.df_dim*2, name='dzl_h1_conv')))#16,16,16,128
        h2 = lrelu(dl_bn2(conv2d(h1, config.df_dim*4, name='dzl_h2_conv')))#16,16,16,248
        h3 = lrelu(dl_bn3(conv2d(h2, config.df_dim*8, name='dzl_h3_conv')))
        dim3=np.prod(h3.get_shape().as_list()[1:])
        h3_flat=tf.reshape(h3, [-1,dim3])
        D_labels_logits = linear(h3_flat, config.z_dim, 'dzl_h3_Label')
        D_labels = tf.nn.tanh(D_labels_logits)
        variables = tf.contrib.framework.get_variables(vs)
    return D_labels,variables 

# 需要导入模块: import ops [as 别名]
# 或者: from ops import batch_norm [as 别名]
def dcgan_encoder(opts, inputs, is_training=False, reuse=False):
    num_units = opts['e_num_filters']
    num_layers = opts['e_num_layers']
    layer_x = inputs
    for i in xrange(num_layers):
        scale = 2**(num_layers - i - 1)
        layer_x = ops.conv2d(opts, layer_x, num_units / scale,
                             scope='h%d_conv' % 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)
    if opts['e_noise'] != 'gaussian':
        res = ops.linear(opts, layer_x, opts['zdim'], scope='hfinal_lin')
        return res
    else:
        mean = ops.linear(opts, layer_x, opts['zdim'], scope='mean_lin')
        log_sigmas = ops.linear(opts, layer_x,
                                opts['zdim'], scope='log_sigmas_lin')
        return mean, log_sigmas 

# 需要导入模块: import ops [as 别名]
# 或者: from ops import batch_norm [as 别名]
def inception_v3_parameters(weight_decay=0.00004, stddev=0.1,
                            batch_norm_decay=0.9997, batch_norm_epsilon=0.001):
  """Yields the scope with the default parameters for inception_v3.

  Args:
    weight_decay: the weight decay for weights variables.
    stddev: standard deviation of the truncated guassian weight distribution.
    batch_norm_decay: decay for the moving average of batch_norm momentums.
    batch_norm_epsilon: small float added to variance to avoid dividing by zero.

  Yields:
    a arg_scope with the parameters needed for inception_v3.
  """
  # Set weight_decay for weights in Conv and FC layers.
  with scopes.arg_scope([ops.conv2d, ops.fc],
                        weight_decay=weight_decay):
    # Set stddev, activation and parameters for batch_norm.
    with scopes.arg_scope([ops.conv2d],
                          stddev=stddev,
                          activation=tf.nn.relu,
                          batch_norm_params={
                              'decay': batch_norm_decay,
                              'epsilon': batch_norm_epsilon}) as arg_scope:
      yield arg_scope 

# 需要导入模块: import ops [as 别名]
# 或者: from ops import batch_norm [as 别名]
def generator(self, opts, noise, is_training=False, reuse=False, keep_prob=1.):
        """ Decoder actually.

        """

        output_shape = self._data.data_shape
        num_units = opts['g_num_filters']

        with tf.variable_scope("GENERATOR", reuse=reuse):
            # if not opts['convolutions']:
            if opts['g_arch'] == 'mlp':
                layer_x = noise
                for i in range(opts['g_num_layers']):
                    layer_x = ops.linear(opts, layer_x, num_units, 'h%d_lin' % i)
                    layer_x = tf.nn.relu(layer_x)
                    if opts['batch_norm']:
                        layer_x = ops.batch_norm(
                            opts, layer_x, is_training, reuse, scope='bn%d' % i)
                out = ops.linear(opts, layer_x, np.prod(output_shape), 'h%d_lin' % (i + 1))
                out = tf.reshape(out, [-1] + list(output_shape))
                if opts['input_normalize_sym']:
                    return tf.nn.tanh(out)
                else:
                    return tf.nn.sigmoid(out)
            elif opts['g_arch'] in ['dcgan', 'dcgan_mod']:
                return self.dcgan_like_arch(opts, noise, is_training, reuse, keep_prob)
            elif opts['g_arch'] == 'conv_up_res':
                return self.conv_up_res(opts, noise, is_training, reuse, keep_prob)
            elif opts['g_arch'] == 'ali':
                return self.ali_deconv(opts, noise, is_training, reuse, keep_prob)
            elif opts['g_arch'] == 'began':
                return self.began_dec(opts, noise, is_training, reuse, keep_prob)
            else:
                raise ValueError('%s unknown' % opts['g_arch']) 

# 需要导入模块: import ops [as 别名]
# 或者: from ops import batch_norm [as 别名]
def encoder(self, opts, input_, is_training=False, reuse=False, keep_prob=1.):
        if opts['e_add_noise']:
            def add_noise(x):
                shape = tf.shape(x)
                return x + tf.truncated_normal(shape, 0.0, 0.01)
            def do_nothing(x):
                return x
            input_ = tf.cond(is_training, lambda: add_noise(input_), lambda: do_nothing(input_))
        num_units = opts['e_num_filters']
        num_layers = opts['e_num_layers']
        with tf.variable_scope("ENCODER", reuse=reuse):
            if not opts['convolutions']:
                hi = input_
                for i in range(num_layers):
                    hi = ops.linear(opts, hi, num_units, scope='h%d_lin' % i)
                    if opts['batch_norm']:
                        hi = ops.batch_norm(opts, hi, is_training, reuse, scope='bn%d' % i)
                    hi = tf.nn.relu(hi)
                if opts['e_is_random']:
                    latent_mean = ops.linear(
                        opts, hi, opts['latent_space_dim'], 'h%d_lin' % (i + 1))
                    log_latent_sigmas = ops.linear(
                        opts, hi, opts['latent_space_dim'], 'h%d_lin_sigma' % (i + 1))
                    return latent_mean, log_latent_sigmas
                else:
                    return ops.linear(opts, hi, opts['latent_space_dim'], 'h%d_lin' % (i + 1))
            elif opts['e_arch'] == 'dcgan':
                return self.dcgan_encoder(opts, input_, is_training, reuse, keep_prob)
            elif opts['e_arch'] == 'ali':
                return self.ali_encoder(opts, input_, is_training, reuse, keep_prob)
            elif opts['e_arch'] == 'began':
                return self.began_encoder(opts, input_, is_training, reuse, keep_prob)
            else:
                raise ValueError('%s Unknown' % opts['e_arch']) 

# 需要导入模块: import ops [as 别名]
# 或者: from ops import batch_norm [as 别名]
def dcgan_encoder(self, opts, input_, is_training=False, reuse=False, keep_prob=1.):
        num_units = opts['e_num_filters']
        num_layers = opts['e_num_layers']
        layer_x = input_
        for i in xrange(num_layers):
            scale = 2**(num_layers-i-1)
            layer_x = ops.conv2d(opts, layer_x, num_units / scale, scope='h%d_conv' % 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)
                layer_x = tf.nn.dropout(layer_x, _keep_prob)

            if opts['e_3x3_conv'] > 0:
                before = layer_x
                for j in range(opts['e_3x3_conv']):
                    layer_x = ops.conv2d(opts, layer_x, num_units / scale, d_h=1, d_w=1,
                                         scope='conv2d_3x3_%d_%d' % (i, j),
                                         conv_filters_dim=3)
                    layer_x = tf.nn.relu(layer_x)
                layer_x += before  # Residual connection.

        if opts['e_is_random']:
            latent_mean = ops.linear(
                opts, layer_x, opts['latent_space_dim'], scope='hlast_lin')
            log_latent_sigmas = ops.linear(
                opts, layer_x, opts['latent_space_dim'], scope='hlast_lin_sigma')
            return latent_mean, log_latent_sigmas
        else:
            return ops.linear(opts, layer_x, opts['latent_space_dim'], scope='hlast_lin') 

# 需要导入模块: import ops [as 别名]
# 或者: from ops import batch_norm [as 别名]
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 

# 需要导入模块: import ops [as 别名]
# 或者: from ops import batch_norm [as 别名]
def discriminator(hparams, x, scope_name, train, reuse):

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

        d_bn1 = ops.batch_norm(name='d_bn1')
        d_bn2 = ops.batch_norm(name='d_bn2')
        d_bn3 = ops.batch_norm(name='d_bn3')

        h0 = ops.lrelu(ops.conv2d(x, hparams.df_dim, name='d_h0_conv'))

        h1 = ops.conv2d(h0, hparams.df_dim*2, name='d_h1_conv')
        h1 = ops.lrelu(d_bn1(h1, train=train))

        h2 = ops.conv2d(h1, hparams.df_dim*4, name='d_h2_conv')
        h2 = ops.lrelu(d_bn2(h2, train=train))

        h3 = ops.conv2d(h2, hparams.df_dim*8, name='d_h3_conv')
        h3 = ops.lrelu(d_bn3(h3, train=train))

        h4 = ops.linear(tf.reshape(h3, [hparams.batch_size, -1]), 1, 'd_h3_lin')

        d_logit = h4
        d = tf.nn.sigmoid(d_logit)

    return d, d_logit 

# 需要导入模块: import ops [as 别名]
# 或者: from ops import batch_norm [as 别名]
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 

# 需要导入模块: import ops [as 别名]
# 或者: from ops import batch_norm [as 别名]
def discriminator(hparams, x, train, reuse):

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

    d_bn1 = ops.batch_norm(name='d_bn1')
    d_bn2 = ops.batch_norm(name='d_bn2')
    d_bn3 = ops.batch_norm(name='d_bn3')

    h0 = ops.lrelu(ops.conv2d(x, hparams.df_dim, name='d_h0_conv'))

    h1 = ops.conv2d(h0, hparams.df_dim*2, name='d_h1_conv')
    h1 = ops.lrelu(d_bn1(h1, train=train))

    h2 = ops.conv2d(h1, hparams.df_dim*4, name='d_h2_conv')
    h2 = ops.lrelu(d_bn2(h2, train=train))

    h3 = ops.conv2d(h2, hparams.df_dim*8, name='d_h3_conv')
    h3 = ops.lrelu(d_bn3(h3, train=train))

    h4 = ops.linear(tf.reshape(h3, [hparams.batch_size, -1]), 1, 'd_h3_lin')

    d_logit = h4
    d = tf.nn.sigmoid(d_logit)

    return d, d_logit