Python utils 模块,weight_variable() 实例源码

我们从Python开源项目中,提取了以下12个代码示例,用于说明如何使用utils.weight_variable()

项目:WassersteinGAN.tensorflow    作者:shekkizh    | 项目源码 | 文件源码
def _discriminator(self, input_images, dims, train_phase, activation=tf.nn.relu, scope_name="discriminator",
                       scope_reuse=False):
        N = len(dims)
        with tf.variable_scope(scope_name) as scope:
            if scope_reuse:
                scope.reuse_variables()
            h = input_images
            skip_bn = True  # First layer of discriminator skips batch norm
            for index in range(N - 2):
                W = utils.weight_variable([4, 4, dims[index], dims[index + 1]], name="W_%d" % index)
                b = tf.zeros([dims[index+1]])
                h_conv = utils.conv2d_strided(h, W, b)
                if skip_bn:
                    h_bn = h_conv
                    skip_bn = False
                else:
                    h_bn = utils.batch_norm(h_conv, dims[index + 1], train_phase, scope="disc_bn%d" % index)
                h = activation(h_bn, name="h_%d" % index)
                utils.add_activation_summary(h)

            W_pred = utils.weight_variable([4, 4, dims[-2], dims[-1]], name="W_pred")
            b = tf.zeros([dims[-1]])
            h_pred = utils.conv2d_strided(h, W_pred, b)
        return None, h_pred, None  # Return the last convolution output. None values are returned to maintatin disc from other GAN
项目:GAN    作者:kunrenzhilu    | 项目源码 | 文件源码
def _discriminator(self, input_images, dims, train_phase, activation=tf.nn.relu, scope_name="discriminator",
                       scope_reuse=False):
        N = len(dims)
        with tf.variable_scope(scope_name) as scope:
            if scope_reuse:
                scope.reuse_variables()
            h = input_images
            skip_bn = True  # First layer of discriminator skips batch norm
            for index in range(N - 2):
                W = utils.weight_variable([4, 4, dims[index], dims[index + 1]], name="W_%d" % index)
                b = tf.zeros([dims[index+1]])
                h_conv = utils.conv2d_strided(h, W, b)
                if skip_bn:
                    h_bn = h_conv
                    skip_bn = False
                else:
                    h_bn = utils.batch_norm(h_conv, dims[index + 1], train_phase, scope="disc_bn%d" % index)
                h = activation(h_bn, name="h_%d" % index)
                utils.add_activation_summary(h)

            W_pred = utils.weight_variable([4, 4, dims[-2], dims[-1]], name="W_pred")
            b = tf.zeros([dims[-1]])
            h_pred = utils.conv2d_strided(h, W_pred, b)
        return None, h_pred, None  # Return the last convolution output. None values are returned to maintatin disc from other GAN
项目:RickLiuGC    作者:liuruijin17    | 项目源码 | 文件源码
def LearningRegularizationOmitting(cv_left, cv_right,
                           batch_size=1, F=32, D=192, H=256, W=512, SHARE=None):

    Y36relu_left = cv_left
    Y36relu_right = cv_right
    with tf.name_scope('Conv3d37'):
        with tf.variable_scope('params', reuse=SHARE):
            W37 = weight_variable((3, 3, 3, 1, 2 * F))
        output37shape = [batch_size, D, H, W, 1]
        Y37_left = conv3dt(Y36relu_left, W37, outputshape=output37shape, stride=2)
        Y37_right = conv3dt(Y36relu_right, W37, outputshape=output37shape, stride=2)

    return Y37_left, Y37_right
项目:WassersteinGAN.tensorflow    作者:shekkizh    | 项目源码 | 文件源码
def _generator(self, z, dims, train_phase, activation=tf.nn.relu, scope_name="generator"):
        N = len(dims)
        image_size = self.resized_image_size // (2 ** (N - 1))
        with tf.variable_scope(scope_name) as scope:
            W_z = utils.weight_variable([self.z_dim, dims[0] * image_size * image_size], name="W_z")
            b_z = utils.bias_variable([dims[0] * image_size * image_size], name="b_z")
            h_z = tf.matmul(z, W_z) + b_z
            h_z = tf.reshape(h_z, [-1, image_size, image_size, dims[0]])
            h_bnz = utils.batch_norm(h_z, dims[0], train_phase, scope="gen_bnz")
            h = activation(h_bnz, name='h_z')
            utils.add_activation_summary(h)

            for index in range(N - 2):
                image_size *= 2
                W = utils.weight_variable([5, 5, dims[index + 1], dims[index]], name="W_%d" % index)
                b = utils.bias_variable([dims[index + 1]], name="b_%d" % index)
                deconv_shape = tf.pack([tf.shape(h)[0], image_size, image_size, dims[index + 1]])
                h_conv_t = utils.conv2d_transpose_strided(h, W, b, output_shape=deconv_shape)
                h_bn = utils.batch_norm(h_conv_t, dims[index + 1], train_phase, scope="gen_bn%d" % index)
                h = activation(h_bn, name='h_%d' % index)
                utils.add_activation_summary(h)

            image_size *= 2
            W_pred = utils.weight_variable([5, 5, dims[-1], dims[-2]], name="W_pred")
            b_pred = utils.bias_variable([dims[-1]], name="b_pred")
            deconv_shape = tf.pack([tf.shape(h)[0], image_size, image_size, dims[-1]])
            h_conv_t = utils.conv2d_transpose_strided(h, W_pred, b_pred, output_shape=deconv_shape)
            pred_image = tf.nn.tanh(h_conv_t, name='pred_image')
            utils.add_activation_summary(pred_image)

        return pred_image
项目:WassersteinGAN.tensorflow    作者:shekkizh    | 项目源码 | 文件源码
def _discriminator(self, input_images, dims, train_phase, activation=tf.nn.relu, scope_name="discriminator",
                       scope_reuse=False):
        N = len(dims)
        with tf.variable_scope(scope_name) as scope:
            if scope_reuse:
                scope.reuse_variables()
            h = input_images
            skip_bn = True  # First layer of discriminator skips batch norm
            for index in range(N - 2):
                W = utils.weight_variable([5, 5, dims[index], dims[index + 1]], name="W_%d" % index)
                b = utils.bias_variable([dims[index + 1]], name="b_%d" % index)
                h_conv = utils.conv2d_strided(h, W, b)
                if skip_bn:
                    h_bn = h_conv
                    skip_bn = False
                else:
                    h_bn = utils.batch_norm(h_conv, dims[index + 1], train_phase, scope="disc_bn%d" % index)
                h = activation(h_bn, name="h_%d" % index)
                utils.add_activation_summary(h)

            shape = h.get_shape().as_list()
            image_size = self.resized_image_size // (2 ** (N - 2))  # dims has input dim and output dim
            h_reshaped = tf.reshape(h, [self.batch_size, image_size * image_size * shape[3]])
            W_pred = utils.weight_variable([image_size * image_size * shape[3], dims[-1]], name="W_pred")
            b_pred = utils.bias_variable([dims[-1]], name="b_pred")
            h_pred = tf.matmul(h_reshaped, W_pred) + b_pred

        return tf.nn.sigmoid(h_pred), h_pred, h
项目:WassersteinGAN.tensorflow    作者:shekkizh    | 项目源码 | 文件源码
def _generator(self, z, dims, train_phase, activation=tf.nn.relu, scope_name="generator"):
        N = len(dims)
        image_size = self.resized_image_size // (2 ** (N - 1))
        with tf.variable_scope(scope_name) as scope:
            W_z = utils.weight_variable([self.z_dim, dims[0] * image_size * image_size], name="W_z")
            h_z = tf.matmul(z, W_z)
            h_z = tf.reshape(h_z, [-1, image_size, image_size, dims[0]])
            h_bnz = utils.batch_norm(h_z, dims[0], train_phase, scope="gen_bnz")
            h = activation(h_bnz, name='h_z')
            utils.add_activation_summary(h)

            for index in range(N - 2):
                image_size *= 2
                W = utils.weight_variable([4, 4, dims[index + 1], dims[index]], name="W_%d" % index)
                b = tf.zeros([dims[index + 1]])
                deconv_shape = tf.pack([tf.shape(h)[0], image_size, image_size, dims[index + 1]])
                h_conv_t = utils.conv2d_transpose_strided(h, W, b, output_shape=deconv_shape)
                h_bn = utils.batch_norm(h_conv_t, dims[index + 1], train_phase, scope="gen_bn%d" % index)
                h = activation(h_bn, name='h_%d' % index)
                utils.add_activation_summary(h)

            image_size *= 2
            W_pred = utils.weight_variable([4, 4, dims[-1], dims[-2]], name="W_pred")
            b = tf.zeros([dims[-1]])
            deconv_shape = tf.pack([tf.shape(h)[0], image_size, image_size, dims[-1]])
            h_conv_t = utils.conv2d_transpose_strided(h, W_pred, b, output_shape=deconv_shape)
            pred_image = tf.nn.tanh(h_conv_t, name='pred_image')
            utils.add_activation_summary(pred_image)

        return pred_image
项目:ste-GAN-ography2    作者:bin2415    | 项目源码 | 文件源码
def fc_layer(x, shape, name):
    num_inputs, num_outputs = shape
    W = utils.weight_variable(shape, 1.0, name + "/W")
    b = utils.bias_variable([num_outputs], 0.0, name + "/b")
    return tf.nn.sigmoid(tf.matmul(x, W) + b)
项目:ste-GAN-ography2    作者:bin2415    | 项目源码 | 文件源码
def weight_variable(shape, std, name):
    initial = tf.truncated_normal(shape, stddev = std)   #???????????????????
    W = tf.Variable(initial, name = name)
    return W
项目:ste-GAN-ography2    作者:bin2415    | 项目源码 | 文件源码
def conv_layer(x, filter_shape, stride, sigmoid, name):
    filter_width, num_inputs, num_outputs = filter_shape
    W = weight_variable(filter_shape, 0.1, name + "/W")
    b = bias_variable([num_outputs], 0.0, name + "/b")
    z = tf.nn.conv1d(x, W, stride = stride, padding = 'SAME') + b
    a = tf.nn.sigmoid(z) if sigmoid else tf.nn.tanh(z)
    return a
项目:GAN    作者:kunrenzhilu    | 项目源码 | 文件源码
def _generator(self, z, dims, train_phase, activation=tf.nn.relu, scope_name="generator"):
        N = len(dims)
        image_size = self.resized_image_size // (2 ** (N - 1))
        with tf.variable_scope(scope_name) as scope:
            W_z = utils.weight_variable([self.z_dim, dims[0] * image_size * image_size], name="W_z")
            b_z = utils.bias_variable([dims[0] * image_size * image_size], name="b_z")
            h_z = tf.matmul(z, W_z) + b_z
            h_z = tf.reshape(h_z, [-1, image_size, image_size, dims[0]])
            h_bnz = utils.batch_norm(h_z, dims[0], train_phase, scope="gen_bnz")
            h = activation(h_bnz, name='h_z')
            utils.add_activation_summary(h)

            for index in range(N - 2):
                image_size *= 2
                W = utils.weight_variable([5, 5, dims[index + 1], dims[index]], name="W_%d" % index)
                b = utils.bias_variable([dims[index + 1]], name="b_%d" % index)
                deconv_shape = tf.stack([tf.shape(h)[0], image_size, image_size, dims[index + 1]])
                h_conv_t = utils.conv2d_transpose_strided(h, W, b, output_shape=deconv_shape)
                h_bn = utils.batch_norm(h_conv_t, dims[index + 1], train_phase, scope="gen_bn%d" % index)
                h = activation(h_bn, name='h_%d' % index)
                utils.add_activation_summary(h)

            image_size *= 2
            W_pred = utils.weight_variable([5, 5, dims[-1], dims[-2]], name="W_pred")
            b_pred = utils.bias_variable([dims[-1]], name="b_pred")
            deconv_shape = tf.stack([tf.shape(h)[0], image_size, image_size, dims[-1]])
            h_conv_t = utils.conv2d_transpose_strided(h, W_pred, b_pred, output_shape=deconv_shape)
            pred_image = tf.nn.tanh(h_conv_t, name='pred_image')
            utils.add_activation_summary(pred_image)

        return pred_image
项目:GAN    作者:kunrenzhilu    | 项目源码 | 文件源码
def _discriminator(self, input_images, dims, train_phase, activation=tf.nn.relu, scope_name="discriminator",
                       scope_reuse=False):
        N = len(dims)
        with tf.variable_scope(scope_name) as scope:
            if scope_reuse:
                scope.reuse_variables()
            h = input_images
            skip_bn = True  # First layer of discriminator skips batch norm
            for index in range(N - 2):
                W = utils.weight_variable([5, 5, dims[index], dims[index + 1]], name="W_%d" % index)
                b = utils.bias_variable([dims[index + 1]], name="b_%d" % index)
                h_conv = utils.conv2d_strided(h, W, b)
                if skip_bn:
                    h_bn = h_conv
                    skip_bn = False
                else:
                    h_bn = utils.batch_norm(h_conv, dims[index + 1], train_phase, scope="disc_bn%d" % index)
                h = activation(h_bn, name="h_%d" % index)
                utils.add_activation_summary(h)

            shape = h.get_shape().as_list()
            image_size = self.resized_image_size // (2 ** (N - 2))  # dims has input dim and output dim
            h_reshaped = tf.reshape(h, [self.batch_size, image_size * image_size * shape[3]])
            W_pred = utils.weight_variable([image_size * image_size * shape[3], dims[-1]], name="W_pred")
            b_pred = utils.bias_variable([dims[-1]], name="b_pred")
            h_pred = tf.matmul(h_reshaped, W_pred) + b_pred

        return tf.nn.sigmoid(h_pred), h_pred, h
项目:GAN    作者:kunrenzhilu    | 项目源码 | 文件源码
def _generator(self, z, dims, train_phase, activation=tf.nn.relu, scope_name="generator"):
        N = len(dims)
        image_size = self.resized_image_size // (2 ** (N - 1))
        with tf.variable_scope(scope_name) as scope:
            W_z = utils.weight_variable([self.z_dim, dims[0] * image_size * image_size], name="W_z")
            h_z = tf.matmul(z, W_z)
            h_z = tf.reshape(h_z, [-1, image_size, image_size, dims[0]])
            h_bnz = utils.batch_norm(h_z, dims[0], train_phase, scope="gen_bnz")
            h = activation(h_bnz, name='h_z')
            utils.add_activation_summary(h)

            for index in range(N - 2):
                image_size *= 2
                W = utils.weight_variable([4, 4, dims[index + 1], dims[index]], name="W_%d" % index)
                b = tf.zeros([dims[index + 1]])
                deconv_shape = tf.stack([tf.shape(h)[0], image_size, image_size, dims[index + 1]])
                h_conv_t = utils.conv2d_transpose_strided(h, W, b, output_shape=deconv_shape)
                h_bn = utils.batch_norm(h_conv_t, dims[index + 1], train_phase, scope="gen_bn%d" % index)
                h = activation(h_bn, name='h_%d' % index)
                utils.add_activation_summary(h)

            image_size *= 2
            W_pred = utils.weight_variable([4, 4, dims[-1], dims[-2]], name="W_pred")
            b = tf.zeros([dims[-1]])
            deconv_shape = tf.stack([tf.shape(h)[0], image_size, image_size, dims[-1]])
            h_conv_t = utils.conv2d_transpose_strided(h, W_pred, b, output_shape=deconv_shape)
            pred_image = tf.nn.tanh(h_conv_t, name='pred_image')
            utils.add_activation_summary(pred_image)

        return pred_image