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()。
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
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
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
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
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
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)
def weight_variable(shape, std, name): initial = tf.truncated_normal(shape, stddev = std) #??????????????????? W = tf.Variable(initial, name = name) return W
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
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
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
