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Python slim.conv2d函数代码示例

本文整理汇总了Python中tensorflow.contrib.slim.conv2d函数的典型用法代码示例。如果您正苦于以下问题:Python conv2d函数的具体用法?Python conv2d怎么用?Python conv2d使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。


在下文中一共展示了conv2d函数的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: build_arch_baseline

def build_arch_baseline(input, is_train: bool, num_classes: int):

    bias_initializer = tf.truncated_normal_initializer(
        mean=0.0, stddev=0.01)  # tf.constant_initializer(0.0)
    # The paper didnot mention any regularization, a common l2 regularizer to weights is added here
    weights_regularizer = tf.contrib.layers.l2_regularizer(5e-04)

    tf.logging.info('input shape: {}'.format(input.get_shape()))

    # weights_initializer=initializer,
    with slim.arg_scope([slim.conv2d, slim.fully_connected], trainable=is_train, biases_initializer=bias_initializer, weights_regularizer=weights_regularizer):
        with tf.variable_scope('relu_conv1') as scope:
            output = slim.conv2d(input, num_outputs=32, kernel_size=[
                                 5, 5], stride=1, padding='SAME', scope=scope, activation_fn=tf.nn.relu)
            output = slim.max_pool2d(output, [2, 2], scope='max_2d_layer1')

            tf.logging.info('output shape: {}'.format(output.get_shape()))

        with tf.variable_scope('relu_conv2') as scope:
            output = slim.conv2d(output, num_outputs=64, kernel_size=[
                                 5, 5], stride=1, padding='SAME', scope=scope, activation_fn=tf.nn.relu)
            output = slim.max_pool2d(output, [2, 2], scope='max_2d_layer2')

            tf.logging.info('output shape: {}'.format(output.get_shape()))

        output = slim.flatten(output)
        output = slim.fully_connected(output, 1024, scope='relu_fc3', activation_fn=tf.nn.relu)
        tf.logging.info('output shape: {}'.format(output.get_shape()))
        output = slim.dropout(output, 0.5, scope='dp')
        output = slim.fully_connected(output, num_classes, scope='final_layer', activation_fn=None)
        tf.logging.info('output shape: {}'.format(output.get_shape()))
        return output
开发者ID:lzqkean,项目名称:deep_learning,代码行数:32,代码来源:capsnet_em.py

示例2: create_inner_block

def create_inner_block(
        incoming, scope, nonlinearity=tf.nn.elu,
        weights_initializer=tf.truncated_normal_initializer(1e-3),
        bias_initializer=tf.zeros_initializer(), regularizer=None,
        increase_dim=False, summarize_activations=True):
    n = incoming.get_shape().as_list()[-1]
    stride = 1
    if increase_dim:
        n *= 2
        stride = 2

    incoming = slim.conv2d(
        incoming, n, [3, 3], stride, activation_fn=nonlinearity, padding="SAME",
        normalizer_fn=_batch_norm_fn, weights_initializer=weights_initializer,
        biases_initializer=bias_initializer, weights_regularizer=regularizer,
        scope=scope + "/1")
    if summarize_activations:
        tf.summary.histogram(incoming.name + "/activations", incoming)

    incoming = slim.dropout(incoming, keep_prob=0.6)

    incoming = slim.conv2d(
        incoming, n, [3, 3], 1, activation_fn=None, padding="SAME",
        normalizer_fn=None, weights_initializer=weights_initializer,
        biases_initializer=bias_initializer, weights_regularizer=regularizer,
        scope=scope + "/2")
    return incoming
开发者ID:BenJamesbabala,项目名称:deep_sort,代码行数:27,代码来源:generate_detections.py

示例3: create_test_network_7

def create_test_network_7():
  """Aligned network for test, with a control dependency.

  The graph is similar to create_test_network_1(), except that it includes an
  assert operation on the left branch.

  Returns:
    g: Tensorflow graph object (Graph proto).
  """
  g = ops.Graph()
  with g.as_default():
    # An 8x8 test image.
    x = array_ops.placeholder(dtypes.float32, (1, 8, 8, 1), name='input_image')
    # Left branch.
    l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID')
    l1_shape = array_ops.shape(l1)
    assert_op = control_flow_ops.Assert(
        gen_math_ops.equal(l1_shape[1], 2), [l1_shape], summarize=4)
    # Right branch.
    l2_pad = array_ops.pad(x, [[0, 0], [1, 0], [1, 0], [0, 0]])
    l2 = slim.conv2d(l2_pad, 1, [3, 3], stride=2, scope='L2', padding='VALID')
    l3 = slim.conv2d(l2, 1, [1, 1], stride=2, scope='L3', padding='VALID')
    # Addition.
    with ops.control_dependencies([assert_op]):
      nn.relu(l1 + l3, name='output')
  return g
开发者ID:Ajaycs99,项目名称:tensorflow,代码行数:26,代码来源:receptive_field_test.py

示例4: content_extractor

 def content_extractor(self, images, reuse=False):
     # images: (batch, 32, 32, 3) or (batch, 32, 32, 1)
     
     if images.get_shape()[3] == 1:
         # For mnist dataset, replicate the gray scale image 3 times.
         images = tf.image.grayscale_to_rgb(images)
     
     with tf.variable_scope('content_extractor', reuse=reuse):
         with slim.arg_scope([slim.conv2d], padding='SAME', activation_fn=None,
                              stride=2,  weights_initializer=tf.contrib.layers.xavier_initializer()):
             with slim.arg_scope([slim.batch_norm], decay=0.95, center=True, scale=True, 
                                 activation_fn=tf.nn.relu, is_training=(self.mode=='train' or self.mode=='pretrain')):
                 
                 net = slim.conv2d(images, 64, [3, 3], scope='conv1')   # (batch_size, 16, 16, 64)
                 net = slim.batch_norm(net, scope='bn1')
                 net = slim.conv2d(net, 128, [3, 3], scope='conv2')     # (batch_size, 8, 8, 128)
                 net = slim.batch_norm(net, scope='bn2')
                 net = slim.conv2d(net, 256, [3, 3], scope='conv3')     # (batch_size, 4, 4, 256)
                 net = slim.batch_norm(net, scope='bn3')
                 net = slim.conv2d(net, 128, [4, 4], padding='VALID', scope='conv4')   # (batch_size, 1, 1, 128)
                 net = slim.batch_norm(net, activation_fn=tf.nn.tanh, scope='bn4')
                 if self.mode == 'pretrain':
                     net = slim.conv2d(net, 10, [1, 1], padding='VALID', scope='out')
                     net = slim.flatten(net)
                 return net
开发者ID:ALISCIFP,项目名称:domain-transfer-network,代码行数:25,代码来源:model.py

示例5: create_test_network_4

def create_test_network_4():
  """Misaligned network for test.

  The graph corresponds to a variation from the example from the second figure
  in go/cnn-rf-computation#arbitrary-computation-graphs. Layer 2 uses 'SAME'
  padding, which makes its padding dependent on the input image dimensionality.
  In this case, the effective padding will be undetermined, and the utility is
  not able to check the network alignment.

  Returns:
    g: Tensorflow graph object (Graph proto).
  """
  g = ops.Graph()
  with g.as_default():
    # An input test image with unknown spatial resolution.
    x = array_ops.placeholder(
        dtypes.float32, (None, None, None, 1), name='input_image')
    # Left branch.
    l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID')
    # Right branch.
    l2 = slim.conv2d(x, 1, [3, 3], stride=2, scope='L2', padding='SAME')
    l3 = slim.conv2d(l2, 1, [1, 1], stride=2, scope='L3', padding='VALID')
    # Addition.
    nn.relu(l1 + l3, name='output')
  return g
开发者ID:Ajaycs99,项目名称:tensorflow,代码行数:25,代码来源:receptive_field_test.py

示例6: decoder

 def decoder(self, latent_var, is_training):
     activation_fn = leaky_relu  # tf.nn.relu
     weight_decay = 0.0 
     with tf.variable_scope('decoder'):
         with slim.arg_scope([slim.batch_norm],
                             is_training=is_training):
             with slim.arg_scope([slim.conv2d, slim.fully_connected],
                                 weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
                                 weights_regularizer=slim.l2_regularizer(weight_decay),
                                 normalizer_fn=slim.batch_norm,
                                 normalizer_params=self.batch_norm_params):
                 net = slim.fully_connected(latent_var, 4096, activation_fn=None, normalizer_fn=None, scope='Fc_1')
                 net = tf.reshape(net, [-1,4,4,256], name='Reshape')
                 
                 net = tf.image.resize_nearest_neighbor(net, size=(8,8), name='Upsample_1')
                 net = slim.conv2d(net, 128, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_1a')
                 net = slim.repeat(net, 3, conv2d_block, 0.1, 128, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_1b')
         
                 net = tf.image.resize_nearest_neighbor(net, size=(16,16), name='Upsample_2')
                 net = slim.conv2d(net, 64, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_2a')
                 net = slim.repeat(net, 3, conv2d_block, 0.1, 64, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_2b')
         
                 net = tf.image.resize_nearest_neighbor(net, size=(32,32), name='Upsample_3')
                 net = slim.conv2d(net, 32, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_3a')
                 net = slim.repeat(net, 3, conv2d_block, 0.1, 32, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_3b')
         
                 net = tf.image.resize_nearest_neighbor(net, size=(64,64), name='Upsample_4')
                 net = slim.conv2d(net, 3, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_4a')
                 net = slim.repeat(net, 3, conv2d_block, 0.1, 3, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_4b')
                 net = slim.conv2d(net, 3, [3, 3], 1, activation_fn=None, scope='Conv2d_4c')
             
     return net
开发者ID:NickyGeorge,项目名称:facenet,代码行数:32,代码来源:dfc_vae_resnet.py

示例7: build_arch

def build_arch(input, is_train, num_classes):
    data_size = int(input.get_shape()[1])
    # initializer = tf.truncated_normal_initializer(mean=0.0, stddev=0.01)
    # bias_initializer = tf.constant_initializer(0.0)
    # weights_regularizer = tf.contrib.layers.l2_regularizer(5e-04)

    with slim.arg_scope([slim.conv2d], trainable=is_train):#, activation_fn=None, , , biases_initializer=bias_initializer, weights_regularizer=weights_regularizer
        with tf.variable_scope('conv1') as scope:
            output = slim.conv2d(input, num_outputs=256, kernel_size=[9, 9], stride=1, padding='VALID', scope=scope)
            data_size = data_size-8
            assert output.get_shape() == [cfg.batch_size, data_size, data_size, 256]
            tf.logging.info('conv1 output shape: {}'.format(output.get_shape()))

        with tf.variable_scope('primary_caps_layer') as scope:
            output = slim.conv2d(output, num_outputs=32*8, kernel_size=[9, 9], stride=2, padding='VALID', scope=scope)#, activation_fn=None
            output = tf.reshape(output, [cfg.batch_size, -1, 8])
            output = squash(output)
            data_size = int(np.floor((data_size-8)/2))
            assert output.get_shape() == [cfg.batch_size, data_size*data_size*32, 8]
            tf.logging.info('primary capsule output shape: {}'.format(output.get_shape()))

        with tf.variable_scope('digit_caps_layer') as scope:
            with tf.variable_scope('u') as scope:
                u_hats = vec_transform(output, num_classes, 16)
                assert u_hats.get_shape() == [cfg.batch_size, num_classes, data_size*data_size*32, 16]
                tf.logging.info('digit_caps_layer u_hats shape: {}'.format(u_hats.get_shape()))

            with tf.variable_scope('routing') as scope:
                output = dynamic_routing(u_hats)
                assert output.get_shape() == [cfg.batch_size, num_classes, 16]
                tf.logging.info('the output capsule has shape: {}'.format(output.get_shape()))

        output_len = tf.norm(output, axis=-1)

    return output, output_len
开发者ID:lzqkean,项目名称:deep_learning,代码行数:35,代码来源:capsnet_dynamic_routing.py

示例8: iter_func

  def iter_func(self, state):
    sc = predictron_arg_scope()

    with tf.variable_scope('value'):
      value_net = slim.fully_connected(slim.flatten(state), 32, scope='fc0')
      value_net = layers.batch_norm(value_net, activation_fn=tf.nn.relu, scope='fc0/preact')
      value_net = slim.fully_connected(value_net, self.maze_size, activation_fn=None, scope='fc1')

    with slim.arg_scope(sc):
      net = slim.conv2d(state, 32, [3, 3], scope='conv1')
      net = layers.batch_norm(net, activation_fn=tf.nn.relu, scope='conv1/preact')
      net_flatten = slim.flatten(net, scope='conv1/flatten')

      with tf.variable_scope('reward'):
        reward_net = slim.fully_connected(net_flatten, 32, scope='fc0')
        reward_net = layers.batch_norm(reward_net, activation_fn=tf.nn.relu, scope='fc0/preact')
        reward_net = slim.fully_connected(reward_net, self.maze_size, activation_fn=None, scope='fc1')

      with tf.variable_scope('gamma'):
        gamma_net = slim.fully_connected(net_flatten, 32, scope='fc0')
        gamma_net = layers.batch_norm(gamma_net, activation_fn=tf.nn.relu, scope='fc0/preact')
        gamma_net = slim.fully_connected(gamma_net, self.maze_size, activation_fn=tf.nn.sigmoid, scope='fc1')

      with tf.variable_scope('lambda'):
        lambda_net = slim.fully_connected(net_flatten, 32, scope='fc0')
        lambda_net = layers.batch_norm(lambda_net, activation_fn=tf.nn.relu, scope='fc0/preact')
        lambda_net = slim.fully_connected(lambda_net, self.maze_size, activation_fn=tf.nn.sigmoid, scope='fc1')

      net = slim.conv2d(net, 32, [3, 3], scope='conv2')
      net = layers.batch_norm(net, activation_fn=tf.nn.relu, scope='conv2/preact')

      net = slim.conv2d(net, 32, [3, 3], scope='conv3')
      net = layers.batch_norm(net, activation_fn=tf.nn.relu, scope='conv3/preact')
    return net, reward_net, gamma_net, lambda_net, value_net
开发者ID:b-kartal,项目名称:predictron,代码行数:34,代码来源:predictron.py

示例9: create_test_network

def create_test_network():
  """Convolutional neural network for test.

  Returns:
    g: Tensorflow graph object (Graph proto).
  """
  g = ops.Graph()
  with g.as_default():
    # An input test image with unknown spatial resolution.
    x = array_ops.placeholder(
        dtypes.float32, (None, None, None, 1), name='input_image')
    # Left branch before first addition.
    l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID')
    # Right branch before first addition.
    l2_pad = array_ops.pad(x, [[0, 0], [1, 0], [1, 0], [0, 0]], name='L2_pad')
    l2 = slim.conv2d(l2_pad, 1, [3, 3], stride=2, scope='L2', padding='VALID')
    l3 = slim.max_pool2d(l2, [3, 3], stride=2, scope='L3', padding='SAME')
    # First addition.
    l4 = nn.relu(l1 + l3, name='L4_relu')
    # Left branch after first addition.
    l5 = slim.conv2d(l4, 1, [1, 1], stride=2, scope='L5', padding='SAME')
    # Right branch after first addition.
    l6 = slim.conv2d(l4, 1, [3, 3], stride=2, scope='L6', padding='SAME')
    # Final addition.
    gen_math_ops.add(l5, l6, name='L7_add')

  return g
开发者ID:Ajaycs99,项目名称:tensorflow,代码行数:27,代码来源:graph_compute_order_test.py

示例10: create_test_network_9

def create_test_network_9():
  """Aligned network for test, including an intermediate addition.

  The graph is the same as create_test_network_8(), except that VALID padding is
  changed to SAME.

  Returns:
    g: Tensorflow graph object (Graph proto).
  """
  g = ops.Graph()
  with g.as_default():
    # An input test image with unknown spatial resolution.
    x = array_ops.placeholder(
        dtypes.float32, (None, None, None, 1), name='input_image')
    # Left branch before first addition.
    l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='SAME')
    # Right branch before first addition.
    l2 = slim.conv2d(x, 1, [3, 3], stride=2, scope='L2', padding='SAME')
    l3 = slim.conv2d(l2, 1, [1, 1], stride=2, scope='L3', padding='SAME')
    # First addition.
    l4 = nn.relu(l1 + l3)
    # Left branch after first addition.
    l5 = slim.conv2d(l4, 1, [1, 1], stride=2, scope='L5', padding='SAME')
    # Right branch after first addition.
    l6 = slim.conv2d(l4, 1, [3, 3], stride=2, scope='L6', padding='SAME')
    # Final addition.
    nn.relu(l5 + l6, name='output')

  return g
开发者ID:Ajaycs99,项目名称:tensorflow,代码行数:29,代码来源:receptive_field_test.py

示例11: build_feature_pyramid

    def build_feature_pyramid(self):

        '''
        reference: https://github.com/CharlesShang/FastMaskRCNN
        build P2, P3, P4, P5, P6
        :return: multi-scale feature map
        '''

        feature_pyramid = {}
        with tf.variable_scope('feature_pyramid'):
            with slim.arg_scope([slim.conv2d], weights_regularizer=slim.l2_regularizer(self.rpn_weight_decay)):
                feature_pyramid['P5'] = slim.conv2d(self.feature_maps_dict['C5'],
                                                    num_outputs=256,
                                                    kernel_size=[1, 1],
                                                    stride=1,
                                                    scope='build_P5')

                feature_pyramid['P6'] = slim.max_pool2d(feature_pyramid['P5'],
                                                        kernel_size=[2, 2], stride=2, scope='build_P6')
                # P6 is down sample of P5

                for layer in range(4, 1, -1):
                    p, c = feature_pyramid['P' + str(layer + 1)], self.feature_maps_dict['C' + str(layer)]
                    up_sample_shape = tf.shape(c)
                    up_sample = tf.image.resize_nearest_neighbor(p, [up_sample_shape[1], up_sample_shape[2]],
                                                                 name='build_P%d/up_sample_nearest_neighbor' % layer)

                    c = slim.conv2d(c, num_outputs=256, kernel_size=[1, 1], stride=1,
                                    scope='build_P%d/reduce_dimension' % layer)
                    p = up_sample + c
                    p = slim.conv2d(p, 256, kernel_size=[3, 3], stride=1,
                                    padding='SAME', scope='build_P%d/avoid_aliasing' % layer)
                    feature_pyramid['P' + str(layer)] = p

        return feature_pyramid
开发者ID:mbossX,项目名称:RRPN_FPN_Tensorflow,代码行数:35,代码来源:build_rpn.py

示例12: encoder

    def encoder(self, images, is_training):
        activation_fn = leaky_relu  # tf.nn.relu
        weight_decay = 0.0
        with tf.variable_scope('encoder'):
            with slim.arg_scope([slim.batch_norm],
                                is_training=is_training):
                with slim.arg_scope([slim.conv2d, slim.fully_connected],
                                    weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
                                    weights_regularizer=slim.l2_regularizer(weight_decay),
                                    normalizer_fn=slim.batch_norm,
                                    normalizer_params=self.batch_norm_params):
                    net = images
                    
                    net = slim.conv2d(net, 32, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_1a')
                    net = slim.repeat(net, 3, conv2d_block, 0.1, 32, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_1b')
                    
                    net = slim.conv2d(net, 64, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_2a')
                    net = slim.repeat(net, 3, conv2d_block, 0.1, 64, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_2b')

                    net = slim.conv2d(net, 128, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_3a')
                    net = slim.repeat(net, 3, conv2d_block, 0.1, 128, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_3b')

                    net = slim.conv2d(net, 256, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_4a')
                    net = slim.repeat(net, 3, conv2d_block, 0.1, 256, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_4b')
                    
                    net = slim.flatten(net)
                    fc1 = slim.fully_connected(net, self.latent_variable_dim, activation_fn=None, normalizer_fn=None, scope='Fc_1')
                    fc2 = slim.fully_connected(net, self.latent_variable_dim, activation_fn=None, normalizer_fn=None, scope='Fc_2')
        return fc1, fc2
开发者ID:NickyGeorge,项目名称:facenet,代码行数:29,代码来源:dfc_vae_resnet.py

示例13: conv_net_kelz

def conv_net_kelz(inputs):
  """Builds the ConvNet from Kelz 2016."""
  with slim.arg_scope(
      [slim.conv2d, slim.fully_connected],
      activation_fn=tf.nn.relu,
      weights_initializer=tf.contrib.layers.variance_scaling_initializer(
          factor=2.0, mode='FAN_AVG', uniform=True)):
    net = slim.conv2d(
        inputs, 32, [3, 3], scope='conv1', normalizer_fn=slim.batch_norm)

    net = slim.conv2d(
        net, 32, [3, 3], scope='conv2', normalizer_fn=slim.batch_norm)
    net = slim.max_pool2d(net, [1, 2], stride=[1, 2], scope='pool2')
    net = slim.dropout(net, 0.25, scope='dropout2')

    net = slim.conv2d(
        net, 64, [3, 3], scope='conv3', normalizer_fn=slim.batch_norm)
    net = slim.max_pool2d(net, [1, 2], stride=[1, 2], scope='pool3')
    net = slim.dropout(net, 0.25, scope='dropout3')

    # Flatten while preserving batch and time dimensions.
    dims = tf.shape(net)
    net = tf.reshape(net, (dims[0], dims[1],
                           net.shape[2].value * net.shape[3].value), 'flatten4')

    net = slim.fully_connected(net, 512, scope='fc5')
    net = slim.dropout(net, 0.5, scope='dropout5')

    return net
开发者ID:Alice-ren,项目名称:magenta,代码行数:29,代码来源:model.py

示例14: network_det

	def network_det(self,inputs,reuse=False):

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

		with slim.arg_scope([slim.conv2d, slim.fully_connected],
							 activation_fn = tf.nn.relu,
							 weights_initializer = tf.truncated_normal_initializer(0.0, 0.01)):
			
			conv1 = slim.conv2d(inputs, 96, [11,11], 4, padding= 'VALID', scope='conv1')
			max1 = slim.max_pool2d(conv1, [3,3], 2, padding= 'VALID', scope='max1')

			conv2 = slim.conv2d(max1, 256, [5,5], 1, scope='conv2')
			max2 = slim.max_pool2d(conv2, [3,3], 2, padding= 'VALID', scope='max2')
			conv3 = slim.conv2d(max2, 384, [3,3], 1, scope='conv3')

			conv4 = slim.conv2d(conv3, 384, [3,3], 1, scope='conv4')
			conv5 = slim.conv2d(conv4, 256, [3,3], 1, scope='conv5')
			pool5 = slim.max_pool2d(conv5, [3,3], 2, padding= 'VALID', scope='pool5')
			
			shape = int(np.prod(pool5.get_shape()[1:]))
			fc6 = slim.fully_connected(tf.reshape(pool5, [-1, shape]), 4096, scope='fc6')
			
			fc_detection = slim.fully_connected(fc6, 512, scope='fc_det1')
			out_detection = slim.fully_connected(fc_detection, 2, scope='fc_det2', activation_fn = None)
			
		return out_detection
开发者ID:dmehr,项目名称:HyperFace-TensorFlow-implementation,代码行数:27,代码来源:model_prediction.py

示例15: localization_squeezenet

	def localization_squeezenet(self, inputs):

		with tf.variable_scope('localization_network'):	
			with slim.arg_scope([slim.conv2d], activation_fn = tf.nn.relu,
									padding = 'SAME',
									weights_initializer = tf.constant_initializer(0.0)):

				conv1 = slim.conv2d(inputs, 64, [3,3], 2, padding = 'VALID', scope='conv1')
				pool1 = slim.max_pool2d(conv1, [2,2], 2, scope='pool1')
				fire2 = self.fire_module(pool1, 16, 64, scope = 'fire2')
				fire3 = self.fire_module(fire2, 16, 64, scope = 'fire3', res_connection=True)
				fire4 = self.fire_module(fire3, 32, 128, scope = 'fire4')
				pool4 = slim.max_pool2d(fire4, [2,2], 2, scope='pool4')
				fire5 = self.fire_module(pool4, 32, 128, scope = 'fire5', res_connection=True)
				fire6 = self.fire_module(fire5, 48, 192, scope = 'fire6')
				fire7 = self.fire_module(fire6, 48, 192, scope = 'fire7', res_connection=True)
				fire8 = self.fire_module(fire7, 64, 256, scope = 'fire8')
				pool8 = slim.max_pool2d(fire8, [2,2], 2, scope='pool8')
				fire9 = self.fire_module(pool8, 64, 256, scope = 'fire9', res_connection=True)
				conv10 = slim.conv2d(fire9, 128, [1,1], 1, scope='conv10')
				shape = int(np.prod(conv10.get_shape()[1:]))
				identity = np.array([[1., 0., 0.],
									[0., 1., 0.]])
				identity = identity.flatten()
				fc11 = slim.fully_connected(tf.reshape(conv10, [-1, shape]), 6, biases_initializer = tf.constant_initializer(identity), scope='fc11')
		return fc11
开发者ID:dmehr,项目名称:HyperFace-TensorFlow-implementation,代码行数:26,代码来源:model.py


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