Python TensorflowUtils.avg_pool_2x2方法代码示例

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import avg_pool_2x2 [as 别名]
def vgg_net(weights, image):
    layers = (
        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',

        'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',

        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',
        'relu3_3'  # 'conv3_4', 'relu3_4', 'pool3',

        # 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
        # 'relu4_3', 'conv4_4', 'relu4_4', 'pool4',
        #
        # 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',
        # 'relu5_3', 'conv5_4', 'relu5_4'
    )

    net = {}
    current = image
    for i, name in enumerate(layers):
        kind = name[:4]
        if kind == 'conv':
            kernels, bias = weights[i][0][0][0][0]
            # matconvnet: weights are [width, height, in_channels, out_channels]
            # tensorflow: weights are [height, width, in_channels, out_channels]
            kernels = np.transpose(kernels, (1, 0, 2, 3))
            bias = bias.reshape(-1)
            current = utils.conv2d_basic(current, kernels, bias)
        elif kind == 'relu':
            current = tf.nn.relu(current)
        elif kind == 'pool':
            current = utils.avg_pool_2x2(current)
        net[name] = current

    assert len(net) == len(layers)
    return net

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import avg_pool_2x2 [as 别名]
def inference_fully_convolutional(dataset):
    '''
    Fully convolutional inference on notMNIST dataset
    :param datset: [batch_size, 28*28*1] tensor
    :return: logits
    '''
    dataset_reshaped = tf.reshape(dataset, [-1, 28, 28, 1])
    with tf.name_scope("conv1") as scope:
        W_conv1 = utils.weight_variable_xavier_initialized([3, 3, 1, 32], name="W_conv1")
        b_conv1 = utils.bias_variable([32], name="b_conv1")
        h_conv1 = tf.nn.relu(utils.conv2d_strided(dataset_reshaped, W_conv1, b_conv1))

    with tf.name_scope("conv2") as scope:
        W_conv2 = utils.weight_variable_xavier_initialized([3, 3, 32, 64], name="W_conv2")
        b_conv2 = utils.bias_variable([64], name="b_conv2")
        h_conv2 = tf.nn.relu(utils.conv2d_strided(h_conv1, W_conv2, b_conv2))

    with tf.name_scope("conv3") as scope:
        W_conv3 = utils.weight_variable_xavier_initialized([3, 3, 64, 128], name="W_conv3")
        b_conv3 = utils.bias_variable([128], name="b_conv3")
        h_conv3 = tf.nn.relu(utils.conv2d_strided(h_conv2, W_conv3, b_conv3))

    with tf.name_scope("conv4") as scope:
        W_conv4 = utils.weight_variable_xavier_initialized([3, 3, 128, 256], name="W_conv4")
        b_conv4 = utils.bias_variable([256], name="b_conv4")
        h_conv4 = tf.nn.relu(utils.conv2d_strided(h_conv3, W_conv4, b_conv4))

    with tf.name_scope("conv5") as scope:
        # W_conv5 = utils.weight_variable_xavier_initialized([2, 2, 256, 512], name="W_conv5")
        # b_conv5 = utils.bias_variable([512], name="b_conv5")
        # h_conv5 = tf.nn.relu(utils.conv2d_strided(h_conv4, W_conv5, b_conv5))
        h_conv5 = utils.avg_pool_2x2(h_conv4)

    with tf.name_scope("conv6") as scope:
        W_conv6 = utils.weight_variable_xavier_initialized([1, 1, 256, 10], name="W_conv6")
        b_conv6 = utils.bias_variable([10], name="b_conv6")
        logits = tf.nn.relu(utils.conv2d_basic(h_conv5, W_conv6, b_conv6))
        print logits.get_shape()
        logits = tf.reshape(logits, [-1, 10])
    return logits

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import avg_pool_2x2 [as 别名]
def vgg_net(weights, image):
    layers = (
        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',

        'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',

        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',
        'relu3_3', 'conv3_4', 'relu3_4', 'pool3',

        'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
        'relu4_3', 'conv4_4', 'relu4_4', 'pool4',

        'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',
        'relu5_3', 'conv5_4', 'relu5_4'
    )

    net = {}
    current = image
    for i, name in enumerate(layers):
        if name in ['conv3_4', 'relu3_4', 'conv4_4', 'relu4_4', 'conv5_4', 'relu5_4']:
            continue
        kind = name[:4]
        if kind == 'conv':
            kernels, bias = weights[i][0][0][0][0]
            # matconvnet: weights are [width, height, in_channels, out_channels]
            # tensorflow: weights are [height, width, in_channels, out_channels]
            kernels = utils.get_variable(np.transpose(kernels, (1, 0, 2, 3)), name=name + "_w")
            bias = utils.get_variable(bias.reshape(-1), name=name + "_b")
            current = utils.conv2d_basic(current, kernels, bias)
        elif kind == 'relu':
            current = tf.nn.relu(current, name=name)
            if FLAGS.debug:
                utils.add_activation_summary(current)
        elif kind == 'pool':
            current = utils.avg_pool_2x2(current)
        net[name] = current

    return net