First_Purification.py 文件源码

def finalTrainingLayer(classCount, finalTensorName, bottleneckTensor):
    with tf.name_scope('input'):
        bottleneckInput = tf.placeholder_with_default(
            bottleneckTensor, shape = [None, BOTTLENECK_TENSOR_SIZE],
            name = 'BottleneckInputPlaceholder')

    groundTruthInput = tf.placeholder(tf.float32,
                                      [None, classCount],
                                      name = 'GroundTruthInput')
    layerName = 'finalLayer'
    with tf.name_scope(layerName):
        with tf.name_scope('weights'):
            initialValue = tf.truncated_normal([BOTTLENECK_TENSOR_SIZE, classCount],
                                               stddev=0.001)
            layerWeights = tf.Variable(initialValue, name = 'finalWeights')
            tensorBoardUsage(layerWeights)
        with tf.name_scope('biases'):
            layerBiases = tf.Variable(tf.zeros([classCount]), name='finalBiases')
            tensorBoardUsage(layerBiases)
        with tf.name_scope('WxPlusB'):
            logits = tf.matmul(bottleneckInput, layerWeights) + layerBiases
            tf.summary.histogram('pre_activations', logits)

    finalTensor = tf.nn.softmax(logits, name=finalTensorName)
    tf.summary.histogram('activations', finalTensor)

    with tf.name_scope('crossEntropy'):
        crossEntropy = tf.nn.softmax_cross_entropy_with_logits(
                       labels=groundTruthInput, logits=logits)
        with tf.name_scope('total'):
            crossEntropyMean = tf.reduce_mean(crossEntropy)
    tf.summary.scalar('cross_entropy', crossEntropyMean)

    with tf.name_scope('train'):
        optimizer = tf.train.GradientDescentOptimizer(LEARNING_RATE)
        trainStep = optimizer.minimize(crossEntropyMean)

    return (trainStep, crossEntropyMean, bottleneckInput, groundTruthInput,
            finalTensor)