alexnet.py 文件源码

def classifier(x, dropout):
    """
    AlexNet fully connected layers definition

    Args:
        x: tensor of shape [batch_size, width, height, channels]
        dropout: probability of non dropping out units

    Returns:
        fc3: 1000 linear tensor taken just before applying the softmax operation
            it is needed to feed it to tf.softmax_cross_entropy_with_logits()
        softmax: 1000 linear tensor representing the output probabilities of the image to classify

    """
    pool5 = cnn(x)

    dim = pool5.get_shape().as_list()
    flat_dim = dim[1] * dim[2] * dim[3] # 6 * 6 * 256
    flat = tf.reshape(pool5, [-1, flat_dim])

    with tf.name_scope('alexnet_classifier') as scope:
        with tf.name_scope('alexnet_classifier_fc1') as inner_scope:
            wfc1 = tu.weight([flat_dim, 4096], name='wfc1')
            bfc1 = tu.bias(0.0, [4096], name='bfc1')
            fc1 = tf.add(tf.matmul(flat, wfc1), bfc1)
            #fc1 = tu.batch_norm(fc1)
            fc1 = tu.relu(fc1)
            fc1 = tf.nn.dropout(fc1, dropout)

        with tf.name_scope('alexnet_classifier_fc2') as inner_scope:
            wfc2 = tu.weight([4096, 4096], name='wfc2')
            bfc2 = tu.bias(0.0, [4096], name='bfc2')
            fc2 = tf.add(tf.matmul(fc1, wfc2), bfc2)
            #fc2 = tu.batch_norm(fc2)
            fc2 = tu.relu(fc2)
            fc2 = tf.nn.dropout(fc2, dropout)

        with tf.name_scope('alexnet_classifier_output') as inner_scope:
            wfc3 = tu.weight([4096, 1000], name='wfc3')
            bfc3 = tu.bias(0.0, [1000], name='bfc3')
            fc3 = tf.add(tf.matmul(fc2, wfc3), bfc3)
            softmax = tf.nn.softmax(fc3)

    return fc3, softmax