2 qq 28254507 qq_28254507 于 2018.02.14 17:22 提问

Tensorflow实现简单CNN模型中某一层shape的计算问题 5C

RT,网上看到一篇资料,实现了一个简单的CNN模型,但是有个shape我有点蒙,不知道怎么算的,代码如下:
这是alexnet网络定义的部分 ,我们只需要修改这一部就可以了

 def alex_net(_X, _weights, _biases, _dropout):
    # Reshape input picture
    _X = tf.reshape(_X, shape=[-1, 28, 28, 1])

    # Convolution Layer
    conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'])
    # Max Pooling (down-sampling)
    pool1 = max_pool('pool1', conv1, k=2)
    # Apply Normalization
    norm1 = norm('norm1', pool1, lsize=4)
    # Apply Dropout
    norm1 = tf.nn.dropout(norm1, _dropout)

    # Convolution Layer
    conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'])
    # Max Pooling (down-sampling)
    pool2 = max_pool('pool2', conv2, k=2)
    # Apply Normalization
    norm2 = norm('norm2', pool2, lsize=4)
    # Apply Dropout
    norm2 = tf.nn.dropout(norm2, _dropout)

    # Convolution Layer
    conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'])
    # Max Pooling (down-sampling)
    pool3 = max_pool('pool3', conv3, k=2)
    # Apply Normalization
    norm3 = norm('norm3', pool3, lsize=4)
    # Apply Dropout
    norm3 = tf.nn.dropout(norm3, _dropout)

    # Fully connected layer
    dense1 = tf.reshape(norm3, [-1, _weights['wd1'].get_shape().as_list()[0]]) # Reshape conv3 output to fit dense layer input
    dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1') # Relu activation

    dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') # Relu activation

    # Output, class prediction
    out = tf.matmul(dense2, _weights['out']) + _biases['out']
    return out

# Store layers weight & bias
weights = {
    'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])),
    'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])),
    'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])),
    'wd1': tf.Variable(tf.random_normal([4*4*256, 1024])),
    'wd2': tf.Variable(tf.random_normal([1024, 1024])),
    'out': tf.Variable(tf.random_normal([1024, 10]))
}
biases = {
    'bc1': tf.Variable(tf.random_normal([64])),
    'bc2': tf.Variable(tf.random_normal([128])),
    'bc3': tf.Variable(tf.random_normal([256])),
    'bd1': tf.Variable(tf.random_normal([1024])),
    'bd2': tf.Variable(tf.random_normal([1024])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

# Construct model
pred = alex_net(x, weights, biases, keep_prob)

weights 项下的“wd1”,shape的输入处是4*4*256,4是怎么算出来的,学生自学所以不是很明白,各位帮忙解释一下

资料的网址:TensorFlow人工智能引擎入门教程之三 实现一个自创的CNN卷积神经网络 - zhuyuping的个人空间
https://my.oschina.net/yilian/blog/661409

2个回答

devmiao
devmiao   Ds   Rxr 2018.02.14 23:51
loorygo
loorygo   2018.02.17 15:41

相邻的4个像素 * 每个象素占4个字节* (一个字节表示的最大数值255 + 1)

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准确详细的回答,更有利于被提问者采纳,从而获得C币。复制、灌水、广告等回答会被删除,是时候展现真正的技术了!