卷积神经网络：权重和偏置初始化

Question

我正在构建卷积神经网络以将数据分类为不同的类别输入数据的形状为：30000、6、15、1 数据有 30000 个样本、15 个预测变量和 6 个可能的类别。

我的模型定义如下

x = tf.placeholder("float", [None, 6,15,1])
y = tf.placeholder("float", [None, n_classes])

#Define Weights
weights = {
    'wc1': tf.get_variable('W0', shape=(3,3,1,8), initializer=tf.contrib.layers.xavier_initializer()), 
    'wc2': tf.get_variable('W1', shape=(3,3,32,12), initializer=tf.contrib.layers.xavier_initializer()), 
    'wc3': tf.get_variable('W2', shape=(3,3,64,16), initializer=tf.contrib.layers.xavier_initializer()), 
    'wc4': tf.get_variable('W3', shape=(3,3,64,20), initializer=tf.contrib.layers.xavier_initializer()),
    'wd1': tf.get_variable('W4', shape=(4*4*15,15), initializer=tf.contrib.layers.xavier_initializer()), 
    'out': tf.get_variable('W6', shape=(15,n_classes), initializer=tf.contrib.layers.xavier_initializer()), 
}

biases = {
    'bc1': tf.get_variable('B0', shape=(8), initializer=tf.contrib.layers.xavier_initializer()),
    'bc2': tf.get_variable('B1', shape=(12), initializer=tf.contrib.layers.xavier_initializer()),
    'bc3': tf.get_variable('B2', shape=(16), initializer=tf.contrib.layers.xavier_initializer()),
    'bc4': tf.get_variable('B3', shape=(20), initializer=tf.contrib.layers.xavier_initializer()),
    'bd1': tf.get_variable('B4', shape=(15), initializer=tf.contrib.layers.xavier_initializer()),
    'out': tf.get_variable('B5', shape=(6), initializer=tf.contrib.layers.xavier_initializer()),
}

#Define convolutional layer
def conv2d(x, W, b, strides=1, reuse=True):
    # Conv2D wrapper, with bias and relu activation
    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
    x = tf.nn.bias_add(x, b)
    return tf.nn.relu(x)

#Define Maxpool layer
def maxpool2d(x, k=2):
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],padding='SAME')

#Define a convolutional neural network function
def conv_net(x, weights, biases):  
    conv1 = conv2d(x, weights['wc1'], biases['bc1'])
    conv1 = maxpool2d(conv1, k=2)

    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
    conv2 = maxpool2d(conv2, k=2)

    conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
    conv3 = maxpool2d(conv3, k=2)

    conv4 = conv2d(conv3, weights['wc4'], biases['bc4'])
    conv4 = maxpool2d(conv4, k=2)


    # Fully connected layer
    # Reshape conv2 output to fit fully connected layer input
    fc1 = tf.reshape(conv4, [-1, weights['wd1'].get_shape().as_list()[0]])
    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
    fc1 = tf.nn.relu(fc1)
    # Output, class prediction 
    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
    return out

我收到错误消息：ValueError: Dimensions must be equal, but are 8 and 32 for 'Conv2D_1' (op: 'Conv2D') with input shapes: [?,8,3,8], [3,3,32,12].

当我执行时：

pred = conv_net(x, weights, biases)

我经历了多个 conv2D 模型，但其中大部分用于图像分类，我可能在这里遗漏了一些我无法识别的东西。请帮忙。

Answer 1

权重 wc2、wc3 和 wc4 的输入通道数需要与前一层的输出通道数相同。保持输出通道的数量不变，它们将更改为：

    'wc1': tf.get_variable('W0', shape=(3,3,1,8), initializer=tf.contrib.layers.xavier_initializer()),
    'wc2': tf.get_variable('W1', shape=(3,3,8,12), initializer=tf.contrib.layers.xavier_initializer()),
    'wc3': tf.get_variable('W2', shape=(3,3,12,16), initializer=tf.contrib.layers.xavier_initializer()),
    'wc4': tf.get_variable('W3', shape=(3,3,16,20), initializer=tf.contrib.layers.xavier_initializer()),

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