卷積神經網絡：權重和偏差初始化

Question

我正在構建卷積神經網絡，以將數據分類為不同類別。輸入數據的形狀為：30000、6、15、1，數據具有30000個樣本，15個預測變量和6個可能的類別。

我的模型定義如下。

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

我收到錯誤消息： 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].

當我執行時：

pred = conv_net(x, weights, biases)

我經歷了多個conv2D模型，但是大多數模型都是用於圖像分類的，在這里我可能會丟失一些我無法識別的東西。 請幫忙。

Answer 1

權重wc2 ， wc3和wc4的輸入通道數必須與前一層的輸出通道數相同。 保持輸出通道的數量不變，它們將更改為：

    '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()),