Question

我正在建立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 该数据有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].

执行时：

pred = conv_net(x, weights, biases)

我经历了多个conv2D模型，但是大多数模型都是用于图像分类的，在这里我可能会丢失一些我无法识别的东西。请帮忙。

Answer 1

权重int mode = 1; Mode m = Mode.values()[mode - 1]; //m will be ONE，wc2和wc3的输入通道数必须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：

wc4

卷积神经网络：权重和偏差初始化

1 个答案: