问题描述
我正在按以下方式实现CNN，但出现了此错误：
I am implementing the CNN as below, but I got this error:
我在下面附加了我的部分代码。我怀疑错误是由于我的体重和偏见的形状和尺寸引起的。
I have attached my partial code below. I suspect the error is coming from the shapes and dimensions of my weight and biases.
我要实现的目标-我想将CNN层减少两个
What I'm trying to implement - I want to reduce the CNN layers from two fully connected layers to just one fully connected layer, meaning, out=tf.add(tf.add(fc1....) and stop it there.
nInput = 32
nChannels = 3
nClasses = 10
# Placeholder and drop-out
X = tf.placeholder(tf.float32, [None, nInput, nInput, nChannels])
Y = tf.placeholder(tf.float32, [None, nClasses])
keep_prob = tf.placeholder(tf.float32)
def conv2d(x, W, b, strides=1):
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)
def maxpool2d(x, k=2):
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')
def normalize_layer(pooling):
#norm = tf.nn.lrn(pooling, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1')
norm = tf.contrib.layers.batch_norm(pooling, center=True, scale=True)
return norm
def drop_out(fc, keep_prob=0.4):
drop_out = tf.layers.dropout(fc, rate=keep_prob)
return drop_out
weights = {
'WC1': tf.Variable(tf.random_normal([5, 5, 3, 32]), name='W0'),
'WC2': tf.Variable(tf.random_normal([5*5*32, 64]), name='W1'),
#'WD1': tf.Variable(tf.random_normal([8 * 8 * 64, 64]), name='W2'),
#'WD2': tf.Variable(tf.random_normal([64, 128]), name='W3'),
'out': tf.Variable(tf.random_normal([64, nClasses]), name='W5')
}
biases = {
'BC1': tf.Variable(tf.random_normal([32]), name='B0'),
'BC2': tf.Variable(tf.random_normal([64]), name='B1'),
#'BD1': tf.Variable(tf.random_normal([64]), name='B2'),
#'BD2': tf.Variable(tf.random_normal([128]), name='B3'),
'out': tf.Variable(tf.random_normal([nClasses]), name='B5')
}
def conv_net(x, weights, biases):
conv1 = conv2d(x, weights['WC1'], biases['BC1'])
conv1 = maxpool2d(conv1)
conv1 = normalize_layer(conv1)
#conv2 = conv2d(conv1, weights['WC2'], biases['BC2'])
#conv2 = maxpool2d(conv2)
#conv2 = normalize_layer(conv2)
fc1 = tf.reshape(conv1, [-1, weights['WC2'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['WC2']), biases['BC2'])
fc1 = tf.nn.relu(fc1) # Using self-normalization activation
fc1 = drop_out(fc1)
#fc2 = tf.add(tf.matmul(fc1, weights['WD2']), biases['BD2'])
#fc2 = tf.nn.selu(fc2) # Using self-normalization activation
#fc2 = drop_out(fc2)
out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
out = tf.nn.softmax(out)
return out
推荐答案
我认为权重字典的 WC2参数有问题。它应该是'WC2'：tf.Variable（tf.random_normal（[16 * 16 * 32，64]），name ='W1'）
I think there is something wrong with 'WC2' parameter of weights dictionary. It should be 'WC2': tf.Variable(tf.random_normal([16*16*32, 64]), name='W1')
应用 1 卷积和最大池运算后，您将对 32 x 32 x的输入图像进行下采样3 到 16 x 16 x 3 ，现在您需要展平该降采样的输出，以将其作为输入输入到完全连接的层。这就是为什么您需要通过 16 * 16 * 32 。
After applying 1 convolution and max-pooling operations, you are downsampling the input image from 32 x 32 x 3 to 16 x 16 x 3 and now you need to flatten this downsampled output to feed this as input to the fully connected layer. That's why you need to pass 16*16*32.
这篇关于CIFAR-10 TensorFlow：InvalidArgumentError（请参见上文的回溯）：logits和标签必须可广播的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！