有人可以用张量流（手写数字）向我解释卷积神经网络的设计吗？（输入图像：28 x 28 | 输出：10 (n_classes)）

Question

我正在尝试开始 CNN 设计，我发现了这段代码，我试图从（f.maps 大小、步幅 ....）推断设计。

据我了解，我们有：输入 --> Conv5-32 --> maxpool --> Conv5-5 --> maxpool --> fc1 --> outputs.

我没弄清楚的是 fc1 的输入，为什么它是 7 x 7？

有人可以帮助我吗？（我是初学者）

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('/tmp/data/', one_hot=True)


#Parameters
learning_rate = 0.001
training_iters = 200000
batch_size = 28
display_step = 10


#Network Parameters

n_input = 784
n_output = 10
dropout  = 0.75

#tf grath input
x = tf.placeholder(tf.float32, [None,n_input])
y = tf.placeholder(tf.float32, [None, n_output])
keep_prob = tf.placeholder(tf.float32)



# Create some wrappers for simplicity
def conv2d(x, W, b, strides=1):
    # 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)


def maxpool2d(x, k=2):
    # MaxPool2D wrapper
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
                          padding='SAME')




# Create model
def conv_net(x, weights, biases, dropout):
    # Reshape input picture
    x = tf.reshape(x, shape=[-1, 28, 28, 1])

    # Convolution Layer
    conv1 = conv2d(x, weights['wc1'], biases['bc1'])
    # Max Pooling (down-sampling)
    conv1 = maxpool2d(conv1, k=2)

    # Convolution Layer
    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
    # Max Pooling (down-sampling)
    conv2 = maxpool2d(conv2, k=2)

    # Fully connected layer
    # Reshape conv2 output to fit fully connected layer input
    fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
    fc1 = tf.nn.relu(fc1)
    # Apply Dropout
    fc1 = tf.nn.dropout(fc1, dropout)

    # Output, class prediction
    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
    return out




# Store layers weight & bias
weights = {
    # 5x5 conv, 1 input, 32 outputs
    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
    # 5x5 conv, 32 inputs, 64 outputs
    'wc2': tf.Variable(tf.random_normal([5, 5, 32, 5])),
    # fully connected, 7*7*64 inputs, 1024 outputs
    'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),
    # 1024 inputs, 10 outputs (class prediction)
    'out': tf.Variable(tf.random_normal([1024, n_classes]))
}

biases = {
    'bc1': tf.Variable(tf.random_normal([32])),
    'bc2': tf.Variable(tf.random_normal([64])),
    'bd1': tf.Variable(tf.random_normal([1024])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

Answer 1

这是因为最大池化。它在每个维度上将输入的大小除以 2。

所以在第一次最大池之后，你的 28x28 变成 14x14，然后在第二次之后变成 7x7。

Answer 2

希望我的下图能解开你的疑惑

有人可以用张量流（手写数字）向我解释卷积神经网络的设计吗？（输入图像：28 x 28 | 输出：10 (n_classes)）

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有人可以用张量流（手写数字）向我解释卷积神经网络的设计吗？ （输入图像：28 x 28 | 输出：10 (n_classes)）

Someone can explain to me the design of a Convolutional NN with tensorflow (hand written digits)? (input img : 28 x 28 | output : 10 (n_classes))

machine-learning

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有人可以用张量流（手写数字）向我解释卷积神经网络的设计吗？（输入图像：28 x 28 | 输出：10 (n_classes)）