AutoEncoder形状

Question

我尝试在Tensorflow中创建一个自动编码器，而不使用contriib。这是原始代码

autoencoder.py

下面是我修改的程序：

    import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt




ae_inputs = tf.placeholder(tf.float32, (None, 32, 32, 1))  # input to the network (MNIST images)


xi = tf.nn.conv2d(ae_inputs, 
                 filter=tf.Variable(tf.random_normal([5,5,1,32])), 
                 strides=[1,2,2,1],
                 padding='SAME')
print("xi {0}".format(xi))

xi = tf.nn.conv2d(xi, 
                 filter=tf.Variable(tf.random_normal([5,5,32,16])), 
                 strides=[1,2,2,32],
                 padding='SAME')
print("xi {0}".format(xi))

xi = tf.nn.conv2d(xi, 
                 filter=tf.Variable(tf.random_normal([5,5,16,8])), 
                 strides=[1,4,4,16],
                 padding='SAME')
print("xi {0}".format(xi))






xo = tf.nn.conv2d_transpose(xi, 
                 filter=tf.Variable(tf.random_normal([5,5,16,8])), 
                 output_shape=[1, 8, 8, 16],
                 strides=[1,4,4,1],
                 padding='SAME')
print("xo {0}".format(xo))

xo = tf.nn.conv2d_transpose(xo, 
                 filter=tf.Variable(tf.random_normal([5,5,32,16])), 
                 output_shape=[1, 16, 16, 32],
                 strides=[1,2,2,1],
                 padding='SAME')
print("xo {0}".format(xo))

xo = tf.nn.conv2d_transpose(xo, 
                 filter=tf.Variable(tf.random_normal([5,5,1,32])), 
                 output_shape=[1, 32, 32, 1],
                 strides=[1,2,2,1],
                 padding='SAME')

print("xo {0}".format(xo))

印刷的结果是：

xi张量(“conv2D:0”，shape=(?，16，16，32)，dtype=float32) xi张量(“Conv2D_1:0”，shape=(?，8，8，16)，dtype=float32) xi张量(“Conv2D_2:0”，shape=(?，2，2，8)，dtype=float32) xo张量(“conv2d_transpose:0”，shape=(1，8，8，16)，dtype=float32) xo张量(“conv2d_transpose_1:0”，shape=(1，16，16，32)，( dtype=float32) xo张量(“conv2d_transpose_2:0”，shape=(1，32，32，1)，dtype=float32)

输出似乎具有良好的形状，但我不太确定conv2和conv2_transpose中的所有参数。

如果需要，有人能纠正我的代码吗？

编辑：@刘，当你告诉我时，我添加了relu函数，但我不知道在哪里添加偏见：

xi = tf.nn.conv2d(ae_inputs,
             filter=tf.Variable(tf.random_normal([5,5,1,32])),
             strides=[1,2,2,1],
             padding='SAME')
xi = tf.nn.relu(xi)
# xi = max_pool(xi,2)
print("xi {0}".format(xi))

xi = tf.nn.conv2d(xi,
                 filter=tf.Variable(tf.random_normal([5,5,32,16])),
                 strides=[1,2,2,1],
                 padding='SAME')
xi = tf.nn.relu(xi)
# xi = max_pool(xi,2)
print("xi {0}".format(xi))

xi = tf.nn.conv2d(xi,
                 filter=tf.Variable(tf.random_normal([5,5,16,8])),
                 strides=[1,4,4,1],
                 padding='SAME')
xi = tf.nn.relu(xi)
# xi = max_pool(xi,4)
print("xi {0}".format(xi))






xo = tf.nn.conv2d_transpose(xi,
                 filter=tf.Variable(tf.random_normal([5,5,16,8])),
                 output_shape=[tf.shape(xi)[0], 8, 8, 16],
                 strides=[1,4,4,1],
                 padding='SAME')
xo = tf.nn.relu(xo)

print("xo {0}".format(xo))

xo = tf.nn.conv2d_transpose(xo,
                 filter=tf.Variable(tf.random_normal([5,5,32,16])),
                 output_shape=[tf.shape(xo)[0], 16, 16, 32],
                 strides=[1,2,2,1],
                 padding='SAME')
xo = tf.nn.relu(xo)

print("xo {0}".format(xo))

xo = tf.nn.conv2d_transpose(xo,
                 filter=tf.Variable(tf.random_normal([5,5,1,32])),
                 output_shape=[tf.shape(xo)[0], 32, 32, 1],
                 strides=[1,2,2,1],
                 padding='SAME')
xo = tf.nn.tanh(xo)
print("xo {0}".format(xo))
return xo

我不明白原始代码有什么区别：

# encoder
# 32 x 32 x 1   ->  16 x 16 x 32
# 16 x 16 x 32  ->  8 x 8 x 16
# 8 x 8 x 16    ->  2 x 2 x 8
print('inputs {0}'.format(inputs))

net = lays.conv2d(inputs, 32, [5, 5], stride=2, padding='SAME')
print('net {0}'.format(net))

net = lays.conv2d(net, 16, [5, 5], stride=2, padding='SAME')
print('net {0}'.format(net))

net = lays.conv2d(net, 8, [5, 5], stride=4, padding='SAME')
print('net {0}'.format(net))

# decoder
# 2 x 2 x 8    ->  8 x 8 x 16
# 8 x 8 x 16   ->  16 x 16 x 32
# 16 x 16 x 32  ->  32 x 32 x 1
net = lays.conv2d_transpose(net, 16, [5, 5], stride=4, padding='SAME')
print('net {0}'.format(net))

net = lays.conv2d_transpose(net, 32, [5, 5], stride=2, padding='SAME')
print('net {0}'.format(net))

net = lays.conv2d_transpose(net, 1, [5, 5], stride=2, padding='SAME', activation_fn=tf.nn.tanh)

print('net {0}'.format(net))
return net

Edit2：

@刘我根据您的修改制作了新版本的自动编码器：

mean = 0
    stdvev = 0.1
    with tf.name_scope('L0'):
        xi = tf.nn.conv2d(ae_inputs,
                     filter=tf.truncated_normal([5,5,1,32], mean = mean, stddev=stdvev),
                     strides=[1,1,1,1],
                     padding='SAME')
        xi =  tf.nn.bias_add(xi, bias_variable([32]))
        xi = max_pool(xi,2)
        print("xi {0}".format(xi))

    with tf.name_scope('L1'):
        xi = tf.nn.conv2d(xi,
                         filter=tf.truncated_normal([5,5,32,16], mean = mean, stddev=stdvev),
                         strides=[1,1,1,1],
                         padding='SAME')
        xi =  tf.nn.bias_add(xi, bias_variable([16]))
        xi = max_pool(xi,2)
        print("xi {0}".format(xi))

    with tf.name_scope('L2'):
        xi = tf.nn.conv2d(xi,
                         filter=tf.truncated_normal([5,5,16,8], mean = mean, stddev=stdvev),
                         strides=[1,1,1,1],
                         padding='SAME')
        xi =  tf.nn.bias_add(xi, bias_variable([8]))
        xi = max_pool(xi,4)
        print("xi {0}".format(xi))


    with tf.name_scope('L3'):
        xo = tf.nn.conv2d_transpose(xi,
                         filter=tf.truncated_normal([5,5,16,8], mean = mean, stddev=stdvev),
                         output_shape=[tf.shape(xi)[0], 8, 8, 16],
                         strides=[1,4,4,1],
                         padding='SAME')
        xo =  tf.nn.bias_add(xo, bias_variable([16]))
        print("xo {0}".format(xo))

    with tf.name_scope('L4'):
        xo = tf.nn.conv2d_transpose(xo,
                         filter=tf.truncated_normal([5,5,32,16], mean = mean, stddev=stdvev),
                         output_shape=[tf.shape(xo)[0], 16, 16, 32],
                         strides=[1,2,2,1],
                         padding='SAME')
        xo =  tf.nn.bias_add(xo, bias_variable([32]))
        print("xo {0}".format(xo))

    with tf.name_scope('L5'):
        xo = tf.nn.conv2d_transpose(xo,
                         filter=tf.truncated_normal([5,5,1,32], mean = mean, stddev=stdvev),
                         output_shape=[tf.shape(xo)[0], 32, 32, 1],
                         strides=[1,2,2,1],
                         padding='SAME')
        xo =  tf.nn.bias_add(xo, bias_variable([1]))
        xo = tf.nn.tanh(xo)
        print("xo {0}".format(xo))

但是结果是一样的，解码的值是不一样的。

Edit3：

我将过滤器定义更改为

filter=tf.truncated_normal([5,5,16,8], mean = mean, stddev=stdvev),

至

 filter= tf.get_variable('filter2',[5,5,16,8]),

结果似乎收敛到更好的结果，但仍然收敛到一个不同的价值。在原始代码(0.006)和我的版本0.015中。我认为它来自于滤波器的初始化值和偏差。我怎么能做到这一点？

Answer 1

你忘了一个偏见和一个激活。所以你的网络比PCA弱。我建议你用tf.layers代替。如果您想使用tf.nn，那么请使用tf.get_variable。此外，还必须添加：tf.nn.bias_add tf.nn.relu (或任何其他激活)

如果您想知道代码是否有效，只需使用以下方法测试：

sess = tf.Session()
sess.run(tf.tf.global_variables_initializer())
test_output = sess.run(xo, feed_dict={ae_inputs : np.random.random((1, 32, 32, 1))}
print(test_output)

编辑 Ok，所以您发布的代码基本上使用了tf.layers API，其中包含了偏见和激活。tf.nn API更基本，只适用卷积，但没有激活或偏置。

基于您的编辑，我认为您希望在nn中实现CAE。典型的编码器层如下：

conv = tf.nn.conv2d(
                     nput=input_tensor,
                     filter=tf.get_variable("conv_weight_name", shape=[height,
                                                                width,
                                                                number_input_feature_maps,
                                                                number_output_feature_maps]),
                     strides=[1, 1, 1, 1],
                     padding="SAME")
bias = tf.nn.bias_add(conv, tf.get_variable("name_bias",
                                            [number_output_feature_maps]))
layer_out = tf.nn.relu(bias)

这是一个典型的转置卷积层。

conv_transpose = tf.nn.conv2d_transpose(value=input_tensor,
                       filter=tf.get_variable("deconnv_weight_name", shape=[height,
                                                                     width,
                                                                     number_output_feature_maps,
                                                                     number_input_feature_maps]),
                       output_shape=[batc_size, height_output, width_ouput, feature_maps_output],
                       strides=[1, 1, 1, 1])
bias = tf.nn.bias_add(conv_transpose, tf.get_variable("name_bias", shape=[number_output_feature_maps]))

layer_out = tf.nn.relu(bias)
           `

如果你对名字有疑问，就在逗号上问。