我从这里得到了张量流mnist treaining的例子
并添加了我从这里得到的第一个卷积层可视化代码：
https://gist.github.com/kukuruza/03731dc494603ceab0c5
（我稍微修改了代码以使其适用于灰度图像）
我看到的是在训练期间图像根本没有变化！ 但是，如果我用零而不是随机值初始化第一层，则会发生更改。我用张量板可视化结果。完整代码如下。
我想知道，代码中是否有任何错误，或者我们真的不需要第一个卷积层来对mnist进行分类？
from __future__ import print_function
import tensorflow as tf
# Import MNIST data
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 = 128
display_step = 10
# Network Parameters
n_input = 784 # MNIST data input (img shape: 28*28)
n_classes = 10 # MNIST total classes (0-9 digits)
dropout = 0.75 # Dropout, probability to keep units
# tf Graph input
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)
# 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, 64])),
# 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]))
}
def put_kernels_on_grid (kernel, grid_Y, grid_X, pad = 1):
'''Visualize conv. features as an image (mostly for the 1st layer).
Place kernel into a grid, with some paddings between adjacent filters.
Args:
kernel: tensor of shape [Y, X, NumChannels, NumKernels]
(grid_Y, grid_X): shape of the grid. Require: NumKernels == grid_Y * grid_X
User is responsible of how to break into two multiples.
pad: number of black pixels around each filter (between them)
Return:
Tensor of shape [(Y+2*pad)*grid_Y, (X+2*pad)*grid_X, NumChannels, 1].
'''
x_min = tf.reduce_min(kernel)
x_max = tf.reduce_max(kernel)
kernel1 = (kernel - x_min) / (x_max - x_min)
# pad X and Y
x1 = tf.pad(kernel1, tf.constant( [[pad,pad],[pad, pad],[0,0],[0,0]] ), mode = 'CONSTANT')
# X and Y dimensions, w.r.t. padding
Y = kernel1.get_shape()[0] + 2 * pad
X = kernel1.get_shape()[1] + 2 * pad
channels = kernel1.get_shape()[2]
# put NumKernels to the 1st dimension
x2 = tf.transpose(x1, (3, 0, 1, 2))
# organize grid on Y axis
x3 = tf.reshape(x2, tf.pack([grid_X, Y * grid_Y, X, channels])) #3
# switch X and Y axes
x4 = tf.transpose(x3, (0, 2, 1, 3))
# organize grid on X axis
x5 = tf.reshape(x4, tf.pack([1, X * grid_X, Y * grid_Y, channels])) #3
# back to normal order (not combining with the next step for clarity)
x6 = tf.transpose(x5, (2, 1, 3, 0))
# to tf.image_summary order [batch_size, height, width, channels],
# where in this case batch_size == 1
x7 = tf.transpose(x6, (3, 0, 1, 2))
# scale to [0, 255] and convert to uint8
return tf.image.convert_image_dtype(x7, dtype = tf.uint8)
# Construct model
pred = conv_net(x, weights, biases, keep_prob)
# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# Evaluate model
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
grid = put_kernels_on_grid (weights['wc1'], grid_Y = 4, grid_X = 8)
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
train_writer = tf.train.SummaryWriter('./train', sess.graph)
sess.run(init)
step = 1
i = 0
# Keep training until reach max iterations
while step * batch_size < training_iters:
wc1_summary = tf.image_summary('conv1/features'+ str(i), grid, max_images = 1)
batch_x, batch_y = mnist.train.next_batch(batch_size)
# Run optimization op (backprop)
_, summary = sess.run([optimizer, wc1_summary], feed_dict={x: batch_x, y: batch_y,
keep_prob: dropout})
train_writer.add_summary(summary)
if step % display_step == 0:
# Calculate batch loss and accuracy
loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,
y: batch_y,
keep_prob: 1.})
print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
"{:.6f}".format(loss) + ", Training Accuracy= " + \
"{:.5f}".format(acc))
step += 1
i += 1
print("Optimization Finished!")
# Calculate accuracy for 256 mnist test images
print("Testing Accuracy:", \
sess.run(accuracy, feed_dict={x: mnist.test.images[:256],
y: mnist.test.labels[:256],
keep_prob: 1.}))
答案 0 :(得分：1)
很可能该层中的梯度值变得太低，因此很难或不可能看到它们的更新。
梯度消失是深度网络的常见问题。
您可以检查是否是您的情况：