为 mnist 数据集构建我的 CNN 时准确率非常低
Getting terribly low accuracy when building my CNN for the mnist dataset
如标题所述，我的 CNN 在 mnist 数据集上的准确性非常低 (~70%)。我的架构包括两个卷积层和两个完全连接的层。我很高兴我得到了它 运行 但现在我对如何优化我的代码感到困惑。任何帮助将不胜感激。
import os
import tensorflow as tf
import sys
import urllib
if sys.version_info[0] >= 3:
from urllib.request import urlretrieve
else:
from urllib import urlretrieve
LOGDIR = 'log3/'
GITHUB_URL ='https://raw.githubusercontent.com/mamcgrath/TensorBoard-TF-Dev-Summit-Tutorial/master/'
### MNIST EMBEDDINGS ###
mnist = tf.contrib.learn.datasets.mnist.read_data_sets(train_dir=LOGDIR + 'data', one_hot=True)
### Get a sprite and labels file for the embedding projector ###
urlretrieve(GITHUB_URL + 'labels_1024.tsv', LOGDIR + 'labels_1024.tsv')
urlretrieve(GITHUB_URL + 'sprite_1024.png', LOGDIR + 'sprite_1024.png')
# Add convolution layer
def conv_layer(input, size_in, size_out, name="conv"):
with tf.name_scope(name):
#w = tf.Variable(tf.zeros([5, 5, size_in, size_out]), name="W")
#b = tf.Variable(tf.zeros([size_out]), name="B")
w = tf.Variable(tf.truncated_normal([4, 4, size_in, size_out], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding="SAME")
act = tf.nn.relu(conv + b)
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
return tf.nn.max_pool(act, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
# Add fully connected layer
def fc_layer(input, size_in, size_out, name="fc"):
with tf.name_scope(name):
w = tf.Variable(tf.truncated_normal([size_in, size_out], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
act = tf.nn.relu(tf.matmul(input, w) + b)
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
return act
def mnist_model(learning_rate, use_two_conv, use_two_fc, hparam):
tf.reset_default_graph()
sess = tf.Session()
# Setup placeholders, and reshape the data
x = tf.placeholder(tf.float32, shape=[None, 784], name="x")
x_image = tf.reshape(x, [-1, 28, 28, 1])
tf.summary.image('input', x_image, 3)
y = tf.placeholder(tf.float32, shape=[None, 10], name="labels")
if use_two_conv:
conv1 = conv_layer(x_image, 1, 32, "conv1")
conv_out = conv_layer(conv1, 32, 64, "conv2")
else:
conv1 = conv_layer(x_image, 1, 64, "conv")
conv_out = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
flattened = tf.reshape(conv_out, [-1, 7 * 7 * 64])
if use_two_fc:
fc1 = fc_layer(flattened, 7 * 7 * 64, 1024, "fc1")
embedding_input = fc1
embedding_size = 1024
logits = fc_layer(fc1, 1024, 10, "fc2")
else:
embedding_input = flattened
embedding_size = 7*7*64
logits = fc_layer(flattened, 7*7*64, 10, "fc")
with tf.name_scope("xent"):
xent = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=y), name="xent")
tf.summary.scalar("xent", xent)
with tf.name_scope("train"):
train_step = tf.train.AdamOptimizer(learning_rate).minimize(xent)
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar("accuracy", accuracy)
summ = tf.summary.merge_all()
embedding = tf.Variable(tf.zeros([1024, embedding_size]), name="test_embedding")
assignment = embedding.assign(embedding_input)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter(LOGDIR + hparam)
writer.add_graph(sess.graph)
config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
embedding_config = config.embeddings.add()
embedding_config.tensor_name = embedding.name
embedding_config.sprite.image_path = LOGDIR + 'sprite_1024.png'
embedding_config.metadata_path = LOGDIR + 'labels_1024.tsv'
# Specify the width and height of a single thumbnail.
embedding_config.sprite.single_image_dim.extend([28, 28])
tf.contrib.tensorboard.plugins.projector.visualize_embeddings(writer, config)
for i in range(201):
batch = mnist.train.next_batch(100)
if i % 5 == 0:
[train_accuracy, s] = sess.run([accuracy, summ], feed_dict={x: batch[0], y: batch[1]})
writer.add_summary(s, i)
print (train_accuracy)
if i % 500 == 0:
sess.run(assignment, feed_dict={x: mnist.test.images[:1024], y: mnist.test.labels[:1024]})
saver.save(sess, os.path.join(LOGDIR, "model.ckpt"), i)
sess.run(train_step, feed_dict={x: batch[0], y: batch[1]})
def make_hparam_string(learning_rate, use_two_fc, use_two_conv):
conv_param = "conv2" if use_two_conv else "conv1"
fc_param = "fc2" if use_two_fc else "fc1"
return "lr_%.0E%s%s" % (learning_rate, conv_param, fc_param)
def main():
# You can try adding some more learning rates
#for learning_rate in [1E-3, 1E-4, 1E-5]:
for learning_rate in [1E-4]:
# Include "False" as a value to try different model architectures
#for use_two_fc in [True, False]:
for use_two_fc in [True]:
#for use_two_conv in [True, False]:
for use_two_conv in [True]:
# Construct a hyperparameter string for each one (example: "lr_1E-3fc2conv2")
hparam = make_hparam_string(learning_rate, use_two_fc, use_two_conv)
print('Starting run for %s' % hparam)
sys.stdout.flush() # this forces print-ed lines to show up.
# Actually run with the new settings
mnist_model(learning_rate, use_two_fc, use_two_conv, hparam)
if __name__ == '__main__':
main()
更新代码：
import os
import tensorflow as tf
import sys
import urllib
if sys.version_info[0] >= 3:
from urllib.request import urlretrieve
else:
from urllib import urlretrieve
LOGDIR = 'log3/'
GITHUB_URL ='https://raw.githubusercontent.com/mamcgrath/TensorBoard-TF-Dev-Summit-Tutorial/master/'
### MNIST EMBEDDINGS ###
mnist = tf.contrib.learn.datasets.mnist.read_data_sets(train_dir=LOGDIR + 'data', one_hot=True)
### Get a sprite and labels file for the embedding projector ###
urlretrieve(GITHUB_URL + 'labels_1024.tsv', LOGDIR + 'labels_1024.tsv')
urlretrieve(GITHUB_URL + 'sprite_1024.png', LOGDIR + 'sprite_1024.png')
# Add convolution layer
def conv_layer(input, size_in, size_out, name="conv"):
with tf.name_scope(name):
#w = tf.Variable(tf.zeros([5, 5, size_in, size_out]), name="W")
#b = tf.Variable(tf.zeros([size_out]), name="B")
w = tf.Variable(tf.truncated_normal([4, 4, size_in, size_out], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding="SAME")
act = tf.nn.relu(conv + b)
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
return tf.nn.max_pool(act, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
# Add fully connected layer
def fc_layer(input, size_in, size_out, name="fc"):
with tf.name_scope(name):
w = tf.Variable(tf.truncated_normal([size_in, size_out], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
act = tf.nn.relu(tf.matmul(input, w) + b)
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
return act
def mnist_model(learning_rate, use_two_conv, use_two_fc, hparam):
tf.set_random_seed(1)
tf.reset_default_graph()
sess = tf.Session()
# Setup placeholders, and reshape the data
x = tf.placeholder(tf.float32, shape=[None, 784], name="x")
x_image = tf.reshape(x, [-1, 28, 28, 1])
tf.summary.image('input', x_image, 3)
y = tf.placeholder(tf.float32, shape=[None, 10], name="labels")
if use_two_conv:
conv1 = conv_layer(x_image, 1, 32, "conv1")
conv_out = conv_layer(conv1, 32, 64, "conv2")
else:
conv1 = conv_layer(x_image, 1, 64, "conv")
conv_out = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
flattened = tf.reshape(conv_out, [-1, 7 * 7 * 64])
if use_two_fc:
fc1 = fc_layer(flattened, 7 * 7 * 64, 40, "fc1")
embedding_input = fc1
embedding_size = 40
logits = fc_layer(fc1, 40, 10, "fc2")
else:
embedding_input = flattened
embedding_size = 40
logits = fc_layer(flattened, 40, 10, "fc")
with tf.name_scope("xent"):
xent = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=y), name="xent")
tf.summary.scalar("xent", xent)
with tf.name_scope("train"):
train_step = tf.train.AdamOptimizer(learning_rate).minimize(xent)
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar("accuracy", accuracy)
summ = tf.summary.merge_all()
embedding = tf.Variable(tf.zeros([100, embedding_size]), name="test_embedding")
assignment = embedding.assign(embedding_input)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter(LOGDIR + hparam)
writer.add_graph(sess.graph)
config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
embedding_config = config.embeddings.add()
embedding_config.tensor_name = embedding.name
embedding_config.sprite.image_path = LOGDIR + 'sprite_1024.png'
embedding_config.metadata_path = LOGDIR + 'labels_1024.tsv'
# Specify the width and height of a single thumbnail.
embedding_config.sprite.single_image_dim.extend([28, 28])
tf.contrib.tensorboard.plugins.projector.visualize_embeddings(writer, config)
for i in range(201):
batch = mnist.train.next_batch(150)
if i % 5 == 0:
[train_accuracy, s] = sess.run([accuracy, summ], feed_dict={x: batch[0], y: batch[1]})
writer.add_summary(s, i)
print (train_accuracy)
#if i % 500 == 0:
#sess.run(assignment, feed_dict={x: mnist.test.images[:1024], y: mnist.test.labels[:1024]})
#saver.save(sess, os.path.join(LOGDIR, "model.ckpt"), i)
sess.run(train_step, feed_dict={x: batch[0], y: batch[1]})
def make_hparam_string(learning_rate, use_two_fc, use_two_conv):
conv_param = "conv2" if use_two_conv else "conv1"
fc_param = "fc2" if use_two_fc else "fc1"
return "lr_%.0E%s%s" % (learning_rate, conv_param, fc_param)
def main():
# You can try adding some more learning rates
#for learning_rate in [1E-3, 1E-4, 1E-5]:
for learning_rate in [0.002]:
# Include "False" as a value to try different model architectures
#for use_two_fc in [True, False]:
for use_two_fc in [True]:
#for use_two_conv in [True, False]:
for use_two_conv in [True]:
# Construct a hyperparameter string for each one (example: "lr_1E-3fc2conv2")
hparam = make_hparam_string(learning_rate, use_two_fc, use_two_conv)
print('Starting run for %s' % hparam)
sys.stdout.flush() # this forces print-ed lines to show up.
# Actually run with the new settings
mnist_model(learning_rate, use_two_fc, use_two_conv, hparam)
if __name__ == '__main__':
main()
更新结果：
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
0.106667
0.213333
0.326667
0.426667
0.466667
0.473333
0.48
0.493333
0.506667
0.506667
0.473333
0.426667
0.46
0.46
0.506667
0.433333
0.526667
0.453333
0.506667
0.513333
0.42
0.426667
0.493333
0.486667
0.48
0.493333
0.5
0.493333
0.533333
0.433333
0.5
0.426667
0.42
0.493333
0.466667
0.466667
0.433333
0.46
0.52
0.513333
0.453333
您的隐藏层太大，因此很难训练网络。我将它的大小从 1024 减少到 40，并修改了 if use_two_fc 语句中的块，如下所示：
if use_two_fc:
fc1 = fc_layer(flattened, 7 * 7 * 64, 40, "fc1")
embedding_input = fc1
embedding_size = 40
logits = fc_layer(fc1, 40, 10, "fc2")
我还把学习率改成了0.002。我将批量大小增加到 150，所以
batch = mnist.train.next_batch(150)
我还将迭代次数增加到 301。最后，为了使结果更加一致，我在行
之后添加了 tf.set_random_seed(1)
tf.reset_default_graph()。它将准确度提高到 99%，如您在打印的准确度列表中所见：
Extracting log3/data\train-images-idx3-ubyte.gz
Extracting log3/data\train-labels-idx1-ubyte.gz
Extracting log3/data\t10k-images-idx3-ubyte.gz
Extracting log3/data\t10k-labels-idx1-ubyte.gz
Starting run for lr_2E-03conv2fc2
0.133333
0.3
0.706667
0.693333
0.686667
0.726667
0.806667
0.84
0.893333
0.826667
0.846667
0.893333
0.893333
0.9
0.92
0.946667
0.893333
0.893333
0.966667
0.946667
0.946667
0.906667
0.946667
0.96
0.946667
0.966667
0.906667
0.946667
0.96
0.946667
0.92
0.96
0.953333
0.953333
0.933333
0.9
0.953333
0.966667
0.966667
0.986667
0.966667
0.986667
0.986667
0.933333
0.973333
0.973333
0.98
0.96
0.973333
0.973333
0.973333
0.986667
0.946667
0.966667
0.926667
0.94
0.96
0.973333
0.986667
0.993333
0.993333
如标题所述，我的 CNN 在 mnist 数据集上的准确性非常低 (~70%)。我的架构包括两个卷积层和两个完全连接的层。我很高兴我得到了它 运行 但现在我对如何优化我的代码感到困惑。任何帮助将不胜感激。
import os
import tensorflow as tf
import sys
import urllib
if sys.version_info[0] >= 3:
from urllib.request import urlretrieve
else:
from urllib import urlretrieve
LOGDIR = 'log3/'
GITHUB_URL ='https://raw.githubusercontent.com/mamcgrath/TensorBoard-TF-Dev-Summit-Tutorial/master/'
### MNIST EMBEDDINGS ###
mnist = tf.contrib.learn.datasets.mnist.read_data_sets(train_dir=LOGDIR + 'data', one_hot=True)
### Get a sprite and labels file for the embedding projector ###
urlretrieve(GITHUB_URL + 'labels_1024.tsv', LOGDIR + 'labels_1024.tsv')
urlretrieve(GITHUB_URL + 'sprite_1024.png', LOGDIR + 'sprite_1024.png')
# Add convolution layer
def conv_layer(input, size_in, size_out, name="conv"):
with tf.name_scope(name):
#w = tf.Variable(tf.zeros([5, 5, size_in, size_out]), name="W")
#b = tf.Variable(tf.zeros([size_out]), name="B")
w = tf.Variable(tf.truncated_normal([4, 4, size_in, size_out], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding="SAME")
act = tf.nn.relu(conv + b)
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
return tf.nn.max_pool(act, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
# Add fully connected layer
def fc_layer(input, size_in, size_out, name="fc"):
with tf.name_scope(name):
w = tf.Variable(tf.truncated_normal([size_in, size_out], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
act = tf.nn.relu(tf.matmul(input, w) + b)
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
return act
def mnist_model(learning_rate, use_two_conv, use_two_fc, hparam):
tf.reset_default_graph()
sess = tf.Session()
# Setup placeholders, and reshape the data
x = tf.placeholder(tf.float32, shape=[None, 784], name="x")
x_image = tf.reshape(x, [-1, 28, 28, 1])
tf.summary.image('input', x_image, 3)
y = tf.placeholder(tf.float32, shape=[None, 10], name="labels")
if use_two_conv:
conv1 = conv_layer(x_image, 1, 32, "conv1")
conv_out = conv_layer(conv1, 32, 64, "conv2")
else:
conv1 = conv_layer(x_image, 1, 64, "conv")
conv_out = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
flattened = tf.reshape(conv_out, [-1, 7 * 7 * 64])
if use_two_fc:
fc1 = fc_layer(flattened, 7 * 7 * 64, 1024, "fc1")
embedding_input = fc1
embedding_size = 1024
logits = fc_layer(fc1, 1024, 10, "fc2")
else:
embedding_input = flattened
embedding_size = 7*7*64
logits = fc_layer(flattened, 7*7*64, 10, "fc")
with tf.name_scope("xent"):
xent = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=y), name="xent")
tf.summary.scalar("xent", xent)
with tf.name_scope("train"):
train_step = tf.train.AdamOptimizer(learning_rate).minimize(xent)
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar("accuracy", accuracy)
summ = tf.summary.merge_all()
embedding = tf.Variable(tf.zeros([1024, embedding_size]), name="test_embedding")
assignment = embedding.assign(embedding_input)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter(LOGDIR + hparam)
writer.add_graph(sess.graph)
config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
embedding_config = config.embeddings.add()
embedding_config.tensor_name = embedding.name
embedding_config.sprite.image_path = LOGDIR + 'sprite_1024.png'
embedding_config.metadata_path = LOGDIR + 'labels_1024.tsv'
# Specify the width and height of a single thumbnail.
embedding_config.sprite.single_image_dim.extend([28, 28])
tf.contrib.tensorboard.plugins.projector.visualize_embeddings(writer, config)
for i in range(201):
batch = mnist.train.next_batch(100)
if i % 5 == 0:
[train_accuracy, s] = sess.run([accuracy, summ], feed_dict={x: batch[0], y: batch[1]})
writer.add_summary(s, i)
print (train_accuracy)
if i % 500 == 0:
sess.run(assignment, feed_dict={x: mnist.test.images[:1024], y: mnist.test.labels[:1024]})
saver.save(sess, os.path.join(LOGDIR, "model.ckpt"), i)
sess.run(train_step, feed_dict={x: batch[0], y: batch[1]})
def make_hparam_string(learning_rate, use_two_fc, use_two_conv):
conv_param = "conv2" if use_two_conv else "conv1"
fc_param = "fc2" if use_two_fc else "fc1"
return "lr_%.0E%s%s" % (learning_rate, conv_param, fc_param)
def main():
# You can try adding some more learning rates
#for learning_rate in [1E-3, 1E-4, 1E-5]:
for learning_rate in [1E-4]:
# Include "False" as a value to try different model architectures
#for use_two_fc in [True, False]:
for use_two_fc in [True]:
#for use_two_conv in [True, False]:
for use_two_conv in [True]:
# Construct a hyperparameter string for each one (example: "lr_1E-3fc2conv2")
hparam = make_hparam_string(learning_rate, use_two_fc, use_two_conv)
print('Starting run for %s' % hparam)
sys.stdout.flush() # this forces print-ed lines to show up.
# Actually run with the new settings
mnist_model(learning_rate, use_two_fc, use_two_conv, hparam)
if __name__ == '__main__':
main()
更新代码：
import os
import tensorflow as tf
import sys
import urllib
if sys.version_info[0] >= 3:
from urllib.request import urlretrieve
else:
from urllib import urlretrieve
LOGDIR = 'log3/'
GITHUB_URL ='https://raw.githubusercontent.com/mamcgrath/TensorBoard-TF-Dev-Summit-Tutorial/master/'
### MNIST EMBEDDINGS ###
mnist = tf.contrib.learn.datasets.mnist.read_data_sets(train_dir=LOGDIR + 'data', one_hot=True)
### Get a sprite and labels file for the embedding projector ###
urlretrieve(GITHUB_URL + 'labels_1024.tsv', LOGDIR + 'labels_1024.tsv')
urlretrieve(GITHUB_URL + 'sprite_1024.png', LOGDIR + 'sprite_1024.png')
# Add convolution layer
def conv_layer(input, size_in, size_out, name="conv"):
with tf.name_scope(name):
#w = tf.Variable(tf.zeros([5, 5, size_in, size_out]), name="W")
#b = tf.Variable(tf.zeros([size_out]), name="B")
w = tf.Variable(tf.truncated_normal([4, 4, size_in, size_out], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding="SAME")
act = tf.nn.relu(conv + b)
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
return tf.nn.max_pool(act, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
# Add fully connected layer
def fc_layer(input, size_in, size_out, name="fc"):
with tf.name_scope(name):
w = tf.Variable(tf.truncated_normal([size_in, size_out], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
act = tf.nn.relu(tf.matmul(input, w) + b)
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
return act
def mnist_model(learning_rate, use_two_conv, use_two_fc, hparam):
tf.set_random_seed(1)
tf.reset_default_graph()
sess = tf.Session()
# Setup placeholders, and reshape the data
x = tf.placeholder(tf.float32, shape=[None, 784], name="x")
x_image = tf.reshape(x, [-1, 28, 28, 1])
tf.summary.image('input', x_image, 3)
y = tf.placeholder(tf.float32, shape=[None, 10], name="labels")
if use_two_conv:
conv1 = conv_layer(x_image, 1, 32, "conv1")
conv_out = conv_layer(conv1, 32, 64, "conv2")
else:
conv1 = conv_layer(x_image, 1, 64, "conv")
conv_out = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
flattened = tf.reshape(conv_out, [-1, 7 * 7 * 64])
if use_two_fc:
fc1 = fc_layer(flattened, 7 * 7 * 64, 40, "fc1")
embedding_input = fc1
embedding_size = 40
logits = fc_layer(fc1, 40, 10, "fc2")
else:
embedding_input = flattened
embedding_size = 40
logits = fc_layer(flattened, 40, 10, "fc")
with tf.name_scope("xent"):
xent = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=y), name="xent")
tf.summary.scalar("xent", xent)
with tf.name_scope("train"):
train_step = tf.train.AdamOptimizer(learning_rate).minimize(xent)
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar("accuracy", accuracy)
summ = tf.summary.merge_all()
embedding = tf.Variable(tf.zeros([100, embedding_size]), name="test_embedding")
assignment = embedding.assign(embedding_input)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter(LOGDIR + hparam)
writer.add_graph(sess.graph)
config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
embedding_config = config.embeddings.add()
embedding_config.tensor_name = embedding.name
embedding_config.sprite.image_path = LOGDIR + 'sprite_1024.png'
embedding_config.metadata_path = LOGDIR + 'labels_1024.tsv'
# Specify the width and height of a single thumbnail.
embedding_config.sprite.single_image_dim.extend([28, 28])
tf.contrib.tensorboard.plugins.projector.visualize_embeddings(writer, config)
for i in range(201):
batch = mnist.train.next_batch(150)
if i % 5 == 0:
[train_accuracy, s] = sess.run([accuracy, summ], feed_dict={x: batch[0], y: batch[1]})
writer.add_summary(s, i)
print (train_accuracy)
#if i % 500 == 0:
#sess.run(assignment, feed_dict={x: mnist.test.images[:1024], y: mnist.test.labels[:1024]})
#saver.save(sess, os.path.join(LOGDIR, "model.ckpt"), i)
sess.run(train_step, feed_dict={x: batch[0], y: batch[1]})
def make_hparam_string(learning_rate, use_two_fc, use_two_conv):
conv_param = "conv2" if use_two_conv else "conv1"
fc_param = "fc2" if use_two_fc else "fc1"
return "lr_%.0E%s%s" % (learning_rate, conv_param, fc_param)
def main():
# You can try adding some more learning rates
#for learning_rate in [1E-3, 1E-4, 1E-5]:
for learning_rate in [0.002]:
# Include "False" as a value to try different model architectures
#for use_two_fc in [True, False]:
for use_two_fc in [True]:
#for use_two_conv in [True, False]:
for use_two_conv in [True]:
# Construct a hyperparameter string for each one (example: "lr_1E-3fc2conv2")
hparam = make_hparam_string(learning_rate, use_two_fc, use_two_conv)
print('Starting run for %s' % hparam)
sys.stdout.flush() # this forces print-ed lines to show up.
# Actually run with the new settings
mnist_model(learning_rate, use_two_fc, use_two_conv, hparam)
if __name__ == '__main__':
main()
更新结果：
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
0.106667
0.213333
0.326667
0.426667
0.466667
0.473333
0.48
0.493333
0.506667
0.506667
0.473333
0.426667
0.46
0.46
0.506667
0.433333
0.526667
0.453333
0.506667
0.513333
0.42
0.426667
0.493333
0.486667
0.48
0.493333
0.5
0.493333
0.533333
0.433333
0.5
0.426667
0.42
0.493333
0.466667
0.466667
0.433333
0.46
0.52
0.513333
0.453333
您的隐藏层太大，因此很难训练网络。我将它的大小从 1024 减少到 40，并修改了 if use_two_fc 语句中的块，如下所示：
if use_two_fc:
fc1 = fc_layer(flattened, 7 * 7 * 64, 40, "fc1")
embedding_input = fc1
embedding_size = 40
logits = fc_layer(fc1, 40, 10, "fc2")
我还把学习率改成了0.002。我将批量大小增加到 150，所以
batch = mnist.train.next_batch(150)
我还将迭代次数增加到 301。最后，为了使结果更加一致，我在行
之后添加了tf.set_random_seed(1)
tf.reset_default_graph()。它将准确度提高到 99%，如您在打印的准确度列表中所见：
Extracting log3/data\train-images-idx3-ubyte.gz
Extracting log3/data\train-labels-idx1-ubyte.gz
Extracting log3/data\t10k-images-idx3-ubyte.gz
Extracting log3/data\t10k-labels-idx1-ubyte.gz
Starting run for lr_2E-03conv2fc2
0.133333
0.3
0.706667
0.693333
0.686667
0.726667
0.806667
0.84
0.893333
0.826667
0.846667
0.893333
0.893333
0.9
0.92
0.946667
0.893333
0.893333
0.966667
0.946667
0.946667
0.906667
0.946667
0.96
0.946667
0.966667
0.906667
0.946667
0.96
0.946667
0.92
0.96
0.953333
0.953333
0.933333
0.9
0.953333
0.966667
0.966667
0.986667
0.966667
0.986667
0.986667
0.933333
0.973333
0.973333
0.98
0.96
0.973333
0.973333
0.973333
0.986667
0.946667
0.966667
0.926667
0.94
0.96
0.973333
0.986667
0.993333
0.993333