def main(_):
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
#输入变量，mnist图片大小为28*28
x = tf.placeholder(tf.float32, [None, 784])
#输出变量，数字是1-10
y_ = tf.placeholder(tf.float32, [None, 10])
# 构建网络，输入—>第一层卷积—>第一层池化—>第二层卷积—>第二层池化—>第一层全连接—>第二层全连接
y_conv, keep_prob = mnist_model.deepnn(x)
#第一步对网络最后一层的输出做一个softmax，第二步将softmax输出和实际样本做一个交叉熵
#cross_entropy返回的是向量
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
logits=y_conv)
#求cross_entropy向量的平均值得到交叉熵
cross_entropy = tf.reduce_mean(cross_entropy)
#AdamOptimizer是Adam优化算法：一个寻找全局最优点的优化算法，引入二次方梯度校验
with tf.name_scope('adam_optimizer'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
#在测试集上的精确度
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
#将神经网络图模型保存本地，可以通过浏览器查看可视化网络结构
graph_location = tempfile.mkdtemp()
print('Saving graph to: %s' % graph_location)
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())
#将训练的网络保存下来
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(5000):
batch = mnist.train.next_batch(50)
if i % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 1.0
}) #输入是字典，表示tensorflow被feed的值
print('step %d, training accuracy %g' % (i, train_accuracy))
train_step.run(feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 0.5
})
test_accuracy = 0
for i in range(200):
batch = mnist.test.next_batch(50)
test_accuracy += accuracy.eval(feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 1.0
}) / 200
print('test accuracy %g' % test_accuracy)
save_path = saver.save(sess, "mnist_cnn_model.ckpt")
def main(_):
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
y_conv, keep_prob = mnist_model.deepnn(x)
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
with tf.name_scope('adam_optimizer'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_, 1), tf.argmax(y_conv, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
graph_location = tempfile.mkdtemp()
print('saving graph to: %s' % graph_location)
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(5001):
batch = mnist.train.next_batch(50)
if i % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 1.0
})
print("step %d, training accuracy %g" % (i, train_accuracy))
train_step.run(feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 0.5
})
test_accuracy = 0
for i in range(200):
batch = mnist.test.next_batch(50)
test_accuracy += accuracy.eval(feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 1.0
}) / 200
print('test accuracy: %g' % test_accuracy)
save_path = saver.save(sess, "mnist_cnn_model.ckpt")
print(save_path)
def main(argv):
imvalue = load_data(argv)
x = tf.placeholder(tf.float32, [None, 784])
y_conv, keep_prob = mnist_model.deepnn(x)
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
saver.restore(sess, "mnist_cnn_model.ckpt")
prediction = tf.argmax(y_conv, 1)
predint = prediction.eval(feed_dict={x: [imvalue], keep_prob: 1.0})
print(predint[0])
def main(argv):
imvalue = load_data(argv)
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
y_conv, keep_prob = mnist_model.deepnn(x)
y_predict = tf.nn.softmax(y_conv)
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1)
with tf.Session(config=tf.ConfigProto(log_device_placement=False,
gpu_options=gpu_options)) as sess:
#with tf.device("/gpu:0"):
sess.run(init_op)
saver.restore(sess, "mnist_cnn_model.ckpt")
prediction = tf.argmax(y_predict, 1)
predint = prediction.eval(feed_dict={
x: [imvalue],
keep_prob: 1.0
},
session=sess)
print(predint[0])
