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代码实现
import tensorflow as tf
import os
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3_base
# 1、利用数据，在训练的时候实时提供数据
# mnist手写数字数据在运行时候实时提供给给占位符
tf.app.flags.DEFINE_integer("is_train", 1, "指定是否是训练模型，还是拿数据去预测")
FLAGS = tf.app.flags.FLAGS
def create_weights(shape):
return tf.Variable(initial_value=tf.random_normal(shape=shape, stddev=0.01))
def create_model(x):
"""
构建卷积神经网络
:param x:
:return:
"""
# 1）第一个卷积大层
with tf.variable_scope("conv1"):
# 卷积层
# 将x[None, 784]形状进行修改
input_x = tf.reshape(x, shape=[-1, 28, 28, 1])
# 定义filter和偏置
conv1_weights = create_weights(shape=[5, 5, 1, 32])
conv1_bias = create_weights(shape=[32])
conv1_x = tf.nn.conv2d(input=input_x, filter=conv1_weights, strides=[1, 1, 1, 1], padding="SAME") + conv1_bias
# 激活层
relu1_x = tf.nn.relu(conv1_x)
# 池化层
pool1_x = tf.nn.max_pool(value=relu1_x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
# 2）第二个卷积大层
with tf.variable_scope("conv2"):
# 卷积层
# 定义filter和偏置
conv2_weights = create_weights(shape=[5, 5, 32, 64])
conv2_bias = create_weights(shape=[64])
conv2_x = tf.nn.conv2d(input=pool1_x, filter=conv2_weights, strides=[1, 1, 1, 1], padding="SAME") + conv2_bias
# 激活层
relu2_x = tf.nn.relu(conv2_x)
# 池化层
pool2_x = tf.nn.max_pool(value=relu2_x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
# 3）全连接层
with tf.variable_scope("full_connection"):
# [None, 7, 7, 64]->[None, 7 * 7 * 64]
# [None, 7 * 7 * 64] * [7 * 7 * 64, 10] = [None, 10]
x_fc = tf.reshape(pool2_x, shape=[-1, 7 * 7 * 64])
weights_fc = create_weights(shape=[7 * 7 * 64, 10])
bias_fc = create_weights(shape=[10])
y_predict = tf.matmul(x_fc, weights_fc) + bias_fc
return y_predict
def full_connected_mnist():
"""
单层全连接神经网络识别手写数字图片
特征值：[None, 784]
目标值：one_hot编码 [None, 10]
:return:
"""
mnist = input_data.read_data_sets("./mnist_data/", one_hot=True)
# 1、准备数据
# x [None, 784] y_true [None. 10]
with tf.variable_scope("mnist_data"):
x = tf.placeholder(tf.float32, [None, 784])
y_true = tf.placeholder(tf.int32, [None, 10])
y_predict = create_model(x)
# 3、softmax回归以及交叉熵损失计算
with tf.variable_scope("softmax_crossentropy"):
# labels:真实值 [None, 10] one_hot
# logits:全脸层的输出[None,10]
# 返回每个样本的损失组成的列表
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y_predict))
# 4、梯度下降损失优化
with tf.variable_scope("optimizer"):
# 学习率
train_op = tf.train.AdamOptimizer(0.001).minimize(loss)
# 5、得出每次训练的准确率（通过真实值和预测值进行位置比较，每个样本都比较）
with tf.variable_scope("accuracy"):
equal_list = tf.equal(tf.argmax(y_true, 1), tf.argmax(y_predict, 1))
accuracy = tf.reduce_mean(tf.cast(equal_list, tf.float32))
# （2）收集要显示的变量
# 先收集损失和准确率
tf.summary.scalar("losses", loss)
tf.summary.scalar("acc", accuracy)
# 初始化变量op
init_op = tf.global_variables_initializer()
# （3）合并所有变量op
merged = tf.summary.merge_all()
# 创建模型保存和加载
saver = tf.train.Saver()
# 开启会话去训练
with tf.Session() as sess:
# 初始化变量
sess.run(init_op)
# （1）创建一个events文件实例
file_writer = tf.summary.FileWriter("./tmp/summary/", graph=sess.graph)
# 加载模型
# if os.path.exists("./tmp/modelckpt/checkpoint"):
# saver.restore(sess, "./tmp/modelckpt/fc_nn_model")
if FLAGS.is_train == 1:
# 循环步数去训练
for i in range(3000):
# 获取数据，实时提供
# 每步提供50个样本训练
mnist_x, mnist_y = mnist.train.next_batch(50)
# 运行训练op
sess.run(train_op, feed_dict={x: mnist_x, y_true: mnist_y})
print("训练第%d步的准确率为：%f, 损失为：%f " % (i+1,
sess.run(accuracy, feed_dict={x: mnist_x, y_true: mnist_y}),
sess.run(loss, feed_dict={x: mnist_x, y_true: mnist_y})
)
)
# 运行合变量op，写入事件文件当中
summary = sess.run(merged, feed_dict={x: mnist_x, y_true: mnist_y})
file_writer.add_summary(summary, i)
# if i % 100 == 0:
# saver.save(sess, "./tmp/modelckpt/fc_nn_model")
else:
# 如果不是训练，我们就去进行预测测试集数据
for i in range(100):
# 每次拿一个样本预测
mnist_x, mnist_y = mnist.test.next_batch(1)
print("第%d个样本的真实值为：%d, 模型预测结果为：%d" % (
i+1,
tf.argmax(sess.run(y_true, feed_dict={x: mnist_x, y_true: mnist_y}), 1).eval(),
tf.argmax(sess.run(y_predict, feed_dict={x: mnist_x, y_true: mnist_y}), 1).eval()
)
)
return None
if __name__ == "__main__":
full_connected_mnist()
