-- 环境：win10, jupyter notebook/pycharm, python3.x, tensorflow1.3.0-gpu
上章我们学习了tensorflow怎样运用算法太实现MNIST手写字的照片识别，这一节来建立一个简单模型，使得识别精度更高。
Import Numpy ,Tensorflow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
Retrieve the training and test data
x_train, y_train, x_test, y_test = mnist.load_data(one_hot = True)
数据加载
首先讲讲这句话的作用，matplotlib是最著名的Python图表绘制扩展库，它支持输出多种格式的图形图像，并且可以使用多种GUI界面库交互式地显示图表。使用%matplotlib命令可以将matplotlib的图表直接嵌入到Notebook之中，或者使用指定的界面库显示图表，它有一个参数指定matplotlib图表的显示方式。inline表示将图表嵌入到Notebook中。
其次，这个问题是说没有合理的GUI请求，所以要安装以下任何一种都可以，ipython-qtconsole就行。
IPython提供了许多魔法命令，使得在IPython环境中的操作更加得心应手。魔法命令都以%或者%%开头，以%开头的成为行命令，%%开头的称为单元命令。行命令只对命令所在的行有效，而单元命令则必须出现在单元的第一行，对整个单元的代码进行处理。
执行%magic可以查看关于各个命令的说明，而在命令之后添加?可以查看该命令的详细说明。
#Visualiziing the data
import matplotlib.pyplot as plt
%matplotlib inline
#Function for displaying a training image by it's index in the MNIST set
def show_digit(index):
label = y_train[index].argmax(axis=0)
#Reshape 784 array into 28x28 image
image = x_train[index].reshape([28,28])
plt.title('Training data, indx:%d, Label:%d'%(index, label))
plt.imshow(image, cmap='gray_r')
plt.show()
#Display the first (index 0) training image
show_digit(0)
带标签数据显示
Define the meural network
def build_model():
#This resets all parameters and variables
tf.reset_default_graph()
#input layer ,Hidder layer(s),and set how you want to train the model
#input
net = tflearn.input_data([None, 784])
#hidder layer
net = tflearn.fully_connected(net,128, activation = 'ReLU')
#net = tflearn.fully_connected(net, 64, activation = 'ReLU')
#net = tflearn.fully_connected(net, 32, activation = 'ReLU')
net = tflearn.fully_connected(net, 20, activation = 'ReLU')
#Output layer
net = tflearn.fully_connected(net, 10, activation = 'softmax')
net = tflearn.regression(net, optimizer='sgd',learning_rate = 0.1,\
loss = 'categorical_crossentropy')
#This model assumes that your network is named 'net'
model = tflearn.DNN(net)
return model
此神经网络模型包含一个输入层，两个全连接层激活函数为ReLu，一个输出层激活函数为softmax
Build the model
model = build_model()
training network
此处参数为调试后的最佳参数，可以修改了观察得到结果的不同，epoch不能太小，也不能太大，对自己网络训练效果好就得适当
model.fit(x_train,y_train, validation_set = 0.1, show_metric = True,batch_size =10,n_epoch = 11）
训练网络
训练结束
Testting
# Compare the labels that our model predicts with the actual labels
# Find the indices of the most confident prediction for each item. That tells us the predicted digit for that sample.
predictions = np.array(model.predict(testX)).argmax(axis=1)
# Calculate the accuracy, which is the percentage of times the predicated labels matched the actual labels
actual = testY.argmax(axis=1)
test_accuracy = np.mean(predictions == actual, axis=0)
# Print out the result
print("Test accuracy: ", test_accuracy)
97%
