手写数字识别——01:tada:

Mnist_01/mnist.py

@@ -0,0 +1,56 @@
+import tensorflow as tf
+import numpy as np
+import scipy.io as spio
+
+'''运行函数'''
+def NeuralNetwork():
+    data_digits = spio.loadmat('data_digits.mat')
+    X = data_digits['X']
+    y = data_digits['y']
+    m,n = X.shape
+    class_y = np.zeros((m,10))      # y是0,1,2,3...9,需要映射0/1形式
+    for i in range(10):
+        class_y[:,i] = np.float32(y==i).reshape(1,-1) 
+
+    xs = tf.placeholder(tf.float32, shape=[None,400])  # 像素是20x20=400，所以有400个feature
+    ys = tf.placeholder(tf.float32, shape=[None,10])   # 输出有10个
+
+    prediction = add_layer(xs, 400, 10, activation_function=tf.nn.softmax) # 两层神经网络，400x10
+    #prediction = add_layer(layer1, 25, 10, activation_function=tf.nn.softmax)
+
+    #loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
+    loss = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))  # 定义损失函数（代价函数），
+    train = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(loss)     # 使用梯度下降最小化损失
+    init = tf.initialize_all_variables()   # 初始化所有变量
+
+    sess = tf.Session()  # 创建Session
+    sess.run(init)
+
+    for i in range(4000): # 迭代训练4000次
+        sess.run(train, feed_dict={xs:X,ys:class_y})  # 训练train，填入数据
+        if i%50==0:  # 每50次输出当前的准确度
+            print(compute_accuracy(xs,ys,X,class_y,sess,prediction))
+
+'''添加一层神经网络'''
+def add_layer(inputs,in_size,out_size,activation_function=None):
+    Weights = tf.Variable(tf.random_normal([in_size,out_size]))    # 权重，in*out 
+    biases = tf.Variable(tf.zeros([1,out_size]) + 0.1)  
+    Ws_plus_b = tf.matmul(inputs,Weights) + biases   # 计算权重和偏置之后的值
+
+    if activation_function is None:                                                                                               
+        outputs = Ws_plus_b                                                                                                                
+    else:                                                                                                                              
+        outputs = activation_function(Ws_plus_b)    # 调用激励函数运算                                                                                                                 
+    return outputs    
+
+
+'''计算预测准确度'''  
+def compute_accuracy(xs,ys,X,y,sess,prediction):
+    y_pre = sess.run(prediction,feed_dict={xs:X}) 
+    correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(y,1))  #tf.argmax 给出某个tensor对象在某一维上的其数据最大值所在的索引值,即为对应的数字，tf.equal 来检测我们的预测是否真实标签匹配
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) # 平均值即为准确度
+    result = sess.run(accuracy,feed_dict={xs:X,ys:y})
+    return result    
+
+if __name__ == '__main__':
+    NeuralNetwork()

readme.md

@@ -486,6 +486,14 @@ writer = tf.train.SummaryWriter("logs/", sess.graph)
  - 添加：`tf.scalar_summary('loss',loss)`    
  ![enter description here][8]
 
+## 五、手写数字识别_1
+### 1、说明
+- [全部代码](https://github.com/lawlite19/MachineLearning_TensorFlow\Mnist_01\minist.py)：`https://github.com/lawlite19/MachineLearning_TensorFlow\Mnist_01\minist.py`
+- 自己的数据集，没有使用tensorflow中mnist数据集，
+- 之前在机器学习中用Python实现过，地址：`https://github.com/lawlite19/MachineLearning_Python`,这里使用`tensorflow`实现
+
+### 2、代码实现
+-