概述
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('data',one_hot = True) def add_layer(input_data,in_size,out_size,activation_function=None): Weights = tf.Variable(tf.random_normal([in_size,out_size])) Biases = tf.Variable(tf.zeros([1,out_size])+0.1) Wx_plus_b = tf.add(tf.matmul(input_data,Weights),Biases) if activation_function==None: outputs = Wx_plus_b else: outputs = activation_function(Wx_plus_b) #return outputs#,Weights return {'outdata':outputs,'w':Weights} def get_accuracy(t_y): # global l1 # accu = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(l1['outdata'],1),tf.argmax(t_y,1)),dtype = tf.float32)) global prediction accu = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(prediction['outdata'],dtype = tf.float32)) return accu X = tf.placeholder(tf.float32,[None,784]) Y = tf.placeholder(tf.float32,10]) #l1 = add_layer(X,784,10,tf.nn.softmax) #cross_entropy = tf.reduce_mean(-tf.reduce_sum(Y*tf.log(l1['outdata']),reduction_indices= [1])) #l1 = add_layer(X,1024,tf.nn.relu) l1 = add_layer(X,None) prediction = add_layer(l1['outdata'],tf.nn.softmax) cross_entropy = tf.reduce_mean(-tf.reduce_sum(Y*tf.log(prediction['outdata']),reduction_indices= [1])) optimizer = tf.train.GradientDescentOptimizer(0.000001) train = optimizer.minimize(cross_entropy) newW = tf.Variable(tf.random_normal([1024,10])) newOut = tf.matmul(l1['outdata'],newW) newsoftmax = tf.nn.softmax(newOut) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) #print(sess.run(l1_Weights)) for i in range(2): X_train,y_train = mnist.train.next_batch(1) X_train = X_train/255 #需要进行归一化处理 #print(sess.run(l1['w'],Feed_dict={X:X_train})) #print(sess.run(prediction['w'],Feed_dict={X:X_train,Y:y_train})) #print(sess.run(l1['outdata'],Y:y_train}).shape) print(sess.run(prediction['outdata'],Y:y_train})) print(sess.run(newOut,Feed_dict={X:X_train})) print(sess.run(newsoftmax,Feed_dict={X:X_train})) print(y_train) #print(sess.run(l1['outdata'],Feed_dict={X:X_train})) sess.run(train,Y:y_train}) if i%100 == 0: #print(sess.run(cross_entropy,Y:y_train})) accuracy = get_accuracy(mnist.test.labels) print(sess.run(accuracy,Feed_dict={X:mnist.test.images})) #if i%100==0: #print(sess.run(prediction,Feed_dict={X:X_train})) #print(sess.run(cross_entropy,Y:y_train}))
总结
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