学习进度笔记08
TensorFlow逻辑回归
- import tensorflow as tf
- from tensorflow.examples.tutorials.mnist import input_data
- mnist=input_data.read_data_sets("/home/yxcx/tf_data",one_hot=True)
- import os
- os.environ["CUDA_VISIBLE_DEVICES"]="0"
- #Parameters
- learning_rate=0.01
- training_epochs=25
- batch_size=100
- display_step=1
- #tf Graph Input
- x=tf.placeholder(tf.float32,[None,784])
- y=tf.placeholder(tf.float32,[None,10])
- #Set model weights
- W=tf.Variable(tf.zeros([784,10]))
- b=tf.Variable(tf.zeros([10]))
- #Construct model
- pred=tf.nn.softmax(tf.matmul(x,W)+b)
- #Minimize error using cross entropy
- cost=tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred),reduction_indices=1))
- #Gradient Descent
- optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
- #Initialize the variables
- init=tf.global_variables_initializer()
- #Start training
- with tf.Session() as sess:
- sess.run(init)
- #Training cycle
- for epoch in range(training_epochs):
- avg_cost=0
- total_batch=int(mnist.train.num_examples/batch_size)
- # loop over all batches
- for i in range(total_batch):
- batch_xs,batch_ys=mnist.train.next_batch(batch_size)
- #Fit training using batch data
- _,c=sess.run([optimizer,cost],feed_dict={x:batch_xs,y:batch_ys})
- #Conpute average loss
- avg_cost+= c/total_batch
- if (epoch+1) % display_step==0:
- print("Epoch:",'%04d' % (epoch+1),"Cost:" ,"{:.09f}".format(avg_cost))
- print("Optimization Finished!")
- #Test model
- correct_prediction=tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
- # Calculate accuracy for 3000 examples
- accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
- print("Accuracy:",accuracy.eval({x:mnist.test.images[:3000],y:mnist.test.labels[:3000]}))