完整的神經網路樣例程式(一)
阿新 • • 發佈:2018-12-12
import tensorflow as tf from numpy.random import RandomState w1=tf.Variable(tf.random_normal([2,3],stddev=1,seed=1)) w2=tf.Variable(tf.random_normal([3,1],stddev=1,seed=1)) batch_size=8 x=tf.placeholder(tf.float32,shape=(None,2),name="x-input") y_=tf.placeholder(tf.float32,shape=(None,1),name="y-input") a=tf.matmul(x,w1) y=tf.matmul(a,w2) y=tf.sigmoid(y) cross_entropy=-tf.reduce_mean(y_*tf.log(tf.clip_by_value(y,1e-10,1.0))+(1-y_)*tf.log(tf.clip_by_value(1-y,1e-10,1.0))) train_step=tf.train.AdamOptimizer(0.001).minimize(cross_entropy) rdm=RandomState(1) X=rdm.rand(128,2) Y=[[int(x1+x2<1)]for(x1,x2)in X] with tf.Session() as sess: init_op=tf.global_variables_initializer() sess.run(init_op) print(sess.run(w1)) print(sess.run(w2)) print("\n") STEPS=5000 for i in range(STEPS): start=(i*batch_size)%128 end=(i*batch_size)%128+batch_size sess.run([train_step,y,y_],feed_dict={x:X[start:end],y_:Y[start:end]}) if i%1000==0: total_cross_entropy=sess.run(cross_entropy,feed_dict={x:X,y_:Y}) print("After %d training step(s),cross entropy on all data is %g"%(i,total_cross_entropy)) print("\n") print(sess.run(w1)) print(sess.run(w2))