TensorFlow for machine learning 基本模板

TensorFlow for machine learning 基本模板

import tensorflow as tfimport os#paramatesW = tf.Variable(tf.zeros([5,1]),name = 'weight')b = tf.Variable(0,name = 'bias')def combine_inputs(X):    return tf.mul(X,W)+b# forward propagationdef inference(X):    return tf.sigmoid(combine_inputs(X))# cost functiondef loss(X,Y):    return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(combine_inputs(X) , Y) )def train(total_loss)    learning_rate = 0.00001    return tf.train.GraidentDescendOptimizer(learning_rate).minimize(total_loss)def evaluate(X):    return tf.cast(interence(X)>0.5 , tf.float32)With tf.Session() as sess:    tf.initial_all_variables().run()    X,Y = inputs()    total_loss = loss(X,Y)    train_op = train(total_loss)    coord = tf.train.Coordinator()    threads = tf.train_start_queue_runners(sess = sess ,coord = coord)    #actual training loop    training_step = 1000    for step in range(training_step):        sess.run([train_op])    evaluate(X,Y)