只是简单demo， 可以看出tensorflow非常简洁，适合快速实验
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import tensorflow as tf
import numpy as np
import melt_dataset
import sys
from sklearn.metrics import roc_auc_score
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def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.01))
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def model(X, w_h, w_o):
h = tf.nn.sigmoid(tf.matmul(X, w_h)) # this is a basic mlp, think 2 stacked logistic regressions
return tf.matmul(h, w_o) # note that we dont take the softmax at the end because our cost fn does that for us
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batch_size = 50
learning_rate = 0.1
num_iters = 500
hidden_size = 20
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argv = sys.argv
trainset = argv[1]
testset = argv[2]
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trX, trY = melt_dataset.load_dense_data(trainset)
print "finish loading train set ",trainset
teX, teY = melt_dataset.load_dense_data(testset)
print "finish loading test set ", testset
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num_features = trX[0].shape[0]
print ‘num_features: ‘,num_features
print ‘trainSet size: ‘, len(trX)
print ‘testSet size: ‘, len(teX)
print ‘batch_size:‘, batch_size, ‘ learning_rate:‘, learning_rate, ‘ num_iters:‘, num_iters
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X = tf.placeholder("float", [None, num_features]) # create symbolic variables
Y = tf.placeholder("float", [None, 1])
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w_h = init_weights([num_features, hidden_size]) # create symbolic variables
w_o = init_weights([hidden_size, 1])
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py_x = model(X, w_h, w_o)
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cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(py_x, Y)) # compute costs
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # construct an optimizer
predict_op = tf.nn.sigmoid(py_x)
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sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
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for i in range(num_iters):
predicts, cost_ = sess.run([predict_op, cost], feed_dict={X: teX, Y: teY})
print i, ‘auc:‘, roc_auc_score(teY, predicts), ‘cost:‘, cost_
for start, end in zip(range(0, len(trX), batch_size), range(batch_size, len(trX), batch_size)):
sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})
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predicts, cost_ = sess.run([predict_op, cost], feed_dict={X: teX, Y: teY})
print ‘final ‘, ‘auc:‘, roc_auc_score(teY, predicts),‘cost:‘, cost_
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python ./mlp.py corpus/feature.normed.rand.12000.0_2.txt corpus/feature.normed.rand.12000.1_2.txt
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233 auc: 0.932099377357 cost: 0.210673
234 auc: 0.93210173764 cost: 0.210674
235 auc: 0.93210173764 cost: 0.210675
236 auc: 0.932089936225 cost: 0.210676
原文：http://www.cnblogs.com/rocketfan/p/4984577.html