''' """ Neural Network. A 2-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron) implementation with TensorFlow. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/). Links: [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' from __future__ import print_function import tensorflow as tf import os from sklearn.model_selection import train_test_split import pandas as pd import numpy as np from sklearn import preprocessing # Import data def loadDataSet(fileName): dataMat =[] fr = open (fileName) for line in fr.readlines(): lineArr = line.strip().split('\t') dataMat.append([float(lineArr[0]),float(lineArr[1]),float(lineArr[2])]) return dataMat homedir = os.getcwd()#get now path dataMat = loadDataSet(homedir+'/testSet.txt') dataDf=pd.DataFrame(dataMat) Xtr, Xte, ytr, yte = train_test_split(dataDf.ix[:,0:1], dataDf.ix[:,2], test_size=0.1, random_state=0)#X[:,m:n],m to n-1 ''' enc = preprocessing.OneHotEncoder() enc.fit(ytr).transform() ytr=enc.transform(ytr).toarray() ''' # Parameters learning_rate = 0.001 num_steps = 500 display_step = 100 # Network Parameters n_hidden_1 = 20 # 1st layer number of neurons n_hidden_2 = 20 # 2nd layer number of neurons num_input = 2 # data input (2 dimensions) num_classes = 2 # total classes (0-1 digits) #y labels one-hot def dense_to_one_hot(labels_dense, num_classes): """Convert class labels from scalars to one-hot vectors.""" num_labels = labels_dense.shape[0] index_offset = np.arange(num_labels) * num_classes labels_one_hot = np.zeros((num_labels, num_classes)) labels_one_hot.flat[[index_offset + labels_dense.ravel()]] = 1 return labels_one_hot ytr = dense_to_one_hot(ytr,num_classes) yte = dense_to_one_hot(yte,num_classes) # tf Graph input X = tf.placeholder("float", [None, num_input]) Y = tf.placeholder("float", [None, num_classes]) # Store layers weight & bias weights = { 'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])),#num_input*n_hidden_1 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), 'out': tf.Variable(tf.random_normal([n_hidden_2, num_classes])) } biases = { 'b1': tf.Variable(tf.random_normal([n_hidden_1])), 'b2': tf.Variable(tf.random_normal([n_hidden_2])), 'out': tf.Variable(tf.random_normal([num_classes])) } # Create model def neural_net(x): # Hidden fully connected layer with 256 neurons layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) # Hidden fully connected layer with 256 neurons layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) # Output fully connected layer with a neuron for each class out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] return out_layer # Construct model logits = neural_net(X) prediction = tf.nn.sigmoid(logits)#two classifier # Define loss and optimizer loss_op = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=Y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) train_op = optimizer.minimize(loss_op) # Evaluate model correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() # Start training with tf.Session() as sess: # Run the initializer sess.run(init) for step in range(1, num_steps+1): # Run optimization op (backprop) sess.run(train_op, feed_dict={X: Xtr, Y: ytr}) if step % display_step == 0 or step == 1: # Calculate batch loss and accuracy loss, acc = sess.run([loss_op, accuracy], feed_dict={X: Xtr,Y: ytr}) print("Step " + str(step) + ", Minibatch Loss= " + "{:.4f}".format(loss) + ", Training Accuracy= " +"{:.3f}".format(acc)) print("Optimization Finished!") # Calculate accuracy for MNIST test images print("Testing Accuracy:", sess.run(accuracy, feed_dict={X: Xte,Y: yte}))
数据testSet.txt
-0.017612 14.053064 0 -1.395634 4.662541 1 -0.752157 6.538620 0 -1.322371 7.152853 0 0.423363 11.054677 0 0.406704 7.067335 1 0.667394 12.741452 0 -2.460150 6.866805 1 0.569411 9.548755 0 -0.026632 10.427743 0 0.850433 6.920334 1 1.347183 13.175500 0 1.176813 3.167020 1 -1.781871 9.097953 0 -0.566606 5.749003 1 0.931635 1.589505 1 -0.024205 6.151823 1 -0.036453 2.690988 1 -0.196949 0.444165 1 1.014459 5.754399 1 1.985298 3.230619 1 -1.693453 -0.557540 1 -0.576525 11.778922 0 -0.346811 -1.678730 1 -2.124484 2.672471 1 1.217916 9.597015 0 -0.733928 9.098687 0 -3.642001 -1.618087 1 0.315985 3.523953 1 1.416614 9.619232 0 -0.386323 3.989286 1 0.556921 8.294984 1 1.224863 11.587360 0 -1.347803 -2.406051 1 1.196604 4.951851 1 0.275221 9.543647 0 0.470575 9.332488 0 -1.889567 9.542662 0 -1.527893 12.150579 0 -1.185247 11.309318 0 -0.445678 3.297303 1 1.042222 6.105155 1 -0.618787 10.320986 0 1.152083 0.548467 1 0.828534 2.676045 1 -1.237728 10.549033 0 -0.683565 -2.166125 1 0.229456 5.921938 1 -0.959885 11.555336 0 0.492911 10.993324 0 0.184992 8.721488 0 -0.355715 10.325976 0 -0.397822 8.058397 0 0.824839 13.730343 0 1.507278 5.027866 1 0.099671 6.835839 1 -0.344008 10.717485 0 1.785928 7.718645 1 -0.918801 11.560217 0 -0.364009 4.747300 1 -0.841722 4.119083 1 0.490426 1.960539 1 -0.007194 9.075792 0 0.356107 12.447863 0 0.342578 12.281162 0 -0.810823 -1.466018 1 2.530777 6.476801 1 1.296683 11.607559 0 0.475487 12.040035 0 -0.783277 11.009725 0 0.074798 11.023650 0 -1.337472 0.468339 1 -0.102781 13.763651 0 -0.147324 2.874846 1 0.518389 9.887035 0 1.015399 7.571882 0 -1.658086 -0.027255 1 1.319944 2.171228 1 2.056216 5.019981 1 -0.851633 4.375691 1 -1.510047 6.061992 0 -1.076637 -3.181888 1 1.821096 10.283990 0 3.010150 8.401766 1 -1.099458 1.688274 1 -0.834872 -1.733869 1 -0.846637 3.849075 1 1.400102 12.628781 0 1.752842 5.468166 1 0.078557 0.059736 1 0.089392 -0.715300 1 1.825662 12.693808 0 0.197445 9.744638 0 0.126117 0.922311 1 -0.679797 1.220530 1 0.677983 2.556666 1 0.761349 10.693862 0 -2.168791 0.143632 1 1.388610 9.341997 0 0.317029 14.739025 0
执行结果:
Step 1, Minibatch Loss= 45.5055, Training Accuracy= 0.489 Step 100, Minibatch Loss= 0.1006, Training Accuracy= 0.967 Step 200, Minibatch Loss= 0.0741, Training Accuracy= 0.967 Step 300, Minibatch Loss= 0.0736, Training Accuracy= 0.967 Step 400, Minibatch Loss= 0.0731, Training Accuracy= 0.967 Step 500, Minibatch Loss= 0.0726, Training Accuracy= 0.967 Optimization Finished! Testing Accuracy: 0.8