这段代码对我有用。我自己整理一下:)
from sklearn.metrics import precision_recall_fscore_support as scorefrom sklearn.metrics import classification_reportdef print_confusion_matrix(plabels,tlabels):""" functions print the confusion matrix for the different classes to find the error... Input: ----------- plabels: predicted labels for the classes... tlabels: true labels for the classes pre from: http://stackoverflow.com/questions/2148543/how-to-write-a-confusion-matrix-in-python"""import pandas as pdplabels = pd.Series(plabels)tlabels = pd.Series(tlabels)# draw a cross tabulation...df_confusion = pd.crosstab(tlabels,plabels, rownames=['Actual'], colnames=['Predicted'], margins=True)#print df_confusionreturn df_confusiondef confusionMatrix(text,Labels,y_pred, not_partial): y_actu = np.where(Labels[:]==1)[1] df = print_confusion_matrix(y_pred,y_actu) print "n",df #print plt.imshow(df.as_matrix()) if not_partial: print "n",classification_report(y_actu, y_pred) print "nt------------------------------------------------------n"def do_eval(message, sess, correct_prediction, accuracy, pred, X_, y_,x,y): predictions = sess.run([correct_prediction], feed_dict={x: X_, y: y_}) prediction = tf.argmax(pred,1) labels = prediction.eval(feed_dict={x: X_, y: y_}, session=sess) print message, accuracy.eval({x: X_, y: y_}),"n" confusionMatrix("Partial Confusion matrix",y_,predictions[0], False)#Partial confusion Matrix confusionMatrix("Complete Confusion matrix",y_,labels, True) #complete confusion Matrix# Launch the graphwith tf.Session() as sess:sess.run(init)data = zip(X_train,y_train)data = np.array(data)data_size = len(data)num_batches_per_epoch = int(len(data)/batch_size) + 1for epoch in range(training_epochs): avg_cost = 0. # Shuffle the data at each epoch shuffle_indices = np.random.permutation(np.arange(data_size)) shuffled_data = data[shuffle_indices] for batch_num in range(num_batches_per_epoch): start_index = batch_num * batch_size end_index = min((batch_num + 1) * batch_size, data_size) sample = zip(*shuffled_data[start_index:end_index]) #picking up random batches from training set of specific size batch_xs, batch_ys = sample[0],sample[1] # Fit training using batch data sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys}) # Compute average loss avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/num_batches_per_epoch #append loss loss_history.append(avg_cost) # Display logs per epoch step if (epoch % display_step == 0): correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate training accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) trainAccuracy = accuracy.eval({x: X_train, y: y_train}) train_acc_history.append(trainAccuracy) # Calculate validation accuracy valAccuracy = accuracy.eval({x: X_val, y: y_val}) val_acc_history.append(valAccuracy) print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost), "train=",trainAccuracy,"val=", valAccuracyprint "Optimization Finished!n"# evaluation of modelcorrect_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate accuracyaccuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))do_eval("Accuracy of Gold Test set Results: ", sess, correct_prediction, accuracy, pred, X_gold, y_gold, x, y)这是示例输出：
Accuracy of Gold Test set Results: 0.642608Predicted False True AllActual 0 20 46 661 3 1 42 21 1 223 8 4 124 16 7 235 54 259 3136 41 14 557 11 2 138 48 94 1429 29 4 3310 17 4 2111 39 116 155All 307 552 859Predicted 0 1 2 3 4 5 6 7 8 9 10 11 AllActual 0 46 0 0 0 0 8 0 2 2 2 0 6 6610 1 0 1 0 2 0 0 0 0 0 0 423 0 1 3 0 12 0 0 1 0 0 2 2232 0 0 4 1 3 1 1 0 0 0 0 1241 0 0 0 7 12 0 0 1 0 0 2 2358 0 0 1 5 259 9 0 9 3 1 18 31361 0 0 1 6 30 14 1 2 0 0 0 5573 0 0 0 0 2 0 2 4 0 1 1 1386 0 0 1 1 18 0 3 94 8 1 10 14299 0 0 0 0 1 1 1 9 4 0 8 3310 1 0 0 0 3 6 0 1 1 0 4 5 2111 5 1 0 1 0 18 1 0 6 5 2 116 155All 85 2 1 12 23 371 26 11 129 22 9 168 859 precision recall f1-score support 0 0.54 0.70 0.61 66 1 0.50 0.25 0.33 4 2 1.00 0.05 0.09 22 3 0.33 0.33 0.33 12 4 0.30 0.30 0.30 23 5 0.70 0.83 0.76 313 6 0.54 0.25 0.35 55 7 0.18 0.15 0.17 13 8 0.73 0.66 0.69 142 9 0.18 0.12 0.15 33 10 0.44 0.19 0.27 21 11 0.69 0.75 0.72 155 avg / total 0.64 0.64 0.62 859
