confusion-matrix

I have implemented a Nueral Network model for a classification with the help of using TensorFlow. But, i don't know how can i able to draw confusion matrix by using predicted scores (accuracy). I am not an expert of TensorFlow and still in learning phase. Here i pasted my code below please tell me how can i write a code for making confusion from the following code: # Launch the graph with tf.Session() as sess: sess.run(init) # Set logs writer into folder /tmp/tensorflow_logs #summary_writer = tf.train.SummaryWriter('/tmp/tensorflow_logs', graph_def=sess.graph_def) # Training cycle for epoch in

I am plotting a confusion matrix with matplotlib with the following code: from numpy import * import matplotlib.pyplot as plt from pylab import * conf_arr = [[33,2,0,0,0,0,0,0,0,1,3], [3,31,0,0,0,0,0,0,0,0,0], [0,4,41,0,0,0,0,0,0,0,1], [0,1,0,30,0,6,0,0,0,0,1], [0,0,0,0,38,10,0,0,0,0,0], [0,0,0,3,1,39,0,0,0,0,4], [0,2,2,0,4,1,31,0,0,0,2], [0,1,0,0,0,0,0,36,0,2,0], [0,0,0,0,0,0,1,5,37,5,1], [3,0,0,0,0,0,0,0,0,39,0], [0,0,0,0,0,0,0,0,0,0,38] ] norm_conf = [] for i in conf_arr: a = 0 tmp_arr = [] a = sum(i,0) for j in i: tmp_arr.append(float(j)/float(a)) norm_conf.append(tmp_arr) plt.clf() fig =

I'm using Python and have some confusion matrixes. I'd like to calculate precisions and recalls and f-measure by confusion matrixes in multiclass classification. My result logs don't contain y_true and y_pred , just contain confusion matrix. Could you tell me how to get these scores from confusion matrix in multiclass classification? Let's consider the case of MNIST data classification (10 classes), where for a test set of 10,000 samples we get the following confusion matrix cm (Numpy array): array([[ 963, 0, 0, 1, 0, 2, 11, 1, 2, 0], [ 0, 1119, 3, 2, 1, 0, 4, 1, 4, 1], [ 12, 3, 972, 9, 6, 0,