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Q: Why is TensorFlow predicting all 0's or all 1's after training? So my problem is that I am running through the beginner level code in the TensorFlow tutorial and have modified it for my needs but when I make it print sess.run(accuracy, feed_dict={x: x_test, y_: y_test}) it used to always print out a 1.0, now it's always guessing 0's and printing out an ~93% accuracy. When I use tf.argmin(y,1), tf.argmin(y_,1), it guesses all 1's and produces an ~7% accuracy rate. Add up the two and it equals 100%. I don't get how tf.argmin guesses 1's and tf.argmax guesses 0's. Obviously something is wrong with the code. Please have a look and let me know what I can do to fix this issue. I think that the code is going wrong during the training, but I could be wrong. import tensorflow as tf import numpy as np from numpy import genfromtxt data = genfromtxt('cs-training.csv',delimiter=',') # Training data test_data = genfromtxt('cs-test.csv',delimiter=',') # Test data x_train = [] for i in data: x_train.append(i[1:]) x_train = np.array(x_train) y_train = [] for i in data: if i[0] == 0: y_train.append([1., i[0]]) else: y_train.append([0., i[0]]) y_train = np.array(y_train) where_are_NaNs = isnan(x_train) x_train[where_are_NaNs] = 0 x_test = [] for i in test_data: x_test.append(i[1:]) x_test = np.array(x_test) y_test = [] for i in test_data: if i[0] == 0: y_test.append([1., i[0]]) else: y_test.append([0., i[0]]) y_test = np.array(y_test) where_are_NaNs = isnan(x_test) x_test[where_are_NaNs] = 0 x = tf.placeholder("float", [None, 10]) W = tf.Variable(tf.zeros([10,2])) b = tf.Variable(tf.zeros([2])) y = tf.nn.softmax(tf.matmul(x,W) + b) y_ = tf.placeholder("float", [None,2]) cross_entropy = -tf.reduce_sum(y_*tf.log(y)) train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) init = tf.initialize_all_variables() sess = tf.Session() sess.run(init) print "...Training..." g = 0 for i in range(len(x_train)): sess.run(train_step, feed_dict={x: [x_train[g]], y_: [y_train[g]]}) g += 1 At this point, if I make it print [x_train[g]] and print [y_train[g]], this is what the results look like. [array([ 7.66126609e-01, 4.50000000e+01, 2.00000000e+00, 8.02982129e-01, 9.12000000e+03, 1.30000000e+01, 0.00000000e+00, 6.00000000e+00, 0.00000000e+00, 2.00000000e+00])] [array([ 0., 1.])] Ok, let's carry on then. correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print sess.run(accuracy, feed_dict={x: x_test, y_: y_test}) 0.929209 This percentage does not shift. It's guessing all zeros regardless of the onehot that I created for the 2 classes (1 or 0). Here's a look at the data- print x_train[:10] [[ 7.66126609e-01 4.50000000e+01 2.00000000e+00 8.02982129e-01 9.12000000e+03 1.30000000e+01 0.00000000e+00 6.00000000e+00 0.00000000e+00 2.00000000e+00] [ 9.57151019e-01 4.00000000e+01 0.00000000e+00 1.21876201e-01 2.60000000e+03 4.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00] [ 6.58180140e-01 3.80000000e+01 1.00000000e+00 8.51133750e-02 3.04200000e+03 2.00000000e+00 1.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] [ 2.33809776e-01 3.00000000e+01 0.00000000e+00 3.60496820e-02 3.30000000e+03 5.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] [ 9.07239400e-01 4.90000000e+01 1.00000000e+00 2.49256950e-02 6.35880000e+04 7.00000000e+00 0.00000000e+00 1.00000000e+00 0.00000000e+00 0.00000000e+00] [ 2.13178682e-01 7.40000000e+01 0.00000000e+00 3.75606969e-01 3.50000000e+03 3.00000000e+00 0.00000000e+00 1.00000000e+00 0.00000000e+00 1.00000000e+00] [ 3.05682465e-01 5.70000000e+01 0.00000000e+00 5.71000000e+03 0.00000000e+00 8.00000000e+00 0.00000000e+00 3.00000000e+00 0.00000000e+00 0.00000000e+00] [ 7.54463648e-01 3.90000000e+01 0.00000000e+00 2.09940017e-01 3.50000000e+03 8.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] [ 1.16950644e-01 2.70000000e+01 0.00000000e+00 4.60000000e+01 0.00000000e+00 2.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] [ 1.89169052e-01 5.70000000e+01 0.00000000e+00 6.06290901e-01 2.36840000e+04 9.00000000e+00 0.00000000e+00 4.00000000e+00 0.00000000e+00 2.00000000e+00]] print y_train[:10] [[ 0. 1.] [ 1. 0.] [ 1. 0.] [ 1. 0.] [ 1. 0.] [ 1. 0.] [