sess = tf.InteractiveSession()
sess.run(tf.initialize_all_variables())
for i in range(20000):
batch_xs, batch_ys = load_data.get_train_batch(50)
if i % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x: batch_xs, y_: batch_ys, keep_prob: 1.0})
print "step %d, training accuracy %g" % (i, train_accuracy)
train_step.run(feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 0.5})
#print "test accuracy %g"%accuracy.eval(feed_dict={
# x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})
test_set_x = load_data.load_test_data("/home/darshan/Documents/DigitRecognizer/MNIST_data/",
"test.csv")
print(test_set_x.shape)
nbr_of_test_batches = 10
batch_size = load_data.nbr_of_test_dp / nbr_of_test_batches
for j in xrange(nbr_of_test_batches):
test_batch = load_data.get_test_batch(batch_size)
if test_batch is not None:
y_predict = tf.argmax(y_conv, 1)
result_value = sess.run(y_predict, feed_dict={x: test_batch, keep_prob: 1.0})
result_label = xrange((batch_size * j) + 1, (batch_size * (j + 1)) + 1)
z = np.array(zip(result_label, result_value), dtype=[('ImageId', int), ('Label', int)])
np.savetxt('result_cnn' + str(j) + '.csv', z, fmt='%i,%i')
sess.close()
