def main():
Ntrain = 20000
# Input data from file
fname_train_image = "./data/train-images.idx3-ubyte"
fname_train_label = "./data/train-labels.idx1-ubyte"
fname_test_image = "./data/t10k-images.idx3-ubyte"
fname_test_label = "./data/t10k-labels.idx1-ubyte"
train_sample, valid_sample = LoadSample(fname_train_image,
fname_train_label,
validate=True)
test_sample = LoadSample(fname_test_image, fname_test_label)
print(train_sample.images.shape)
while input("q: quit, other : continue ") != 'q':
i = np.random.randint(train_sample.images.shape[0])
arr = train_sample.images[i, :]
lab = train_sample.labels[i, :]
for j in range(10):
if lab[j] == 1: print("Number = ", j)
arr = 255 * np.ones([28, 28]) - arr.reshape(28, 28)
plt.gray()
plt.imshow(arr)
plt.show()
i += 1
mnist = base.Datasets(train=train_sample,
validation=valid_sample,
test=test_sample)
model = Model("model")
model.build()
model.define_loss()
train_step = model.Get_train_step()
## save graph
saver = model.Get_Saver()
graph_location = "./logs/"
sum_accuracy = tf.summary.scalar("accuracy", model.accuracy)
sum_xentropy = tf.summary.scalar("cross entropy", model.cross_entropy)
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())
print('Saving graph to: %s' % graph_location)
##
with tf.Session() as sess:
init = model.Get_initializer()
sess.run(init)
for i in range(Ntrain):
batch = mnist.train.next_batch(50)
if i % 100 == 0:
feed_dict = model.Get_feed_dict(batch[0], batch[1], 1)
train_accuracy = model.accuracy.eval(feed_dict=feed_dict)
tmp_sum_accuracy = sum_accuracy.eval(feed_dict=feed_dict)
tmp_sum_xentropy = sum_xentropy.eval(feed_dict=feed_dict)
train_writer.add_summary(tmp_sum_accuracy, i)
train_writer.add_summary(tmp_sum_xentropy, i)
print('step %d, training accuracy %g' % (i, train_accuracy))
feed_dict = model.Get_feed_dict(batch[0], batch[1], 0.5)
train_step.run(feed_dict=feed_dict
) # dropout 50% neurons within 1024->10 layer
feed_dict = model.Get_feed_dict(mnist.test.images, mnist.test.labels,
1)
print('test accuracy %g' % model.accuracy.eval(feed_dict=feed_dict))
savePath = saver.save(sess, "./out/Model_out")
train_writer.flush()
train_writer.close()
