In [1]:
import os
import numpy as np
from scipy import ndimage
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.examples.tutorials.mnist import input_data
%matplotlib inline
print ("当前TensorFlow版本为 [%s]" % (tf.__version__))
print ("所有包载入完毕")
In [2]:
mnist = input_data.read_data_sets('data/', one_hot=True)
trainimg = mnist.train.images
trainlabel = mnist.train.labels
valimg = mnist.validation.images
vallabel = mnist.validation.labels
testimg = mnist.test.images
testlabel = mnist.test.labels
print ("MNIST ready")
In [3]:
n_input = 784
n_classes = 10
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
is_training = tf.placeholder(tf.bool)
def lrelu(x, leak=0.2, name='lrelu'):
with tf.variable_scope(name):
f1 = 0.5 * (1 + leak)
f2 = 0.5 * (1 - leak)
return f1 * x + f2 * abs(x)
def CNN(inputs, is_training=True):
x = tf.reshape(inputs, [-1, 28, 28, 1])
batch_norm_params = {'is_training': is_training, 'decay': 0.9
, 'updates_collections': None}
init_func = tf.truncated_normal_initializer(stddev=0.01)
net = slim.conv2d(x, 32, [5, 5], padding='SAME'
, activation_fn = lrelu
, weights_initializer = init_func
, normalizer_fn = slim.batch_norm
, normalizer_params = batch_norm_params
, scope='conv1')
net = slim.max_pool2d(net, [2, 2], scope='pool1')
net = slim.conv2d(x, 64, [5, 5], padding='SAME'
, activation_fn = lrelu
, weights_initializer = init_func
, normalizer_fn = slim.batch_norm
, normalizer_params = batch_norm_params
, scope='conv2')
net = slim.max_pool2d(net, [2, 2], scope='pool2')
net = slim.flatten(net, scope='flatten3')
net = slim.fully_connected(net, 1024
, activation_fn = lrelu
, weights_initializer = init_func
, normalizer_fn = slim.batch_norm
, normalizer_params = batch_norm_params
, scope='fc4')
net = slim.dropout(net, keep_prob=0.7, is_training=is_training, scope='dr')
out = slim.fully_connected(net, n_classes
, activation_fn=None, normalizer_fn=None, scope='fco')
return out
print ("NETWORK READY")
In [4]:
# PREDICTION
pred = CNN(x, is_training)
# LOSS AND OPTIMIZER
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
labels=y, logits=pred))
optm = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
corr = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accr = tf.reduce_mean(tf.cast(corr, "float"))
# INITIALIZER
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
print ("FUNCTIONS READY")
In [5]:
print ("=================== TRAINABLE VARIABLES ===================")
t_weights = tf.trainable_variables()
var_names_list = [v.name for v in tf.trainable_variables()]
for i in range(len(t_weights)):
wval = sess.run(t_weights[i])
print ("[%d/%d] [%s] / SAHPE IS %s"
% (i, len(t_weights), var_names_list[i], wval.shape,))
In [6]:
savedir = "nets/cnn_mnist_modern/"
saver = tf.train.Saver(max_to_keep=100)
save_step = 4
if not os.path.exists(savedir):
os.makedirs(savedir)
print ("SAVER READY")
In [7]:
def augment_img(xs):
out = np.copy(xs)
xs_r = np.reshape(xs, [-1, 28, 28])
for i in range(xs_r.shape[0]):
xs_img = xs_r[i, :, :]
bg_value = 0
# ROTATE
angle = np.random.randint(-15, 15, 1).astype(float)
xs_img = ndimage.rotate(xs_img, angle, reshape=False, cval=bg_value)
# ZOOM
rg = 0.1
zoom_factor = np.random.uniform(1., 1.+rg)
h, w = xs_img.shape[:2]
zh = int(np.round(zoom_factor * h))
zw = int(np.round(zoom_factor * w))
top = (zh - h) // 2
left = (zw - w) // 2
zoom_tuple = (zoom_factor,) * 2 + (1,) * (xs_img.ndim - 2)
temp = ndimage.zoom(xs_img[top:top+zh, left:left+zw], zoom_tuple)
trim_top = ((temp.shape[0] - h) // 2)
trim_left = ((temp.shape[1] - w) // 2)
xs_img = temp[trim_top:trim_top+h, trim_left:trim_left+w]
# SHIFT
shift = np.random.randint(-3, 3, 2)
xs_img = ndimage.shift(xs_img, shift, cval=bg_value)
# RESHAPE
xs_v = np.reshape(xs_img, [1, -1])
out[i, :] = xs_v
return out
In [ ]:
naug = 2
batch_xs = trainimg[:naug, :]
xs2 = augment_img(batch_xs)
for i in range(naug):
x1 = batch_xs[i, :].reshape([28, 28])
x2 = xs2[i, :].reshape([28, 28])
plt.matshow(x1, vmin=0, vmax=1, cmap=plt.cm.gray)
plt.title("ORIGINAL")
plt.show()
plt.matshow(x2, vmin=0, vmax=1, cmap=plt.cm.gray)
plt.title("TRANSFORMED")
plt.show()
In [ ]:
# PARAMETERS
training_epochs = 100
batch_size = 50
display_step = 4
val_acc = 0
val_acc_max = 0
# OPTIMIZE
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(mnist.train.num_examples/batch_size)
# ITERATION
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
# AUGMENT DATA
batch_xs = augment_img(batch_xs)
feeds = {x: batch_xs, y: batch_ys, is_training: True}
sess.run(optm, feed_dict=feeds)
avg_cost += sess.run(cost, feed_dict=feeds)
avg_cost = avg_cost / total_batch
# DISPLAY
if (epoch+1) % display_step == 0:
print ("Epoch: %03d/%03d cost: %.9f" % (epoch+1, training_epochs, avg_cost))
randidx = np.random.permutation(trainimg.shape[0])[:500]
feeds = {x: trainimg[randidx], y: trainlabel[randidx], is_training: False}
train_acc = sess.run(accr, feed_dict=feeds)
print (" TRAIN ACCURACY: %.5f" % (train_acc))
feeds = {x: valimg, y: vallabel, is_training: False}
val_acc = sess.run(accr, feed_dict=feeds)
print (" VALIDATION ACCURACY: %.5f" % (val_acc))
# SAVE
if (epoch+1) % save_step == 0:
savename = savedir + "net-" + str(epoch) + ".ckpt"
saver.save(sess=sess, save_path=savename)
print (" [%s] SAVED." % (savename))
# MAXIMUM VALIDATION ACCURACY
if val_acc > val_acc_max:
val_acc_max = val_acc
best_epoch = epoch
print ("\x1b[31m BEST EPOCH UPDATED!! [%d] \x1b[0m" % (best_epoch))
print ("OPTIMIZATION FINISHED")
In [ ]:
best_epoch = 55
restorename = savedir + "net-" + str(best_epoch) + ".ckpt"
print ("LOADING [%s]" % (restorename))
saver.restore(sess, restorename)
feeds = {x: testimg, y: testlabel, is_training: False}
test_acc = sess.run(accr, feed_dict=feeds)
print ("TEST ACCURACY: %.5f" % (test_acc))