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| import gzip
import os
import tempfile
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.contrib.layers import flatten
from tensorflow.examples.tutorials.mnist import input_data
from sklearn.utils import shuffle
"""
Data Loading
"""
dataPath='../dataset/MNIST_data/'
mnist = input_data.read_data_sets(dataPath, one_hot=True)
# read the images and reformat the image shape from [img_num,img_height,img_width] to [img_num,img_height,img_width,1]
img_width = 28
img_height = 28
images = mnist.train.images
img_num, _ = images.shape
images = np.reshape(images,(img_num,img_height,img_width))
images = images[...,np.newaxis]
print('(Input to CNN) Images with shape {}'.format(images.shape))
# read the labels
labels1Hot = mnist.train.labels
print('(Input to CNN) labels1Hot.shape = {}'.format(labels1Hot.shape))
labels = np.argmax(labels1Hot,axis=1)
labels = labels[...,np.newaxis]
print('labels.shape = {}'.format(labels.shape))
n_classes = len(np.unique(labels))# read the labels
labels1Hot = mnist.train.labels
print('(Input to CNN) labels1Hot.shape = {}'.format(labels1Hot.shape))
labels = np.argmax(labels1Hot,axis=1)
labels = labels[...,np.newaxis]
print('labels.shape = {}'.format(labels.shape))
n_classes = len(np.unique(labels))
# load the validation set
images_valid = mnist.validation.images
img_num_valid = len(images_valid)
images_valid = np.reshape(images_valid,(img_num_valid,img_height,img_width))
images_valid = images_valid[...,np.newaxis]
labels1Hot_valid = mnist.validation.labels
print('Having %d number of validation images' % img_num_valid)
plt.figure(figsize=(15,5))
for i in np.arange(2*7):
random_idx = np.random.randint(0,img_num)
plt.subplot(2,7,i+1)
plt.imshow(images[random_idx][...,0],cmap='gray')
plt.title(labels[random_idx][0])
"""
First define the hyper-parameters
"""
# Hyper-parameters
EPOCHS = 30
BATCH_SIZE = 512
rate = 0.001
drop_out_keep_prob = 0.5
ksize = 5
cnn_depth_list = [16, 32]
mlp_depth_list = [256, 128]
cNum = 1
"""
Define the graph and construct it
"""
class MNISTCNN:
def __init__(self, ksize, cnn_depth_list, mlp_depth_list, img_height, img_width, cNum, n_classes):
self._ksize = ksize
self._cnn_depth_list = cnn_depth_list
self._mlp_depth_list = mlp_depth_list
self._img_height = img_height
self._img_width = img_width
self._cNum = cNum
self._n_classes = n_classes
self._mu = 0
self._sigma = 0.1
def create(self,x,keep_prob):
conv = self._conv(x, self._cNum, self._ksize, self._cnn_depth_list[0], 'conv1')
# Pooling. Input = 24x24xlayer_depth['layer_1']. Output = 12x12xlayer_depth['layer_1'].
conv = tf.nn.max_pool(conv, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
for lidx in range(1,len(self._cnn_depth_list)):
conv = self._conv(conv, self._cnn_depth_list[lidx-1], self._ksize, self._cnn_depth_list[lidx], 'conv{}'.format(lidx+1))
conv = tf.nn.max_pool(conv, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
fc = flatten(conv)
bsize, fc_in_dim = fc.shape
fc = self._fc(fc,fc_in_dim,self._mlp_depth_list[0],'fc1')
fc = tf.nn.dropout(fc, keep_prob) # dropout
for lidx in range(1,len(self._mlp_depth_list)):
fc = self._fc(fc,self._mlp_depth_list[lidx-1],self._mlp_depth_list[lidx],'fc{}'.format(lidx+1))
fc = tf.nn.dropout(fc, keep_prob) # dropout
with tf.variable_scope('logits') as scope:
with tf.device('/cpu:0'):
logits_w = tf.get_variable('logits_w', shape=[self._mlp_depth_list[-1],self._n_classes],\
initializer=tf.random_uniform_initializer(-0.1,0.1))
logits_b = tf.get_variable('logits_b', shape=[self._n_classes],\
initializer=tf.zeros_initializer)
logits = tf.nn.xw_plus_b(fc, logits_w, logits_b, name=scope.name)
print(logits.shape)
return logits
def _conv(self, x, in_depth, ksize, out_depth, scope_name, relu=True):
bsize,h,w,cNum = x.shape
assert(h-(ksize-1)>=1)
assert(w-(ksize-1)>=1)
with tf.variable_scope(scope_name) as scope:
with tf.device('/cpu:0'):
# Create tf variables for the weights and biases
weights = tf.get_variable('weights', shape=(ksize, ksize, in_depth, out_depth),\
initializer=tf.random_normal_initializer(self._mu,self._sigma))
biases = tf.get_variable('biases', shape=(out_depth),initializer=tf.zeros_initializer)
out = tf.nn.conv2d(x, weights, strides=[1, 1, 1, 1], padding='VALID',name=scope.name) + biases
if relu:
# Apply ReLu non linearity
relu = tf.nn.relu(out)
return relu
else:
return out
def _fc(self, x, num_in, num_out, scope_name, relu=True):
"""Create a fully connected layer."""
with tf.variable_scope(scope_name) as scope:
with tf.device('/cpu:0'):
# Create tf variables for the weights and biases
weights = tf.get_variable('weights', shape=[num_in, num_out],\
initializer=tf.random_uniform_initializer(-0.1,0.1))
biases = tf.get_variable('biases', [num_out],\
initializer=tf.zeros_initializer)
# Matrix multiply weights and inputs and add bias
out = tf.nn.xw_plus_b(x, weights, biases, name=scope.name)
if relu:
# Apply ReLu non linearity
relu = tf.nn.relu(out)
return relu
else:
return out
mnistCNN = MNISTCNN(ksize, cnn_depth_list, mlp_depth_list, img_height, img_width, cNum, n_classes)
# Averaging gradients for all tower models on GPU
def average_gradients(tower_grads):
"""Calculate the average gradient for each shared variable across all towers.
Note that this function provides a synchronization point across all towers.
Args:
tower_grads: List of lists of (gradient, variable) tuples. The outer list
is over individual gradients. The inner list is over the gradient
calculation for each tower.
Returns:
List of pairs of (gradient, variable) where the gradient has been averaged
across all towers.
"""
average_grads = []
for grad_and_vars in zip(*tower_grads):
# Note that each grad_and_vars looks like the following:
# ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
grads = []
for g, _ in grad_and_vars:
# Add 0 dimension to the gradients to represent the tower.
expanded_g = tf.expand_dims(g, 0)
# Append on a 'tower' dimension which we will average over below.
grads.append(expanded_g)
# Average over the 'tower' dimension.
grad = tf.concat(axis=0, values=grads)
grad = tf.reduce_mean(grad, 0)
# Keep in mind that the Variables are redundant because they are shared
# across towers. So .. we will just return the first tower's pointer to
# the Variable.
v = grad_and_vars[0][1]
grad_and_var = (grad, v)
average_grads.append(grad_and_var)
return average_grads
# Construct model for each GPU, where variables are shared/updated by CPU
with tf.device('/cpu:0'):
optimizer = tf.train.AdamOptimizer(learning_rate = rate)
# Calculate the gradients for each model tower.
tower_grads = []
logits_list = []
feed_x = []
feed_one_hot_y = []
keep_prob = tf.placeholder(tf.float32) # probability to keep units
with tf.variable_scope(tf.get_variable_scope()):
for i in range(2):
with tf.device('/gpu:%d' % i):
x = tf.placeholder(tf.float32, (None, img_height, img_width, cNum))
feed_x.append(x)
one_hot_y = tf.placeholder(tf.int32, (None, n_classes))
feed_one_hot_y.append(one_hot_y)
logits = mnistCNN.create(x,keep_prob)
logits_list.append(logits)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y,logits=logits)
loss_op = tf.reduce_mean(cross_entropy)
tf.get_variable_scope().reuse_variables()
# Calculate the gradients for each batch of data on this model tower.
grads = optimizer.compute_gradients(loss_op)
# Keep track of the gradients across all towers.
tower_grads.append(grads)
with tf.device('/cpu:0'):
# We must calculate the mean of each gradient. Note that this is the
# synchronization point across all towers.
grads = average_gradients(tower_grads)
# Apply the gradients to adjust the shared variables.
apply_gradient_op = optimizer.apply_gradients(grads)
training_op = apply_gradient_op
"""
Prediction/Inference Part
"""
# Define accuracy evaluation, calculate the average accuracy by calling evaluate(X_data, y_data)
# Using model that in the First GPU to calculate
correct_prediction = tf.equal(tf.argmax(logits_list[0], axis=1), tf.argmax(feed_one_hot_y[0], axis=1))
accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
def evaluate(X_data, y_data):
num_examples = len(X_data)
total_accuracy = 0
sess = tf.get_default_session()
for offset in range(0, num_examples, BATCH_SIZE):
batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
accuracy = sess.run(accuracy_operation, feed_dict={feed_x[0]: batch_x, feed_one_hot_y[0]: batch_y, keep_prob: 1.0})
total_accuracy += (accuracy * len(batch_x))
return total_accuracy / num_examples
"""
Run Session
"""
# {feed_x[0]:batch_x_1, feed_x[1]:batch_x_2,\
# feed_one_hot_y[0]:batch_y_1, feed_one_hot_y[1]:batch_y_1, keep_prob:drop_out_keep_prob}
def gen_feed_dict(batch_x, batch_y, drop_out_keep_prob):
assert(len(batch_x)==len(batch_y))
assert(len(batch_x)%2==0)
data_num = int(len(batch_x)/2)
rtn_dict = {}
rtn_dict[feed_x[0]] = batch_x[:data_num]
rtn_dict[feed_x[1]] = batch_x[data_num:]
rtn_dict[feed_one_hot_y[0]] = batch_y[:data_num]
rtn_dict[feed_one_hot_y[1]] = batch_y[data_num:]
rtn_dict[keep_prob] = drop_out_keep_prob
return rtn_dict
### Train your model here.
import time
if not os.path.isdir('./models'):
os.makedirs('./models')
#saver = tf.train.Saver()
accumulate_time = 0.0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
num_examples = img_num
print("Training...")
print()
train_accuracy = np.zeros(EPOCHS)
validation_accuracy = np.zeros(EPOCHS)
stime = time.time()
for i in range(EPOCHS):
stime = time.time()
acc_train_accuracy = 0
X_train, y_train = shuffle(images, labels1Hot)
for offset in range(0, num_examples, BATCH_SIZE):
end = offset + BATCH_SIZE
batch_x, batch_y = X_train[offset:end], y_train[offset:end]
feed_dict = gen_feed_dict(batch_x, batch_y, drop_out_keep_prob)
sess.run(training_op, feed_dict=feed_dict)
etime = time.time()
accumulate_time += etime - stime
validation_accuracy[i] = evaluate(images_valid, labels1Hot_valid)
print("EPOCH {} ...".format(i+1))
print("Validation Accuracy = {:.3f}".format(validation_accuracy[i]))
print('Cost time: ' + str(accumulate_time) + ' sec.')
|
Chih-Sheng Chen
