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densenetfinaltest.py
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densenetfinaltest.py
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# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import numpy as np
import tensorflow as tf
def unpickle(file):
import _pickle as cPickle
fo = open(file, 'rb')
dict = cPickle.load(fo,encoding='latin1')
fo.close()
if 'data' in dict:
dict['data'] = dict['data'].reshape((-1, 3, 32, 32)).swapaxes(1, 3).swapaxes(1, 2).reshape(-1, 32*32*3) / 256.
return dict
def load_data_one(f):
batch = unpickle(f)
data = batch['data']
labels = batch['labels']
print ("Loading %s: %d" % (f, len(data)))
return data, labels
def load_data(files, data_dir, label_count):
data, labels = load_data_one(data_dir + '/' + files[0])
for f in files[1:]:
data_n, labels_n = load_data_one(data_dir + '/' + f)
data = np.append(data, data_n, axis=0)
labels = np.append(labels, labels_n, axis=0)
labels = np.array([ [ float(i == label) for i in range(label_count) ] for label in labels ])
return data, labels
def run_in_batch_avg(session, tensors, batch_placeholders, feed_dict={}, batch_size=200):
res = [ 0 ] * len(tensors)
batch_tensors = [ (placeholder, feed_dict[ placeholder ]) for placeholder in batch_placeholders ]
total_size = len(batch_tensors[0][1])
batch_count = int((total_size + batch_size - 1) / batch_size)
for batch_idx in range(batch_count):
current_batch_size = None
for (placeholder, tensor) in batch_tensors:
batch_tensor = tensor[ batch_idx*batch_size : (batch_idx+1)*batch_size ]
current_batch_size = len(batch_tensor)
feed_dict[placeholder] = tensor[ batch_idx*batch_size : (batch_idx+1)*batch_size ]
tmp = session.run(tensors, feed_dict=feed_dict)
res = [ r + t * current_batch_size for (r, t) in zip(res, tmp) ]
return [ r / float(total_size) for r in res ]
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.01)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.01, shape=shape)
return tf.Variable(initial)
def conv2d(input, in_features, out_features, kernel_size, with_bias=False):
W = weight_variable([ kernel_size, kernel_size, in_features, out_features ])
conv = tf.nn.conv2d(input, W, [ 1, 1, 1, 1 ], padding='SAME')
if with_bias:
return conv + bias_variable([ out_features ])
return conv
def batch_activ_conv(current, in_features, out_features, kernel_size, is_training, keep_prob):
current = tf.contrib.layers.batch_norm(current, scale=True, is_training=is_training, updates_collections=None)
current = tf.nn.relu(current)
current = conv2d(current, in_features, out_features, kernel_size)
current = tf.nn.dropout(current, keep_prob)
return current
def block(input, layers, in_features, growth, is_training, keep_prob):
current = input
features = in_features
for idx in range(layers):
tmp = batch_activ_conv(current, features, growth, 3, is_training, keep_prob)
current = tf.concat((current, tmp),3)
features += growth
return current, features
def avg_pool(input, s):
return tf.nn.avg_pool(input, [ 1, s, s, 1 ], [1, s, s, 1 ], 'VALID')
def run_model(data, image_dim, label_count, depth):
weight_decay = 1e-4
layers = int((depth - 4) / 3)
xs = tf.placeholder("float", shape=[None, image_dim])
ys = tf.placeholder("float", shape=[None, label_count])
lr = tf.placeholder("float", shape=[])
keep_prob = tf.placeholder(tf.float32)
is_training = tf.placeholder("bool", shape=[])
current = tf.reshape(xs, [ -1, 32, 32, 3 ])
current = conv2d(current, 3, 16, 3)
current, features = block(current, layers, 16, 12, is_training, keep_prob)
current = batch_activ_conv(current, features, features, 1, is_training, keep_prob)
current = avg_pool(current, 2)
current, features = block(current, layers, features, 12, is_training, keep_prob)
current = batch_activ_conv(current, features, features, 1, is_training, keep_prob)
current = avg_pool(current, 2)
current, features = block(current, layers, features, 12, is_training, keep_prob)
current = tf.contrib.layers.batch_norm(current, scale=True, is_training=is_training, updates_collections=None)
current = tf.nn.relu(current)
current = avg_pool(current, 8)
final_dim = features
current = tf.reshape(current, [ -1, final_dim ])
Wfc = weight_variable([ final_dim, label_count ])
bfc = bias_variable([ label_count ])
ys_ = tf.nn.softmax( tf.matmul(current, Wfc) + bfc )
cross_entropy = -tf.reduce_mean(ys * tf.log(ys_ + 1e-12))
l2 = tf.add_n([tf.nn.l2_loss(var) for var in tf.trainable_variables()])
train_step = tf.train.MomentumOptimizer(lr, 0.9, use_nesterov=True).minimize(cross_entropy + l2 * weight_decay)
correct_prediction = tf.equal(tf.argmax(ys_, 1), tf.argmax(ys, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
parm_dict={}
for k in tf.global_variables():
if k not in tf.contrib.framework.get_variables_by_suffix('Momentum'):
parm_dict[k.name[:-2]] = k
session = tf.InteractiveSession()
batch_size = 64
learning_rate = 0.1
session.run(tf.global_variables_initializer())
saver = tf.train.Saver(parm_dict)
train_data, train_labels = data['train_data'], data['train_labels']
batch_count = int(len(train_data) / batch_size)
batches_data = np.split(train_data[:batch_count * batch_size], batch_count)
batches_labels = np.split(train_labels[:batch_count * batch_size], batch_count)
print ("Batch per epoch: ", batch_count)
saver.restore(session,'inq16100s.ckpt')
test_results = run_in_batch_avg(session, [ cross_entropy, accuracy ], [ xs, ys ],
feed_dict = { xs: data['test_data'], ys: data['test_labels'], is_training: False, keep_prob: 1. })
print(test_results)
# if test_results[1]>0.93: saver.save(session, 'densenetest_%d.ckpt' % test_results)
data_dir = 'data'
image_size = 32
image_dim = image_size * image_size * 3
meta = unpickle(data_dir + '/batches.meta')
label_names = meta['label_names']
label_count = len(label_names)
train_files = [ 'data_batch_%d' % d for d in range(1, 6) ]
train_data, train_labels = load_data(train_files, data_dir, label_count)
pi = np.random.permutation(len(train_data))
train_data, train_labels = train_data[pi], train_labels[pi]
test_data, test_labels = load_data([ 'test_batch' ], data_dir, label_count)
print ("Train:", np.shape(train_data), np.shape(train_labels))
print ("Test:", np.shape(test_data), np.shape(test_labels))
data = { 'train_data': train_data,
'train_labels': train_labels,
'test_data': test_data,
'test_labels': test_labels }
run_model(data, image_dim, label_count, 40)