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eval_v1.py
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eval_v1.py
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#conv1 conv2 pool fc1 fc2
from PIL import Image
import numpy as np
import tensorflow as tf
import cv2
import time
NUM_CHANNELS = 1
NUM_LABELS = 10
CONV1_DEEP = 32
CONV1_SIZE = 3
CONV2_DEEP = 32
CONV2_SIZE = 3
FC_SIZE = 128
batch_size = 8
learning_rate_base = 0.0001
learning_rate_decay = 0.99
regularaztion_rate = 0.0001
training_steps = 30000
moving_average_decay = 0.99
print('-' * 30)
print('Loading train data and Initlizing CNN Network')
print('-' * 30)
filename_queue = tf.train.string_input_producer([r'C:\Users\lisixu\Desktop\project_rebulid\train.tfrecords'])
reader = tf.TFRecordReader()
_,serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features ={
'label': tf.FixedLenFeature([10], tf.int64),
'image':tf.FixedLenFeature([],tf.string)
})
img = tf.decode_raw(features['image'],tf.uint8)
img = tf.reshape(img,[3072])
img = tf.cast(img,tf.float32)*(1.0/255.0)
label = tf.cast(features['label'],tf.float32)
img_batch,label_batch = tf.train.shuffle_batch([img,label],batch_size=batch_size,capacity= 216,min_after_dequeue=200)
def inference(input_tensor, train, regularizer):
with tf.variable_scope('layer1-conv1',reuse=tf.AUTO_REUSE):
conv1_weights = tf.get_variable("weight", [CONV1_SIZE, CONV1_SIZE, NUM_CHANNELS, CONV1_DEEP],
initializer=tf.truncated_normal_initializer(stddev=0.1))
conv1_biases = tf.get_variable("bias", [CONV1_DEEP], initializer=tf.constant_initializer(0.0))
tf.summary.histogram('layer1-conv1-weights', conv1_weights)
tf.summary.histogram('layer1-conv1-biases', conv1_biases)
conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')
relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))
pass
with tf.variable_scope('layer2-conv2',reuse=tf.AUTO_REUSE):
conv2_weights = tf.get_variable("weight", [CONV2_SIZE, CONV2_SIZE, CONV1_DEEP, CONV2_DEEP],
initializer=tf.truncated_normal_initializer(stddev=0.1))
conv2_biases = tf.get_variable("bias", [CONV2_DEEP], initializer=tf.constant_initializer(0.0))
tf.summary.histogram('layer2-conv2-weights', conv2_weights)
tf.summary.histogram('layer2-conv2-biases', conv2_biases)
conv2 = tf.nn.conv2d(relu1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')
relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))
pass
with tf.variable_scope('layer3-pool'):
pool = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 1, 1, 1], padding='SAME')
pass
pool_shape = pool.get_shape().as_list()
nodes = pool_shape[1] * pool_shape[2] * pool_shape[3]
reshaped = tf.reshape(pool, [pool_shape[0], nodes])
with tf.variable_scope('layer4-fc1',reuse=tf.AUTO_REUSE):
fc1_weights = tf.get_variable("weights", [nodes, FC_SIZE],
initializer=tf.truncated_normal_initializer(stddev=0.1))
if regularizer != None:
tf.add_to_collection('losses', regularizer(fc1_weights))
pass
fc1_biases = tf.get_variable("bias", [FC_SIZE], initializer=tf.constant_initializer(0.1))
tf.summary.histogram('layer4-fc1-weights', fc1_weights)
tf.summary.histogram('layer4-fc1-biases', fc1_biases)
fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)
if train: fc1 = tf.nn.dropout(fc1, 0.5)
pass
with tf.variable_scope('layer4-fc2',reuse=tf.AUTO_REUSE):
fc2_weights = tf.get_variable("weights", [FC_SIZE, NUM_LABELS],
initializer=tf.truncated_normal_initializer(stddev=0.1))
if regularizer != None:
tf.add_to_collection('losses', regularizer(fc2_weights))
pass
fc2_biases = tf.get_variable("bias", [NUM_LABELS], initializer=tf.constant_initializer(0.1))
tf.summary.histogram('layer4-fc2-weights', fc2_weights)
tf.summary.histogram('layer4-fc2-biases', fc2_biases)
logit = tf.matmul(fc1, fc2_weights) + fc2_biases
pass
return logit
x = tf.placeholder(tf.float32, [8, 48, 64,1], name='x-input')
y_ = tf.placeholder(tf.float32, [8, 10], name='y-input')
regularizer = tf.contrib.layers.l2_regularizer(regularaztion_rate)
y = inference(x, train=True, regularizer=regularizer)
global_step = tf.Variable(0, trainable=False)
variable_averages = tf.train.ExponentialMovingAverage(moving_average_decay, global_step)
variable_averages_op = variable_averages.apply(tf.trainable_variables())
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
cross_entropy_mean = tf.reduce_mean(cross_entropy)
loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))
tf.summary.scalar('cross_entropy_mean', cross_entropy_mean)
tf.summary.scalar('loss', loss)
learning_rate = tf.train.exponential_decay(learning_rate_base, global_step, 125, learning_rate_decay)
tf.summary.scalar('learning_rate', learning_rate)
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss, global_step=global_step)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)
with tf.control_dependencies([train_step, variable_averages_op]):
train_op = tf.no_op(name='train')
pass
saver = tf.train.Saver()
merged = tf.summary.merge_all()
cap = cv2.VideoCapture(0)
with tf.Session() as sess:
module_file = tf.train.latest_checkpoint('C:/Users/Vitamin/Desktop/YYG_Final/Our_trin_set/train2')
saver.restore(sess, module_file)
while(1):
data_temp = []
ret, frame = cap.read()
cv2.imshow("capture", frame)
Grayimg = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
data = np.matrix(Grayimg)
data = data.astype(np.float32)
data = cv2.resize(data, (64,48), interpolation=cv2.INTER_AREA)
data_temp.append(data)
data_temp.append(data)
data_temp = np.reshape(data_temp,(2,1,48,64,1))
y_predict = sess.run([y],feed_dict={x: data_temp[0]})
print(np.where(y_predict==np.max(y_predict)))
#print("Current number:%d" % tf.argmax(y_predict))
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
pass