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p256_cifar10.py
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import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import _pickle as pickle
import time
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
def unpickle_(filename):
with open(filename, 'rb') as f:
d = pickle.load(f, encoding='latinl')
return d
def unpickle(file):
import pickle
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='bytes')
return dict
def onehot(labels):
'''one-hot 编码'''
n_sample = len(labels)
n_class = max(labels) + 1
onehot_labels = np.zeros((n_sample, n_class))
onehot_labels[np.arange(n_sample), labels] = 1
return onehot_labels
data1 = unpickle('data/cifar10-dataset/data_batch_1')
data2 = unpickle('data/cifar10-dataset/data_batch_2')
data3 = unpickle('data/cifar10-dataset/data_batch_3')
data4 = unpickle('data/cifar10-dataset/data_batch_4')
data5 = unpickle('data/cifar10-dataset/data_batch_5')
X_train = np.concatenate((data1[b'data'], data2[b'data'], data3[b'data'], data4[b'data'], data5[b'data']), axis=0)
y_train = np.concatenate((data1[b'labels'], data2[b'labels'], data3[b'labels'], data4[b'labels'], data5[b'labels']), axis=0)
y_train = onehot(y_train)
test = unpickle('data/cifar10-dataset/test_batch')
X_test = test[b'data'][:5000, :]
y_test = onehot(test[b'labels'])[:5000, :]
print("Training dataset shape:", X_train.shape)
print("Training labels shape:", y_train.shape)
print("Testing dataset shape:", X_test.shape)
print("Testing labels shape:", y_test.shape)
with tf.device('/cpu:0'):
learning_rate = 1e-3
training_iters = 200
batch_size = 50
display_step = 5
n_features = 3072 # 32*32*3
n_classes = 10
n_fc1 = 384
n_fc2 = 192
x = tf.placeholder(tf.float32, [None, n_features])
y = tf.placeholder(tf.float32, [None, n_classes])
W_conv = {
'conv1': tf.Variable(tf.truncated_normal([5, 5, 3, 32], stddev=0.0001)),
'conv2': tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.01)),
'fc1': tf.Variable(tf.truncated_normal([8*8*64, n_fc1], stddev=0.1)),
'fc2': tf.Variable(tf.truncated_normal([n_fc1, n_fc2], stddev=0.1)),
'fc3': tf.Variable(tf.truncated_normal([n_fc2, n_classes], stddev=0.1))
}
b_conv = {
'conv1': tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[32])),
'conv2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[64])),
'fc1': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_fc1])),
'fc2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_fc2])),
'fc3': tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[n_classes]))
}
x_image = tf.reshape(x, [-1, 32, 32, 3])
#卷积层
conv1= tf.nn.conv2d(x_image, W_conv['conv1'], strides=[1, 1, 1, 1], padding='SAME')
conv1 = tf.nn.bias_add(conv1, b_conv['conv1'])
conv1 = tf.nn.relu(conv1)
# 池化层 1
pool1 = tf.nn.avg_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
# LRN层，Local Rsponse Normalization
norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)
# 卷积层 2
conv2 = tf.nn.conv2d(norm1, W_conv['conv2'], strides=[1, 1, 1, 1], padding='SAME')
conv2 = tf.nn.bias_add(conv2, b_conv['conv2'])
conv2 = tf.nn.relu(conv2)
# LRN层，Local Response Normalization
norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)
# 池化层 2
pool2 = tf.nn.avg_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
reshape = tf.reshape(pool2, [-1, 8*8*64])
fc1 = tf.add(tf.matmul(reshape, W_conv['fc1']), b_conv['fc1'])
fc1 = tf.nn.relu(fc1)
# 全连接层 2
fc2 = tf.add(tf.matmul(fc1, W_conv['fc2']), b_conv['fc2'])
fc2 = tf.nn.relu(fc2)
# 全连接层 3， 即分类层
fc3 = tf.nn.softmax(tf.add(tf.matmul(fc2, W_conv['fc3']), b_conv['fc3']))
# 定义损失
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=fc3, labels=y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
correct_pred = tf.equal(tf.argmax(fc3, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
c = []
total_batch = int(X_train.shape[0] /batch_size)
#for i in range(trainning_iters):
start_time = time.time()
for i in range(200):
for batch in range(total_batch):
batch_x = X_train[batch*batch_size: (batch+1)*batch_size, :]
batch_y = y_train[batch*batch_size: (batch+1)*batch_size, :]
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})
print(acc)
c.append(acc)
end_time = time.time()
print("------%d onpech is finished ---------"% i)
print("Optimization Finished!")
#Test
test_acc = sess.run(accuracy, feed_dict={x: X_test, y: y_test})
print("Testing Accuracy:", test_acc)
plt.plot(c)
plt.xlabel("Iter")
plt.ylabel("Cost")
plt.title("lr=%f, ti=%d, bs=%d, acc=%f" % (learning_rate, training_iters, batch_size, test_acc))
plt.tight_layout()
plt.savefig('cnn-tf-cifar10-%s.png' % test_acc, dpi=200)