# encoding=utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
myGraph = tf.Graph()
with myGraph.as_default():
with tf.name_scope('inputs'):
x_raw = tf.placeholder(tf.float32, shape=[None, 784])
y = tf.placeholder(tf.float32, shape=[None, 10])
with tf.name_scope('conv-layer1'):
x = tf.reshape(x_raw, shape=[-1,28,28,1])
W_conv1 = weight_variable([5,5,1,32])
b_conv1 = bias_variable([32])
l_conv1 = tf.nn.relu(tf.nn.conv2d(x,W_conv1, strides=[1,1,1,1],padding='SAME') + b_conv1)
l_pool1 = tf.nn.max_pool(l_conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
tf.summary.image('x_input', x, max_outputs=10)
tf.summary.histogram('W_conv1', W_conv1)
tf.summary.histogram('b_conv1', b_conv1)
with tf.name_scope('conv-layer2'):
W_conv2 = weight_variable([5,5,32,64])
b_conv2 = bias_variable([64])
l_conv2 = tf.nn.relu(tf.nn.conv2d(l_pool1, W_conv2, strides=[1,1,1,1], padding='SAME')+b_conv2)
l_pool2 = tf.nn.max_pool(l_conv2, ksize=[1,2,2,1],strides=[1,2,2,1], padding='SAME')
tf.summary.histogram('W_conv2', W_conv2)
tf.summary.histogram('b_conv2', b_conv2)
with tf.name_scope('fc1'):
W_fc1 = weight_variable([64*7*7, 1024])
b_fc1 = bias_variable([1024])
l_pool2_flat = tf.reshape(l_pool2, [-1, 64*7*7])
l_fc1 = tf.nn.relu(tf.matmul(l_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
l_fc1_drop = tf.nn.dropout(l_fc1, keep_prob)
tf.summary.histogram('W_fc1', W_fc1)
tf.summary.histogram('b_fc1', b_fc1)
with tf.name_scope('fc2'):
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(l_fc1_drop, W_fc2) + b_fc2
tf.summary.histogram('W_fc1', W_fc1)
tf.summary.histogram('b_fc1', b_fc1)
with tf.name_scope('train'):
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_conv, labels=y))
train_step = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('loss', cross_entropy)
tf.summary.scalar('accuracy', accuracy)
with tf.Session(graph=myGraph) as sess:
sess.run(tf.global_variables_initializer())
merged = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter('mnist-tensorboard/', graph=sess.graph)
for i in range(1001):
batch = mnist.train.next_batch(50)
sess.run(train_step, feed_dict={x_raw:batch[0], y:batch[1], keep_prob:0.7})
if i%100 == 0:
result, acc = sess.run([merged, accuracy], feed_dict={x_raw:batch[0], y:batch[1], keep_prob: 1.})
print("Iter " + str(i) + ", Training Accuracy= " + "{:.5f}".format(acc))
summary_writer.add_summary(result, i)
print("Optimization Finished!")
test_accuracy = sess.run(accuracy, feed_dict={x_raw: mnist.test.images[:256],
y: mnist.test.labels[:256], keep_prob: 1.})
print("Testing Accuracy:", test_accuracy)
from __future__ import print_function
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
learning_rate = 0.001
training_iters = 200000
batch_size = 128
display_step = 10
n_input = 784
n_classes = 10
dropout = 0.75
with tf.name_scope('inputs'):
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32)
def myconv2d(x, W, b, strides=1):
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def maxpool2d(x, k=2):
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')
def conv_net(x, weights, biases, dropout):
# 输入-1代表图片数量不定 , 28*28图片，1通道灰色图
with tf.name_scope('x'):
x = tf.reshape(x, shape=[-1, 28, 28, 1])
############# 第一层卷积 #####################
with tf.name_scope('conv1'):
conv1 = myconv2d(x, weights['wc1'], biases['bc1'])
conv1 = maxpool2d(conv1, k=2)
############# 第二层卷积 #####################
with tf.name_scope('conv2'):
conv2 = myconv2d(conv1, weights['wc2'], biases['bc2'])
conv2 = maxpool2d(conv2, k=2)
############# 第三层全连接 ###################
with tf.name_scope('fc1'):
fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
fc1 = tf.nn.dropout(fc1, dropout)
############# 第四层全连接 ###################
with tf.name_scope('fc2'):
out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
return out
weights = {
# 5x5 conv, 1 input, 32 outputs, 第一层卷积结果14*14*32
'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
# 5x5 conv, 32 inputs, 64 outputs, 第二层卷积结果7*7*64
'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
# fully connected, 7*7*64 inputs, 1024 outputs
'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),
# 1024 inputs, 10 outputs (class prediction)
'out': tf.Variable(tf.random_normal([1024, n_classes]))
}
biases = {
# xx通道输出，对应xx偏置量
'bc1': tf.Variable(tf.random_normal([32])),
'bc2': tf.Variable(tf.random_normal([64])),
'bd1': tf.Variable(tf.random_normal([1024])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
predict = conv_net(x, weights, biases, keep_prob)
############# 逻辑回归分类 #############
with tf.name_scope('cost'):
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predict, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
tf.summary.scalar('cost', cost)
############# 预测 #####################
with tf.name_scope('train'):
correct_pred = tf.equal(tf.argmax(predict, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
tf.summary.scalar('accuracy', accuracy)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
step = 1
merged =tf.summary.merge_all()
writer = tf.summary.FileWriter('tmp-logs/', sess.graph)
############# 训练 #####################
while step * batch_size < training_iters:
batch_x, batch_y = mnist.train.next_batch(batch_size)
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})
if step % display_step == 0:
result, loss, acc = sess.run([merged, cost, accuracy],
feed_dict={x: batch_x, y: batch_y, keep_prob: 1.})
print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
"{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc))
writer.add_summary(result, step)
step += 1
print("Optimization Finished!")
test_accuracy = sess.run(accuracy, feed_dict={x: mnist.test.images[:256],
y: mnist.test.labels[:256], keep_prob: 1.})
print("Testing Accuracy:", test_accuracy)
