main1.py

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data #避免导入出错
from numpy import outer
fmnist = input_data.read_data_sets("data/",one_hot=True)


learning_rate = 0.001
training_iters = 200000
batch_size = 128
display_step = 10

n_input = 784  #28 x 28
n_classes = 10
dropout = 0.9# 全连接多设置大一些

#tf Graph input
x = tf.placeholder(tf.float32,[None,n_input])
y = tf.placeholder(tf.float32,[None,n_classes])
keep_prob = tf.placeholder(tf.float32)#drop(keep probability)


def conv2d(image,w,b):
    return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(image,w,strides=[1,1,1,1],padding='SAME'),b))
def max_pooling(image,k):
    return tf.nn.max_pool(image, ksize=[1,k,k,1], strides=[1,k,k,1], padding='SAME')

weights = {
    'wc1':tf.Variable(tf.random_normal([3,3,1,32],stddev=0.01)), #一律用最小的卷积核3x3，学习细节特征，标准差设置小一点，loss才正常，收敛很快，否则精确度上不去
    'wc2':tf.Variable(tf.random_normal([3,3,32,64],stddev=0.01)),
    'wc3':tf.Variable(tf.random_normal([3,3,64,128],stddev=0.01)),
    'wd1':tf.Variable(tf.random_normal([4*4*128,1024],stddev=0.01)),
    'wd2':tf.Variable(tf.random_normal([1024,1024],stddev=0.01)),
    'wd3':tf.Variable(tf.random_normal([1024,512],stddev=0.01)),
    'out':tf.Variable(tf.random_normal([512,n_classes],stddev=0.01))
}
biases = {
    'bc1':tf.Variable(tf.random_normal([32],stddev=0.01)),
    'bc2':tf.Variable(tf.random_normal([64],stddev=0.01)),
    'bc3':tf.Variable(tf.random_normal([128],stddev=0.01)),
    'bd1':tf.Variable(tf.random_normal([1024],stddev=0.01)),
    'bd2':tf.Variable(tf.random_normal([1024],stddev=0.01)),
    'bd3':tf.Variable(tf.random_normal([512],stddev=0.01)),
    'out':tf.Variable(tf.random_normal([n_classes],stddev=0.01))
}
def conv_net(_X,_weights,_biases,_dropout):
    _X = tf.reshape(_X,[-1,28,28,1])
    conv1 = conv2d(_X,_weights['wc1'],_biases['bc1'])
    conv1 = max_pooling(conv1, k = 2)
    conv1 = tf.nn.dropout(conv1, keep_prob=_dropout)

    conv2 = conv2d(conv1,_weights['wc2'],_biases['bc2'])
    conv2 = max_pooling(conv2, k=2)
    conv2 = tf.nn.dropout(conv2,keep_prob=_dropout)

    conv3 = conv2d(conv2,_weights['wc3'],_biases['bc3'])
    conv3 = max_pooling(conv3,k=2)
    conv3 = tf.nn.dropout(conv3,keep_prob=_dropout)

    dense1 = tf.reshape(conv3,[-1,_weights['wd1'].get_shape().as_list()[0]])
    dense1 = tf.nn.relu(tf.add(tf.matmul(dense1,_weights['wd1']),_biases['bd1']))
    dense2 = tf.nn.relu(tf.matmul(dense1,_weights['wd2'])+_biases['bd2'],name='fc2')
    dense3 = tf.nn.relu(tf.matmul(dense2,weights['wd3'])+_biases['bd3'],name='fc3')
    dense2 = tf.nn.dropout(dense3,_dropout)
    out = tf.add(tf.matmul(dense2,_weights['out']),_biases['out'])

    #print(out)
    return out

pred = conv_net(x, weights, biases, keep_prob)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=pred))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)


correct_pred = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))
init = tf.initialize_all_variables()

with tf.Session() as sess:
    sess.run(init)
    step = 1
    while step * batch_size<training_iters:
        batch_xs,batch_ys = fmnist.train.next_batch(batch_size)
        sess.run(optimizer,feed_dict = {x:batch_xs,y:batch_ys,keep_prob:dropout})
        if step %display_step==0:
            acc = sess.run(accuracy,feed_dict={x:batch_xs,y:batch_ys,keep_prob:1.})
            loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
            print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc))

        step += 1
    print("Optimization Finished!")
    print("Testing Accuracy:",sess.run(accuracy,feed_dict={x: fmnist.test.images[:256], y: fmnist.test.labels[:256], keep_prob: 1.}))