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| import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import os
import cv2
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
"""------------------加载数据---------------------"""
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
trX = trX.reshape(-1, 28, 28, 1)
teX = teX.reshape(-1, 28, 28, 1)
"""------------------构建模型---------------------"""
X = tf.placeholder("float", [None, 28, 28, 1], name="X")
Y = tf.placeholder("float", [None, 10])
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.01))
w1 = init_weights([3, 3, 1, 32])
w2 = init_weights([3, 3, 32, 64])
w3 = init_weights([3, 3, 64, 128])
w4 = init_weights([128 * 4 * 4, 625])
w_o = init_weights([625, 10])
p_keep_conv = tf.placeholder("float", name="p_keep_conv")
p_keep_hidden = tf.placeholder("float", name="p_keep_hidden")
def create_model(X, w1, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden):
conv1 = tf.nn.conv2d(X, w1, strides=[1, 1, 1, 1], padding='SAME')
conv1_out = tf.nn.relu(conv1)
pool1 = tf.nn.max_pool(conv1_out, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
pool1_out = tf.nn.dropout(pool1, p_keep_conv)
conv2 = tf.nn.conv2d(pool1_out, w2, strides=[1, 1, 1, 1], padding='SAME')
conv2_out = tf.nn.relu(conv2)
pool2 = tf.nn.max_pool(conv2_out, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
pool2_out = tf.nn.dropout(pool2, p_keep_conv)
conv3 = tf.nn.conv2d(pool2_out, w3, strides=[1, 1, 1, 1], padding='SAME')
conv3_out = tf.nn.relu(conv3)
pool3 = tf.nn.max_pool(conv3_out, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
pool3 = tf.reshape(pool3, [-1, w4.get_shape().as_list()[0]])
pool3_out = tf.nn.dropout(pool3, p_keep_conv)
fully_layer = tf.matmul(pool3_out, w4)
fully_layer_out = tf.nn.relu(fully_layer)
fully_layer_out = tf.nn.dropout(fully_layer_out, p_keep_hidden)
out = tf.matmul(fully_layer_out, w_o)
return out
model = create_model(X, w1, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=model, labels=Y))
train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
predict_op = tf.argmax(model, 1, name="predict")
saver=tf.train.Saver()
ckpt_dir="./ckpt_dir"
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
"""------------------训练模型或者加载模型进行测试---------------------"""
train_batch_size = 128
test_batch_size = 256
epoches = 5
with tf.Session() as sess:
"""-------训练模型--------"""
tf.global_variables_initializer().run()
"""-----加载模型，用导入的图片进行测试--------"""
src = cv2.imread('./2.png')
src = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
dst = cv2.resize(src, (28, 28), interpolation=cv2.INTER_CUBIC)
picture = np.zeros((28, 28))
for i in range(0, 28):
for j in range(0, 28):
picture[i][j] = (255 - dst[i][j])
picture = picture.reshape(1, 28, 28, 1)
saver.restore(sess, ckpt_dir+"/model.ckpt-4")
predict_result = sess.run(predict_op, feed_dict={X: picture,
p_keep_conv: 1.0,
p_keep_hidden: 1.0})
print("你导入的图片是：", predict_result[0])
|