TensorFlow入門之訓練mnist資料集
阿新 • • 發佈:2019-01-04
import sys,os import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) sess = tf.InteractiveSession() x = tf.placeholder('float',shape=[None, 784]) y_ = tf.placeholder('float', shape=[None, 10]) def weight_varialbe(shape): init_val = tf.truncated_normal( shape, stddev=0.1 ) return tf.Variable(init_val) def bias_variable(shape): init_val = tf.constant( 0.1, shape=shape ) return tf.Variable(init_val) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME') def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME') W_conv1 = weight_varialbe( [5,5,1,32] ) b_conv1 = bias_variable( [32] ) x_image = tf.reshape(x,[-1,28,28,1] ) h_conv1 = tf.nn.relu( conv2d(x_image, W_conv1) + b_conv1 ) h_pool1 = max_pool_2x2(h_conv1) # layer2 W_conv2 = weight_varialbe( [5,5,32,64] ) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu( conv2d(h_pool1, W_conv2) + b_conv2 ) h_pool2 = max_pool_2x2(h_conv2) W_fc1 = weight_varialbe( [7*7*64, 1024] ) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape( h_pool2, [-1, 7*7*64] ) h_fc1 = tf.nn.relu( tf.matmul( h_pool2_flat, W_fc1 ) + b_fc1 ) keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob ) W_fc2 = weight_varialbe([1024,10]) b_fc2 = bias_variable([10]) y_conv = tf.nn.softmax( tf.matmul(h_fc1_drop, W_fc2) + b_fc2 ) cross_entropy = -tf.reduce_sum( y_ * tf.log(y_conv) ) train_step = tf.train.AdamOptimizer(1e-4).minimize( cross_entropy ) correct_perd = tf.equal( tf.argmax(y_conv, 1), tf.argmax(y_,1) ) accuracy = tf.reduce_mean( tf.cast( correct_perd, "float" ) ) sess.run(tf.initialize_all_variables()) for i in range(2000): batch = mnist.train.next_batch(50) train_acc = accuracy.eval( feed_dict={x:batch[0], y_:batch[1], keep_prob:1.0} ) print('setp %d accuracy=%g'%( i, train_acc )) train_step.run(feed_dict={ x:batch[0], y_:batch[1], keep_prob:0.5 } ) print("test accuracy %g"%(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))) if __name__ == "__main__": #counter() #mul_sensor() #feed_sensor() pass
最終在測試集上驗證準確度為0.977。