基于tensorflow卷积神经网络+CK数据库的表情识别算法python实现源码.zip

import tensorflow as tf import numpy as np import decodetfrecord as df from tensorflow.python.framework import graph_util def bn_parmeter(input): scale = tf.Variable(tf.ones([input.get_shape()[-1]]), dtype=tf.float32) offset = tf.Variable(tf.zeros([input.get_shape()[-1]]), dtype=tf.float32) batch_mean = tf.Variable(tf.zeros([input.get_shape()[-1]]), dtype=tf.float32) batch_var = tf.Variable(tf.zeros([input.get_shape()[-1]]), dtype=tf.float32) batch_mean, batch_var =tf.nn.moments(x=input, axes=[0,1,2]) return scale, offset, batch_mean, batch_var def conv_network(height, width, channel): conv1_feature = 64 conv2_feature = 64 conv3_feature = 64 max_pool1_size = 2 max_pool2_size = 2 max_pool3_size = 2 full_connect_feature = 256 keep_prob = 0.99 x = tf.placeholder(tf.float32, shape=[None, height, width, channel], name='inputdata') y = tf.placeholder(tf.uint8, shape=[None, 6], name='label') step = tf.placeholder(tf.float32, name='coefficient') # 权重系数初始化函数 def weight_vaiable(shape, name='weight'): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial, name=name, dtype=tf.float32) # 初始化偏置 def bias_variable(shape, name='biases'): initial = tf.zeros(shape=shape, dtype=tf.float32) return tf.Variable(initial, name=name) # 卷积层函数 def conv2d(input, kernel): return tf.nn.conv2d(input, kernel, strides=[1, 1, 1, 1], padding='SAME') # 池化函数 def pool_max(input, max_pool_size): return tf.nn.max_pool(input, ksize=[1, max_pool_size, max_pool_size, 1], strides=[1, max_pool_size, max_pool_size, 1], padding='SAME', name='pool' ) # 全连接层函数 def fc(input, w, b): return tf.matmul(input, w) + b with tf.name_scope('conv1') as scope: kernel1 = weight_vaiable(shape=[5, 5, channel, conv1_feature]) bias1 = bias_variable(shape=[conv1_feature]) conv1 = tf.nn.bias_add(conv2d(input=x, kernel=kernel1), bias1) scale, offset, batch_mean, batch_var = bn_parmeter(conv1) bn_conv1 = tf.nn.batch_normalization(conv1, mean=batch_mean, variance=batch_var, offset=offset, scale= scale, variance_epsilon=0.0001 ) pool1 = pool_max(bn_conv1, max_pool1_size) with tf.name_scope('conv2') as scope: kernel2 = weight_vaiable(shape=[5, 5, conv1_feature, conv2_feature]) bias2 = bias_variable(shape=[conv2_feature]) conv2 = tf.nn.bias_add(conv2d(input=pool1, kernel=kernel2), bias2) scale, offset, batch_mean, batch_var = bn_parmeter(conv2) bn_conv2 = tf.nn.batch_normalization(conv2, mean=batch_mean, variance=batch_var, offset=offset, scale=scale, variance_epsilon=0.0001 ) pool2 = pool_max(bn_conv2, max_pool2_size) with tf.name_scope('conv3') as scope: kernel3 = weight_vaiable(shape=[3, 3, conv2_feature, conv3_feature]) bias3 = bias_variable(shape=[conv3_feature]) conv3 = tf.nn.bias_add(conv2d(input=pool2, kernel=kernel3), bias3) scale, offset, batch_mean, batch_var = bn_parmeter(conv3) bn_conv3 = tf.nn.batch_normalization(conv3, mean=batch_mean, variance=batch_var, offset=offset, scale=scale, variance_epsilon=0.0001 ) pool3 = pool_max(bn_conv3, max_pool3_size) final_conv_shape = pool3.shape.as_list() final_shape = final_conv_shape[1] * final_conv_shape[2] * final_conv_shape[3] flat_conv_out =tf.reshape(pool3, shape=[-1, final_shape]) with tf.name_scope('full_connection') as scope: weight_fc_1 = weight_vaiable(shape=[final_shape, full_connect_feature]) bias_fc1 = weight_vaiable(shape=[full_connect_feature]) fc1 = tf.nn.relu(fc(flat_conv_out, weight_fc_1, bias_fc1)) drop_out_1 = tf.nn.dropout(fc1, keep_prob=keep_prob) weight_fc_2 = weight_vaiable(shape=[full_connect_feature, 6]) bias_fc2 = weight_vaiable(shape=[6]) fc2 = tf.nn.relu(fc(drop_out_1, weight_fc_2, bias_fc2)) net_output = tf.nn.dropout(fc2, keep_prob=keep_prob) soft_max_output = tf.nn.softmax(net_output, name='output') cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=net_output)) train_step = tf.train.AdamOptimizer(step).minimize(cost) prediction_labels = tf.argmax(soft_max_output, axis=1, name='prediction') read_label = tf.argmax(y, axis=1) accuracy = tf.reduce_sum(tf.cast(tf.equal(prediction_labels, read_label), dtype=tf.float32))/192 return dict(x = x, y = y, cost = cost, accuracy = accuracy, keep_prob = keep_prob, train_step = train_step, step = step) def net_work(graph, width, height, channel, batch_size, num_epochs, pb_file, root='faceset.tfrecords'): batch_set, batch_label = df.single_read_decode(root, batch_size, height, width) v_set, v_label = df.single_read_decode(root, 192, height, width, process=1) init_step = 0.0001 init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) for i in range(num_epochs): train_set, train_label = sess.run([batch_set, batch_label]) sess.run(graph['train_step'], feed_dict={graph['x']: train_set, graph['y']: train_label, graph['step']: init_step * pow(0.9, (i/100))}) val_set, val_label = sess.run([v_set, v_label]) acc_temp = sess.run(graph['accuracy'], feed_dict={graph['x']: val_set, graph['y']: val_label}) print(u'准确率：', acc_temp) coord.request_stop() coord.join() output_graph_def = graph_util.convert_variables_to_constants( sess, tf.get_default_graph().as_graph_def(), ['prediction']) with tf.gfile.GFile(pb_file, 'wb') as f: f.write(output_graph_def.SerializeToString()) coord.request_stop() coord.join(threads) sess.close() def main(): Height = 120 Width = 120 Channel = 3 batch_size = 128 graph_cov = conv_network(height=Height, width=Width, channel=Channel) net_work(graph=graph_cov, width=Width, height=Height, batch_size=batch_size, num_epochs=2000, channel=3, pb_file='conv_net.pb') main()