訓練分類器，並將生成結果分類
阿新 • • 發佈：2018-12-16
import tensorflow as tf import matplotlib.pyplot as plt import numpy as np import os np.set_printoptions(threshold=np.inf) ####################################################################### # 讀取資料和標籤 finaldata = np.load("data/單張999分類訓練集.npy") # 316, 999 finalval = np.load("data/單張999分類測試集.npy") # 316, 999 np.random.seed(1000) np.random.shuffle(finaldata) np.random.shuffle(finalval) ######################################################################## # 超引數 batch_size = 100 learning_rate = 0.001 epochs = 10000 n_class = 999 ####################################################################### # 用numpy進行one_hot編碼 def np_one_hot(labels): n_labels = np.max(labels) + 1 one_hot = np.eye(n_labels)[labels] return one_hot ###################################################################### # 將資料與標籤分離，並對標籤進行one_hot編碼 def input_data(finaldata): data = [] label = [] for i in range(len(finaldata)): data.append(finaldata[i][0]) label.append(finaldata[i][1]) data = np.array(data) data = data.reshape(-1,16,16,1) label = np.array(label) label = np_one_hot(label) return data, label # 訓練集 data, label = input_data(finaldata) # 測試集 data_val, label_val = input_data(finalval) ########################################################################### # 排除管徑對分類的影響 # ones = np.ones((len(data),10,16,1)) # data[:,6::,:,:] = ones # val_ones = np.ones((len(data_val),10,16,1)) # data_val[:,6::,:,:] = val_ones ########################################################################### # 得到batch個數據 # 有資料有label時 def get_batch(inputs=None, labels=None, batch_size=None, shuffle=True): assert len(inputs) == len(labels) indices = np.arange(len(inputs)) if shuffle: np.random.shuffle(indices) # start_idx為batch_size個數 for start_idx in range(0, len(inputs) -batch_size + 1, batch_size): if shuffle: excerpt = indices[start_idx:start_idx + batch_size] else: excerpt = indices[start_idx:start_idx + batch_size] yield inputs[excerpt] , labels[excerpt] ##################################################################### def inference(images): # conv1 16x16x1->16x16x96 with tf.variable_scope("conv1") as scope: weights = tf.get_variable("weights", shape = [3, 3, 1, 96], dtype = tf.float32, initializer = tf.truncated_normal_initializer(stddev=0.05,dtype=tf.float32)) biases = tf.get_variable("biases", shape = [96], dtype = tf.float32, initializer = tf.constant_initializer(0.0)) conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding="SAME") pre_activation = tf.nn.bias_add(conv, biases) conv1 = tf.nn.relu(pre_activation, name=scope.name) # pool1 and norm1 16x16x96->8x8x96 with tf.variable_scope("pooling1_lrn") as scope: pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2 ,1], padding="SAME", name="pooing1") norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001/9.0, beta=0.75, name="norm1") # conv2 8x8x96->8x8x64 with tf.variable_scope("conv2") as scope: weights = tf.get_variable("weights", shape = [3, 3, 96, 64], dtype = tf.float32, initializer = tf.truncated_normal_initializer(stddev=0.05,dtype=tf.float32)) biases = tf.get_variable('biases', shape=[64], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) conv = tf.nn.conv2d(norm1, weights, strides=[1,1,1,1],padding='SAME') pre_activation = tf.nn.bias_add(conv, biases) conv2 = tf.nn.relu(pre_activation, name='conv2') #pool2 and norm2 8x8x64->4x4x64 with tf.variable_scope('pooling2_lrn') as scope: norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001/9.0, beta=0.75,name='norm2') pool2 = tf.nn.max_pool(norm2, ksize=[1,3,3,1], strides=[1,1,1,1], padding='SAME',name='pooling2') print(pool2.shape) # fc3 with tf.variable_scope("fc3") as scope: reshape = tf.reshape(pool2, shape=[-1, 8*8*64]) print(reshape.shape) dim = reshape.get_shape()[1].value weights = tf.get_variable("weights", shape=[dim, 512], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.004,dtype=tf.float32)) biases = tf.get_variable("biases", shape=[512], dtype = tf.float32, initializer = tf.constant_initializer(0.1) ) fc3 = tf.nn.relu(tf.matmul(reshape,weights) + biases, name=scope.name) # fc4 with tf.variable_scope('fc4') as scope: weights = tf.get_variable('weights', shape=[512,256], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.004,dtype=tf.float32)) biases = tf.get_variable('biases', shape=[256], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) local4 = tf.nn.relu(tf.matmul(fc3, weights) + biases, name='fc4') # softmax with tf.variable_scope('softmax_linear') as scope: weights = tf.get_variable('softmax_linear', shape=[256, n_class], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.004,dtype=tf.float32)) biases = tf.get_variable('biases', shape=[n_class], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear') return softmax_linear ###################################################################### # loss函式 def losses(logits, labels): with tf.variable_scope('loss') as scope: # to use this loss fuction, one-hot encoding is needed! cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels, name='xentropy_per_example') loss = tf.reduce_mean(cross_entropy, name='loss') if not os.path.exists('loss'): os.makedirs('loss') tf.summary.scalar(scope.name+'/loss', loss) return loss ################################################################################### images = tf.placeholder(tf.float32,[None, 16, 16, 1]) labels = tf.placeholder(tf.float32,[None, n_class]) #################################################################################### # 訓練模型 def train(): my_global_step = tf.Variable(0, name='global_step', trainable=False) logits = inference(images) prediction = tf.nn.softmax(logits) correct_prediction = tf.equal(tf.argmax(prediction,1),tf.argmax(labels,1)) # loss值 loss = losses(logits, labels) # 準確率 accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) train_op = tf.train.AdamOptimizer(learning_rate).minimize(loss, global_step = my_global_step) saver = tf.train.Saver(tf.global_variables()) summary_op = tf.summary.merge_all() sess = tf.Session() sess.run(tf.global_variables_initializer()) if not os.path.exists('logs'): os.makedirs('logs') summary_writer = tf.summary.FileWriter('logs',sess.graph) for e in range(epochs): for data_batch,label_batch in get_batch(data,label,batch_size): _, loss_value ,train_accuracy_value= sess.run([train_op, loss, accuracy], feed_dict={images:data_batch,labels:label_batch}) # 測試機準確率 test_accuraccy_value = sess.run(accuracy,feed_dict={images:data_val,labels:label_val}) print("第%d個epoch: , loss: %.4f, train_accuracy: %.4f, test_accuracy: %.4f" % (e, loss_value, train_accuracy_value,test_accuraccy_value)) if e % 3 ==0: summary_str = sess.run(summary_op, feed_dict={images:data_batch, labels:label_batch}) summary_writer.add_summary(summary_str, e) saver.save(sess,"classfier_checkpoints/model.ckpt", global_step=e) sess.close() def evaluate(): gen_data = np.load("data/model60.npy") gen_data = gen_data.reshape(-1, 16, 16, 1) # gen_data = gen_data[0:5000,:,:,:] logits = inference(images) # softmax變為概率,若預測正確，其中最大值接近1 prediction = tf.nn.softmax(logits) # 預測的最大值的索引，也即使哪一類 prediction_max = tf.argmax(prediction,1) # 真實的label label_max = tf.argmax(labels,1) # 為布林值 correct_prediction = tf.equal(tf.argmax(prediction,1),tf.argmax(labels,1)) # 將布林值變為小數 accuracy_arr = tf.cast(correct_prediction, tf.float32) # 取這一批的平均值 # accuracy_arr = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) with tf.Session() as sess: saver = tf.train.Saver(tf.global_variables()) saver.restore(sess, tf.train.latest_checkpoint("classfier_checkpoints")) ############################################################################################# # for test_data, test_label in get_batch(data_val, label_val, 1): # prediction_index,label_index,acc = sess.run([prediction_max,label_max,accuracy_arr],feed_dict={images:test_data,labels:test_label}) # prob = acc[0] # print("prob: %.4f " % prob," pre_index:",prediction_index," label_index:",label_index) ########################################################################################## # 預測生成的資料的標籤，並將標籤與生成資料合併 concat_label = [] pre_index_list = [] for i in range(len(gen_data)): single_data_val = gen_data[i].reshape(-1,16,16,1) pre_index = sess.run(prediction_max, feed_dict={images:single_data_val}) pre_index = pre_index.tolist()[0] pre_index_list.append(pre_index) concat_label.append((gen_data[i],pre_index)) print(len(pre_index_list)) concat_label = np.array(concat_label) print(concat_label.shape) np.save("data/gen.npy",concat_label) ########################################################################################## # train() ################################################################################ # 視覺化每一類圖片 gen = np.load("data/gen.npy") def plot_image(finaldata,pre_label): for i in range(len(finaldata)): data = finaldata[i][0] label = finaldata[i][1] if label == pre_label: print(label) data = data.reshape(16,16) zhu_x = data[0] zuo_x = data[1] you_x = data[2] zhu_y = data[3] zuo_y = data[4] you_y = data[5] zhu_diam = data[6][0] zuo_diam = data[7][0] you_diam = data[8][0] plt.plot(zhu_x, zhu_y, color="red", linewidth=20*zhu_diam) plt.plot(zuo_x, zuo_y, color="green", linewidth=20*zuo_diam) plt.plot(you_x, you_y, color="blue", linewidth=20*you_diam) plt.xlim(0,1) plt.ylim(0,1) plt.xticks(np.arange(0,1,0.1)) plt.yticks(np.arange(0,1,0.1)) plt.axis('off') # plt.show() if not os.path.exists("label"): os.makedirs("label") plt.savefig("label/label%d_%d.jpg" % (pre_label,i)) plt.close() # plot_image(gen,2) plot_image(finaldata,2) ############################################################################# # train() # evaluate()