tensorflow cnn

2019-04-15 16:30发布

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#input_dataimport os import numpy as np import tensorflow as tf def get_files(filename): class_train = [] label_train = [] for train_class in os.listdir(filename): for pic in os.listdir(filename+train_class): class_train.append(filename+train_class+'/'+pic) label_train.append(train_class) temp = np.array([class_train,label_train]) temp = temp.transpose() #shuffle the samples np.random.shuffle(temp) #after transpose, images is in dimension 0 and label in dimension 1 image_list = list(temp[:,0]) label_list = list(temp[:,1]) label_list = [int(i) for i in label_list] #print(label_list) return image_list,label_list def get_batches(image, label, resize_w, resize_h, batch_size, capacity): # convert the list of images and labels to tensor image = tf.cast(image, tf.string) label = tf.cast(label, tf.int64) queue = tf.train.slice_input_producer([image, label]) label = queue[1] image_c = tf.read_file(queue[0]) image = tf.image.decode_jpeg(image_c, channels=3) # resize image = tf.image.resize_image_with_crop_or_pad(image, resize_w, resize_h) # (x - mean) / adjusted_stddev image = tf.image.per_image_standardization(image) image_batch, label_batch = tf.train.batch([image, label], batch_size=batch_size, num_threads=64, capacity=capacity) images_batch = tf.cast(image_batch, tf.float32) labels_batch = tf.reshape(label_batch, [batch_size]) return images_batch, labels_batch#modelimport os import numpy as np import tensorflow as tf def mmodel(images,batch_size): with tf.variable_scope('conv1') as scope: weights = tf.get_variable('weights', shape = [3,3,3, 16], dtype = tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32)) biases = tf.get_variable('biases', shape=[16], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) 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) with tf.variable_scope('pooling1_lrn') as scope: pool1 = tf.nn.max_pool(conv1, ksize=[1,2,2,1],strides=[1,2,2,1], padding='SAME', name='pooling1') norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001/9.0, beta=0.75,name='norm1') with tf.variable_scope('conv2') as scope: weights = tf.get_variable('weights', shape=[3,3,16,128], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32)) biases = tf.get_variable('biases', shape=[128], 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') 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=[1,2,2,1], strides=[1,1,1,1], padding='SAME',name='pooling2') with tf.variable_scope('local3') as scope: reshape = tf.reshape(pool2, shape=[batch_size, -1]) dim = reshape.get_shape()[1].value weights = tf.get_variable('weights', shape=[dim,4096], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32)) biases = tf.get_variable('biases', shape=[4096], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name) with tf.variable_scope('softmax_linear') as scope: weights = tf.get_variable('softmax_linear', shape=[4096, 2], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32)) biases = tf.get_variable('biases', shape=[2], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) softmax_linear = tf.add(tf.matmul(local3, weights), biases, name='softmax_linear') return softmax_linear def loss(logits,label_batches): cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,labels=label_batches) cost = tf.reduce_mean(cross_entropy) return cost def get_accuracy(logits,labels): acc = tf.nn.in_top_k(logits,labels,1) acc = tf.cast(acc,tf.float32) acc = tf.reduce_mean(acc) return acc def training(loss,lr): train_op = tf.train.AdamOptimizer(lr).minimize(loss) return train_op#trainimport os import tensorflow as tf import numpy as np import input_data import model def run_training(): data_dir = 'E:\data\dataset\man_woman_flower\' log_dir = 'E:\tensorflow\CNN_classifier_1\logs\man_woman\' image, label = input_data.get_files(data_dir) image_batches, label_batches = input_data.get_batches(image, label, 32, 32, 32, 20) print(image_batches.shape) p = model.mmodel(image_batches, 32) cost = model.loss(p, label_batches) train_op = model.training(cost, 0.001) acc = model.get_accuracy(p, label_batches) sess = tf.Session() init = tf.global_variables_initializer() sess.run(init) saver = tf.train.Saver() coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) try: max_acc = 0 for epoch in range(4000): for i in range(300): if coord.should_stop(): break tra_acc = 0 _, train_acc, train_loss = sess.run([train_op, acc, cost]) print("Iter:%d,loss:%.3f,accuracy:%.2f%%" %(epoch,train_loss, train_acc)) tra_acc += train_acc if max_acc < tra_acc: max_acc = tra_acc check = os.path.join(log_dir, "model.ckpt") saver.save(sess, check) except tf.errors.OutOfRangeError: print("Done!!!") finally: coord.request_stop() coord.join(threads) sess.close() run_training()
# evaluationimport numpy as np from PIL import Image import matplotlib.pyplot as plt import tensorflow as tf import model def get_one_image(img_dir): image = Image.open(img_dir) plt.imshow(image) image = image.resize([32, 32]) image_arr = np.array(image) return image_arr def test(test_file): log_dir = 'E:\tensorflow\CNN_classifier_1\logs\man_woman\' image_arr = get_one_image(test_file) with tf.Graph().as_default(): image = tf.cast(image_arr, tf.float32) image = tf.image.per_image_standardization(image) image = tf.reshape(image, [1, 32, 32, 3]) print(image.shape) p = model.mmodel(image, 1) logits = tf.nn.softmax(p) x = tf.placeholder(tf.float32, shape=[32, 32, 3]) saver = tf.train.Saver() with tf.Session() as sess: ckpt = tf.train.get_checkpoint_state(log_dir) if ckpt and ckpt.model_checkpoint_path: global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1] saver.restore(sess, ckpt.model_checkpoint_path) print('Loading success') else: print('No checkpoint') prediction = sess.run(logits, feed_dict={x: image_arr}) max_index = np.argmax(prediction) print(max_index) test("111.jpg")



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