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# -*- coding=UTF-8 -*-
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import os
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import tensorflow as tf
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import numpy as np
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import time
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from PIL import Image
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w = 64
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h = 64
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c = 3
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TOTAL_TYPE = 5
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path = 'D:/tensorflow/imgaes/'
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model_path='D:/tensorflow/saver/model.ckpt'
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def read_source():
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imgs,labels = [],[]
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for dir in os.listdir(path):
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idx = int(dir)
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folder = os.path.join(path,dir)
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zeros = np.zeros(TOTAL_TYPE)
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zeros[idx]=1
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print("folder :%s"%(folder))
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total = 0
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for f in os.listdir(folder):
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file = os.path.join(folder,f)
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image = Image.open(file).convert('RGB').resize((w, h), Image.ANTIALIAS)
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arr = np.asarray(image)
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imgs.append(arr)
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labels.append(zeros)
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total += 1
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print(total)
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return np.asarray(imgs,np.float32),np.asarray(labels,np.int32)
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data,label=read_source()
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num_example = data.shape[0]
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arr = np.arange(num_example)
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np.random.shuffle(arr)
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data = data[arr]
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label = label[arr]
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# 将所有数据分为训练集和验证集
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ratio = 0.8
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s = np.int(num_example * ratio)
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x_train = data[:s]
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y_train = label[:s]
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x_val = data[s:]
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y_val = label[s:]
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# -----------------构建网络----------------------
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x = tf.placeholder(tf.float32, shape=[None, w, h, 3], name='x')
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y_ = tf.placeholder(tf.float32, shape=[None,5 ], name='y_')
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def inference(input_tensor, train, regularizer):
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with tf.variable_scope('layer1-conv1'):
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conv1_weights = tf.get_variable("weight", [5, 5, 3, 64],
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initializer=tf.truncated_normal_initializer(stddev=0.1))
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conv1_biases = tf.get_variable("bias", [64], initializer=tf.constant_initializer(0.0))
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conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')
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relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))
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relu1 = tf.layers.batch_normalization(relu1,training=train)
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with tf.name_scope("layer2-pool1"):
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pool1 = tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
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with tf.variable_scope("layer3-conv2"):
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conv2_weights = tf.get_variable("weight", [3, 3, 64, 128],
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initializer=tf.truncated_normal_initializer(stddev=0.1))
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conv2_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))
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conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')
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relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))
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relu2 = tf.layers.batch_normalization(relu2, training=train)
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with tf.name_scope("layer4-pool2"):
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pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
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with tf.variable_scope("layer5-conv3"):
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conv3_weights = tf.get_variable("weight", [3, 3, 128, 256],
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initializer=tf.truncated_normal_initializer(stddev=0.1))
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conv3_biases = tf.get_variable("bias", [256], initializer=tf.constant_initializer(0.0))
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conv3 = tf.nn.conv2d(pool2, conv3_weights, strides=[1, 1, 1, 1], padding='SAME')
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relu3 = tf.nn.relu(tf.nn.bias_add(conv3, conv3_biases))
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relu3 = tf.layers.batch_normalization(relu3, training=train)
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with tf.name_scope("layer8-pool4"):
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pool4 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
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norm4 = tf.nn.lrn(pool4, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
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nodes = 8 * 8* 256
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reshaped = tf.reshape(norm4, [-1, nodes])
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with tf.variable_scope('layer9-fc1'):
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fc1_weights = tf.get_variable("weight", [nodes, 1024],
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initializer=tf.truncated_normal_initializer(stddev=0.1))
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if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights))
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fc1_biases = tf.get_variable("bias", [1024], initializer=tf.constant_initializer(0.1))
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fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)
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if train: fc1 = tf.nn.dropout(fc1, 0.8)
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with tf.variable_scope('layer10-fc2'):
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fc2_weights = tf.get_variable("weight", [1024, 512],
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initializer=tf.truncated_normal_initializer(stddev=0.1))
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if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights))
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fc2_biases = tf.get_variable("bias", [512], initializer=tf.constant_initializer(0.1))
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fc2 = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases)
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if train: fc2 = tf.nn.dropout(fc2, 0.8)
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with tf.variable_scope('layer11-fc3'):
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fc3_weights = tf.get_variable("weight", [512, 5],
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initializer=tf.truncated_normal_initializer(stddev=0.1))
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if regularizer != None: tf.add_to_collection('losses', regularizer(fc3_weights))
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fc3_biases = tf.get_variable("bias", [5], initializer=tf.constant_initializer(0.1))
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logit = tf.matmul(fc2, fc3_weights) + fc3_biases
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return logit
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# ---------------------------网络结束---------------------------
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regularizer = tf.contrib.layers.l2_regularizer(0.0001)
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logits = inference(x, False, regularizer)
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# (小处理)将logits乘以1赋值给logits_eval，定义name，方便在后续调用模型时通过tensor名字调用输出tensor
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b = tf.constant(value=1, dtype=tf.float32)
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logits_eval = tf.multiply(logits, b, name='logits_eval')
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# loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y_)
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# loss = -tf.reduce_sum(y_*tf.log(logits))
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loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits,labels=y_))
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update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
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with tf.control_dependencies(update_ops):
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train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
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correct_prediction = tf.equal(tf.argmax(logits, 1),tf.argmax(y_,1))
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acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
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# 定义一个函数，按批次取数据
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def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
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assert len(inputs) == len(targets)
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if shuffle:
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indices = np.arange(len(inputs))
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np.random.shuffle(indices)
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for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
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if shuffle:
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excerpt = indices[start_idx:start_idx + batch_size]
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else:
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excerpt = slice(start_idx, start_idx + batch_size)
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yield inputs[excerpt], targets[excerpt]
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# 训练和测试数据，可将n_epoch设置更大一些
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n_epoch = 200
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batch_size = 64
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saver = tf.train.Saver()
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sess = tf.Session()
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sess.run(tf.global_variables_initializer())
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for epoch in range(n_epoch):
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start_time = time.time()
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# training
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train_loss, train_acc, n_batch = 0, 0, 0
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for x_train_a, y_train_a in minibatches(x_train, y_train, batch_size, shuffle=True):
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_, err, ac = sess.run([train_op, loss, acc], feed_dict={x: x_train_a, y_: y_train_a})
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train_loss += err;
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train_acc += ac;
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n_batch += 1
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print(" train loss: %f" % (np.sum(train_loss) / n_batch))
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print(" train acc: %f" % (np.sum(train_acc) / n_batch))
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# validation
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val_loss, val_acc, n_batch = 0, 0, 0
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for x_val_a, y_val_a in minibatches(x_val, y_val, batch_size, shuffle=False):
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err, ac = sess.run([loss, acc], feed_dict={x: x_val_a, y_: y_val_a})
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val_loss += err
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val_acc += ac
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n_batch += 1
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print(" validation loss: %f" % (np.sum(val_loss) / n_batch))
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print(" validation acc: %f" % (np.sum(val_acc) / n_batch))
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saver.save(sess,model_path)
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sess.close()
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