基于吴恩达深度学习的图像分类识别作业源码.zip

## 前言 本文是代码根据**吴恩达深度学习第四课程第一周第二节作业图像分类识别**修改而成，会简单介绍一下项目流程，然后介绍tensorflow1保存模型的两种方法，以及如何用模型预测。 ## 项目流程简单介绍 这里直接放代码了比较简单。 ``` import tensorflow.compat.v1 as tf from cnn_utils import * SAVE_FILE = "./model/model" SECOND_TRAING = False np.random.seed(1) X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset() X_train = X_train_orig/255. X_test = X_test_orig/255. Y_train = convert_to_one_hot(Y_train_orig, 6).T Y_test = convert_to_one_hot(Y_test_orig, 6).T print ("number of training examples = " + str(X_train.shape[0])) print ("number of test examples = " + str(X_test.shape[0])) print ("X_train shape: " + str(X_train.shape)) print ("Y_train shape: " + str(Y_train.shape)) print ("X_test shape: " + str(X_test.shape)) print ("Y_test shape: " + str(Y_test.shape)) conv_layers = {} def create_placeholders(n_H0, n_W0, n_C0, n_y): X = tf.placeholder(tf.float32, shape=(None, n_H0, n_W0, n_C0), name="X") Y = tf.placeholder(tf.float32, shape=(None, n_y), name="Y") return X, Y def initialize_parameters(): tf.set_random_seed(1) W1 = tf.get_variable('W1', [4, 4, 3, 8], initializer=tf.contrib.layers.xavier_initializer(seed=0)) W2 = tf.get_variable('W2', [2, 2, 8, 16], initializer=tf.contrib.layers.xavier_initializer(seed=0)) parameters = {"W1": W1, "W2": W2} return parameters def forward_propagation(X, parameters): W1 = parameters['W1'] W2 = parameters['W2'] Z1 = tf.nn.conv2d(X, W1, strides= [1,1,1,1], padding= 'SAME') A1 = tf.nn.relu(Z1) P1 = tf.nn.max_pool(A1, ksize= [1,8,8,1], strides= [1,8,8,1], padding= 'SAME') Z2 = tf.nn.conv2d(P1, W2, strides= [1,1,1,1], padding= 'SAME') A2 = tf.nn.relu(Z2) P2 = tf.nn.max_pool(A2, ksize= [1,4,4,1], strides= [1,4,4,1], padding= 'SAME') P2 = tf.layers.flatten(P2) Z3 = tf.layers.dense(inputs=P2 , units=6, name='Z3') return Z3 def compute_cost(Z3, Y): cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits= Z3, labels=Y),name="cost") return cost def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.009, num_epochs = 100, minibatch_size = 64, print_cost = True): tf.set_random_seed(1) seed = 3 (m, n_H0, n_W0, n_C0) = X_train.shape n_y = Y_train.shape[1] costs = [] X, Y = create_placeholders(n_H0, n_W0, n_C0, n_y) parameters = initialize_parameters() Z3 = forward_propagation(X, parameters) cost = compute_cost(Z3, Y) optimizer = tf.train.AdamOptimizer(learning_rate= learning_rate).minimize(cost,name="optimizer") with tf.Session() as sess: init = tf.global_variables_initializer() sess.run(init) for epoch in range(num_epochs): minibatch_cost = 0. num_minibatches = int(m / minibatch_size) seed = seed + 1 minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed) for minibatch in minibatches: (minibatch_X, minibatch_Y) = minibatch _, temp_cost = sess.run([optimizer, cost], feed_dict={X:minibatch_X, Y:minibatch_Y}) minibatch_cost += temp_cost / num_minibatches if print_cost == True and epoch % 5 == 0: print("Cost after epoch %i: %f" %(epoch, minibatch_cost)) if print_cost == True and epoch %1 == 0: costs.append(minibatch_cost) # plot the cost plt.plot(np.squeeze(costs)) plt.ylabel('cost') plt.xlabel('iterations (per tens)') plt.title("Learning rate =" + str(learning_rate)) plt.show() # Calculate the correct predictions predict_op = tf.argmax(Z3, 1) correct_prediction = tf.equal(predict_op, tf.argmax(Y, 1)) # Calculate accuracy on the test set accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print(accuracy) train_accuracy = accuracy.eval({X: X_train, Y: Y_train}) test_accuracy = accuracy.eval({X: X_test, Y: Y_test}) print("Train Accuracy:", train_accuracy) print("Test Accuracy:", test_accuracy) return train_accuracy, test_accuracy, parameters _, _, parameters = model(X_train, Y_train, X_test, Y_test) ``` ## 保存模型 定义model的保存路径 ``` SAVE_FILE = "./model/model" ``` 在model函数中定义 tf.train.Saver(),运行save()保存模型 ``` with tf.Session() as sess: saver = tf.train.Saver() ''' ''' if print_cost == True and epoch % 5 == 0: savepath = saver.save(sess,SAVE_FILE) print("Model saved in file: %s" %savepath) ``` tensorflow训练后保存的模型主要包含两部分，一是网络结构的定义（网络图），二是网络结构里的参数值。 1、.meta文件 .meta 文件以 “protocol buffer”格式保存了整个模型的结构图，模型上定义的操作等信息。 这个文件保存了网络结构的定义。 2、.data-00000-of-00001 文件和 .index 文件 .data-00000-of-00001 文件和 .index 文件合在一起组成了 ckpt 文件，保存了网络结构中所有 权重和偏置 的数值。 .data文件保存的是变量值，.index文件保存的是.data文件中数据和 .meta文件中结构图之间的对应关系（Mabey）。 3、 checkpoint文件 checkpoint是一个文本文件，记录了训练过程中在所有中间节点上保存的模型的名称，首行记录的是最后（最近）一次保存的模型名称。 ## 第一种恢复模型方法:断点续训 只需要修改一处： ``` with tf.Session() as sess: saver = tf.train.Saver() #通过模型进行训练，注意前面已经构建了图，而restore函数是给前面的变量进行赋值。 if SECOND_TRAING: init = tf.global_variables_initializer() sess.run(init) #注意可以在restore之前进行初始化操作，之后不可以 saver.restore(sess, SAVE_FILE) else: init = tf.global_variables_initializer() sess.run(init) ``` 等于是将图重新建立了一遍，和之前图的一样，然后将ckpt文件里的数据restore到图里的变量里。 ## 第二种恢复模型方法： 利用.mate文件恢复图 如果不想重新定义一遍图，可以通过import_meta_graph()来加载图，并用restore给变量赋值，之后可以对获取的值进行操作，如果之后save的话，也会将import_meta_graph()中图引用的部分保存下来。 ``` #不对图进行重构，利用加载的图进行训练 def train_by_model(X_train, Y_train, X_test, Y_test, learning_rate = 0.009, num_epochs = 100, minibatch_size = 64, print_cost = True): tf.set_random_seed(1) seed = 3 (m, n_H0, n_W0, n_C0) = X_train.shape n_y = Y_train.shape[1] costs = [] with tf.Session() as sess: #这里直接将模型的图导入并构建为default saver = tf.train.import_meta_graph('./model/model.meta') saver.restore(sess, SAVE_FILE) graph = tf.get_default_graph() X = graph.get_tensor_by_name('X:0') Y = graph.get_tensor_by_name('Y:0') cost = graph.get_tensor_by_name('cost:0') optimizer = graph.get_operation_by_name('optimizer') Z3 = tf.get_collection('output')[0] for epoch in range(num_epochs): minibatch_cost = 0. num_minibatches = int(m / minibatch_size) seed = seed + 1 minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed) for minibatch in minibatches: (minibatch_X, minibatch_Y) = minibatch _, temp_cost = sess.run([optimizer, cost], feed_dict={X:minibatch_X, Y:minibatch_Y}) minibatch_cost += temp_cost / num_miniba