深度学习图片分类cnn模板 -ag凯发k8国际

构建一个字母abc的手写识别网络,

要求给出算法误差收敛曲线，所给程序要有图片导入接口。

其中a，b，c都代表label，三个文件夹存在具体的图片。只要是这样类型的，直接套下面模板。

import os import cv2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import tensorflow as tf import tqdmfrom tensorflow import keras from keras import input, model, sequential from tensorflow.keras.regularizers import l2 from keras.layers import dense, flatten, inputlayer, reshape, batchnormalization, dropout, conv2d, maxpooling2d from tensorflow.keras.utils import plot_model%matplotlib inline data_dir = './data' # dict of labels categories = { 'a': 0, 'b': 1, 'c': 2 } def load_images(images_folder, img_size = (128,128), scale=false):# store paths to imagesimage_path = []for dirname, _, filenames in os.walk(images_folder):for filename in filenames:image_path.append(os.path.join(dirname, filename))print("there are {} images in {}".format(len(image_path), images_folder))# load images and associated labelsimages = []labels = []for path in tqdm.tqdm(image_path):img = cv2.imread(path) img = cv2.resize(img, img_size) # resize the imagesimg = np.array(img)images.append(img)labels.append(categories[path.split('/')[-2]]) # last folder before the image name is the categoryimages = np.array(images) images = images.astype(np.int64)if scale:images = images/255 # scalereturn image_path, images, np.asarray(labels) img_size = (128,128) image_path, images, labels = load_images(data_dir, img_size=img_size)# resize # images = np.array(images).reshape(-1,128,128,1) images.shape there are 600 images in ./data100%|██████████| 600/600 [00:03<00:00, 183.15it/s](600, 128, 128, 3) # 查看图片 plt.figure(figsize=(10,10)) random_inds = np.random.choice(len(image_path),36) for i in range(36):plt.subplot(6,6,i1)plt.xticks([])plt.yticks([])plt.grid(false)image_ind = random_inds[i]plt.imshow(np.squeeze(images[image_ind]), cmap=plt.cm.binary)label = list(categories.keys())[list(categories.values()).index(labels[image_ind])]plt.title(label) labels_df = pd.dataframe(labels) labels_df.value_counts() 2 201 0 201 1 198 dtype: int64 dataset=[] dataname=[] count=0 for name in tqdm(os.listdir(data_dir)):path=os.path.join(data_dir,name)for im in os.listdir(path):image=cv2.imread(os.path.join(path,im))image2=np.resize(image,(50,50,3))dataset =[image2]dataname =[count]count=count1 100%|██████████| 3/3 [00:03<00:00, 1.06s/it] data=np.array(dataset) dataname=np.array(dataname) data[0].shape (50, 50, 3) print(pd.series(dataname).value_counts()) 1 202 2 201 0 198 dtype: int64 len(categories) 3 from tensorflow.keras.layers import dense, conv2d, flatten, dropout, maxpooling2d, batchnormalizationdef build_cnn_model():cnn_model=tf.keras.sequential([conv2d(filters=32,kernel_size=(3,3),activation='relu',input_shape=images.shape[1:]),maxpooling2d(2,2),batchnormalization(),dropout(0.4),conv2d(filters=64,kernel_size=(3,3),activation='relu', padding='same'),conv2d(filters=64,kernel_size=(3,3),activation='relu', padding='same'),maxpooling2d((2,2)),batchnormalization(),dropout(0.4),conv2d(filters=128,kernel_size=(3,3),activation='relu', padding='same'),conv2d(filters=128,kernel_size=(3,3),activation='relu', padding='same'),maxpooling2d(2,2),batchnormalization(),dropout(0.4),conv2d(filters=256,kernel_size=(3,3),activation='relu', padding='same'),conv2d(filters=256,kernel_size=(3,3),activation='relu', padding='same'),maxpooling2d(2,2),batchnormalization(),dropout(0.4),conv2d(filters=128,kernel_size=(3,3),activation='relu', padding='same'),conv2d(filters=128,kernel_size=(3,3),activation='relu', padding='same'),maxpooling2d(2,2),batchnormalization(),dropout(0.4),conv2d(filters=64,kernel_size=(3,3),activation='relu', padding='same'),conv2d(filters=64,kernel_size=(3,3),activation='relu', padding='same'),maxpooling2d((2,2)),batchnormalization(),dropout(0.4),flatten(),dense(units=len(categories),activation='softmax')])return cnn_modelmodel = build_cnn_model() # initialize the model by passing some data through model.predict(images[[0]]) # print the summary of the layers in the model. print(model.summary()) model: "sequential_3" _________________________________________________________________layer (type) output shape param # =================================================================conv2d_6 (conv2d) (none, 126, 126, 32) 896 max_pooling2d_6 (maxpooling (none, 63, 63, 32) 0 2d) batch_normalization (batchn (none, 63, 63, 32) 128 ormalization) dropout (dropout) (none, 63, 63, 32) 0 conv2d_7 (conv2d) (none, 63, 63, 64) 18496 conv2d_8 (conv2d) (none, 63, 63, 64) 36928 max_pooling2d_7 (maxpooling (none, 31, 31, 64) 0 2d) batch_normalization_1 (batc (none, 31, 31, 64) 256 hnormalization) dropout_1 (dropout) (none, 31, 31, 64) 0 conv2d_9 (conv2d) (none, 31, 31, 128) 73856 conv2d_10 (conv2d) (none, 31, 31, 128) 147584 max_pooling2d_8 (maxpooling (none, 15, 15, 128) 0 2d) batch_normalization_2 (batc (none, 15, 15, 128) 512 hnormalization) dropout_2 (dropout) (none, 15, 15, 128) 0 conv2d_11 (conv2d) (none, 15, 15, 256) 295168 conv2d_12 (conv2d) (none, 15, 15, 256) 590080 max_pooling2d_9 (maxpooling (none, 7, 7, 256) 0 2d) batch_normalization_3 (batc (none, 7, 7, 256) 1024 hnormalization) dropout_3 (dropout) (none, 7, 7, 256) 0 conv2d_13 (conv2d) (none, 7, 7, 128) 295040 conv2d_14 (conv2d) (none, 7, 7, 128) 147584 max_pooling2d_10 (maxpoolin (none, 3, 3, 128) 0 g2d) batch_normalization_4 (batc (none, 3, 3, 128) 512 hnormalization) dropout_4 (dropout) (none, 3, 3, 128) 0 conv2d_15 (conv2d) (none, 3, 3, 64) 73792 conv2d_16 (conv2d) (none, 3, 3, 64) 36928 max_pooling2d_11 (maxpoolin (none, 1, 1, 64) 0 g2d) batch_normalization_5 (batc (none, 1, 1, 64) 256 hnormalization) dropout_5 (dropout) (none, 1, 1, 64) 0 flatten_1 (flatten) (none, 64) 0 dense_6 (dense) (none, 3) 195 ================================================================= total params: 1,719,235 trainable params: 1,717,891 non-trainable params: 1,344 _________________________________________________________________ none tf.keras.utils.plot_model(model, show_shapes=true) from tensorflow.keras.utils import to_categorical from sklearn.preprocessing import labelencoder from sklearn.utils import shuffle le = labelencoder() labels = le.fit_transform(labels) labels = to_categorical(labels) labels[:10] array([[0., 1., 0.],[0., 1., 0.],[0., 1., 0.],[0., 1., 0.],[0., 1., 0.],[0., 1., 0.],[0., 1., 0.],[0., 1., 0.],[0., 1., 0.],[0., 1., 0.]], dtype=float32) model.compile(optimizer = "adam", loss = "binary_crossentropy", metrics = ["accuracy"]) checkpoint_filepath = '/checkpoint.hdf5' model_checkpoint_callback = tf.keras.callbacks.modelcheckpoint(filepath=checkpoint_filepath,save_weights_only=true,monitor='val_accuracy',mode='max',save_best_only=true,save_freq=500) from tensorflow.keras.callbacks import earlystopping early_stopping = earlystopping(monitor='val_accuracy', patience=10, min_delta=0.001, mode='max',restore_best_weights=true ) datagen = imagedatagenerator(horizontal_flip=true,vertical_flip=true,rotation_range=20,zoom_range=0.2,width_shift_range=0.2,height_shift_range=0.2,shear_range=0.1,fill_mode="nearest") from tensorflow.keras.callbacks import reducelronplateau reducelr = reducelronplateau(monitor = "val_accuracy",factor = 0.3, patience = 3,min_delta = 0.001,mode = 'auto',verbose=1) from sklearn.model_selection import train_test_split # train, validation and test splitx_train, x_test, y_train, y_test = train_test_split(images, labels, test_size=0.10, random_state=7)x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.20, random_state=1) print("*-*-*-*-*-*") print("train") print(x_train.shape) print(y_train.shape)print("*-*-*-*-*-*") print("validation") print(x_val.shape) print(y_val.shape)print("*-*-*-*-*-*") print("test") print(x_test.shape) print(y_test.shape) *-*-*-*-*-* train (432, 128, 128, 3) (432, 3) *-*-*-*-*-* validation (108, 128, 128, 3) (108, 3) *-*-*-*-*-* test (60, 128, 128, 3) (60, 3) history = model.fit(x_train, y_train, batch_size = 32, epochs = 100, verbose = 1, validation_data = (x_val, y_val),callbacks=[model_checkpoint_callback, early_stopping, reducelr]) epoch 1/1000 14/14 [==============================] - 8s 238ms/step - loss: 0.8036 - accuracy: 0.3588 - val_loss: 6.2921 - val_accuracy: 0.2963 - lr: 0.0010 epoch 2/1000 14/14 [==============================] - 1s 95ms/step - loss: 0.8162 - accuracy: 0.3796 - val_loss: 5.2361 - val_accuracy: 0.2963 - lr: 0.0010 epoch 3/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.7190 - accuracy: 0.4537 - val_loss: 1.3893 - val_accuracy: 0.3333 - lr: 0.0010 epoch 4/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.6875 - accuracy: 0.4792 - val_loss: 0.7386 - val_accuracy: 0.3519 - lr: 0.0010 epoch 5/1000 14/14 [==============================] - 1s 100ms/step - loss: 0.6144 - accuracy: 0.5949 - val_loss: 0.7014 - val_accuracy: 0.4259 - lr: 0.0010 epoch 6/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.5156 - accuracy: 0.7060 - val_loss: 0.7592 - val_accuracy: 0.4537 - lr: 0.0010 epoch 7/1000 14/14 [==============================] - 1s 96ms/step - loss: 0.4904 - accuracy: 0.7384 - val_loss: 0.7034 - val_accuracy: 0.5370 - lr: 0.0010 epoch 8/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.3854 - accuracy: 0.7940 - val_loss: 0.6092 - val_accuracy: 0.5556 - lr: 0.0010 epoch 9/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.3313 - accuracy: 0.8241 - val_loss: 0.5192 - val_accuracy: 0.6389 - lr: 0.0010 epoch 10/1000 14/14 [==============================] - 1s 93ms/step - loss: 0.2873 - accuracy: 0.8519 - val_loss: 0.5089 - val_accuracy: 0.6111 - lr: 0.0010 epoch 11/1000 14/14 [==============================] - 1s 96ms/step - loss: 0.2346 - accuracy: 0.8981 - val_loss: 0.4359 - val_accuracy: 0.6852 - lr: 0.0010 epoch 12/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.2238 - accuracy: 0.8819 - val_loss: 0.4404 - val_accuracy: 0.6481 - lr: 0.0010 epoch 13/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.1954 - accuracy: 0.8912 - val_loss: 0.4215 - val_accuracy: 0.7500 - lr: 0.0010 epoch 14/1000 14/14 [==============================] - 1s 100ms/step - loss: 0.1792 - accuracy: 0.9051 - val_loss: 0.1971 - val_accuracy: 0.9074 - lr: 0.0010 epoch 15/1000 14/14 [==============================] - 1s 96ms/step - loss: 0.1608 - accuracy: 0.9144 - val_loss: 0.2836 - val_accuracy: 0.8056 - lr: 0.0010 epoch 16/1000 14/14 [==============================] - 1s 95ms/step - loss: 0.1447 - accuracy: 0.9398 - val_loss: 0.2867 - val_accuracy: 0.7500 - lr: 0.0010 epoch 17/1000 14/14 [==============================] - eta: 0s - loss: 0.1215 - accuracy: 0.9375 epoch 00017: reducelronplateau reducing learning rate to 0.0003000000142492354. 14/14 [==============================] - 1s 95ms/step - loss: 0.1215 - accuracy: 0.9375 - val_loss: 0.1474 - val_accuracy: 0.9074 - lr: 0.0010 epoch 18/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.1023 - accuracy: 0.9537 - val_loss: 0.1186 - val_accuracy: 0.9352 - lr: 3.0000e-04 epoch 19/1000 14/14 [==============================] - 1s 101ms/step - loss: 0.0992 - accuracy: 0.9606 - val_loss: 0.1074 - val_accuracy: 0.9444 - lr: 3.0000e-04 epoch 20/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.0837 - accuracy: 0.9676 - val_loss: 0.0917 - val_accuracy: 0.9444 - lr: 3.0000e-04 epoch 21/1000 14/14 [==============================] - 1s 98ms/step - loss: 0.0788 - accuracy: 0.9699 - val_loss: 0.0877 - val_accuracy: 0.9444 - lr: 3.0000e-04 epoch 22/1000 14/14 [==============================] - eta: 0s - loss: 0.0809 - accuracy: 0.9722 epoch 00022: reducelronplateau reducing learning rate to 9.000000427477062e-05. 14/14 [==============================] - 1s 95ms/step - loss: 0.0809 - accuracy: 0.9722 - val_loss: 0.0897 - val_accuracy: 0.9444 - lr: 3.0000e-04 epoch 23/1000 14/14 [==============================] - 1s 95ms/step - loss: 0.0677 - accuracy: 0.9792 - val_loss: 0.0834 - val_accuracy: 0.9537 - lr: 9.0000e-05 epoch 24/1000 14/14 [==============================] - 1s 93ms/step - loss: 0.0741 - accuracy: 0.9722 - val_loss: 0.0771 - val_accuracy: 0.9537 - lr: 9.0000e-05 epoch 25/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.0672 - accuracy: 0.9815 - val_loss: 0.0733 - val_accuracy: 0.9537 - lr: 9.0000e-05 epoch 26/1000 14/14 [==============================] - eta: 0s - loss: 0.0595 - accuracy: 0.9838 epoch 00026: reducelronplateau reducing learning rate to 2.700000040931627e-05. 14/14 [==============================] - 1s 95ms/step - loss: 0.0595 - accuracy: 0.9838 - val_loss: 0.0694 - val_accuracy: 0.9537 - lr: 9.0000e-05 epoch 27/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.0631 - accuracy: 0.9838 - val_loss: 0.0699 - val_accuracy: 0.9537 - lr: 2.7000e-05 epoch 28/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.0591 - accuracy: 0.9861 - val_loss: 0.0705 - val_accuracy: 0.9537 - lr: 2.7000e-05 epoch 29/1000 14/14 [==============================] - eta: 0s - loss: 0.0635 - accuracy: 0.9838 epoch 00029: reducelronplateau reducing learning rate to 8.100000013655517e-06. 14/14 [==============================] - 1s 95ms/step - loss: 0.0635 - accuracy: 0.9838 - val_loss: 0.0697 - val_accuracy: 0.9444 - lr: 2.7000e-05 epoch 30/1000 14/14 [==============================] - 1s 95ms/step - loss: 0.0643 - accuracy: 0.9792 - val_loss: 0.0687 - val_accuracy: 0.9444 - lr: 8.1000e-06 epoch 31/1000 14/14 [==============================] - 1s 100ms/step - loss: 0.0768 - accuracy: 0.9745 - val_loss: 0.0665 - val_accuracy: 0.9537 - lr: 8.1000e-06 epoch 32/1000 14/14 [==============================] - eta: 0s - loss: 0.0645 - accuracy: 0.9861 epoch 00032: reducelronplateau reducing learning rate to 2.429999949526973e-06. 14/14 [==============================] - 1s 95ms/step - loss: 0.0645 - accuracy: 0.9861 - val_loss: 0.0656 - val_accuracy: 0.9537 - lr: 8.1000e-06 epoch 33/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.0635 - accuracy: 0.9792 - val_loss: 0.0645 - val_accuracy: 0.9630 - lr: 2.4300e-06 epoch 34/1000 14/14 [==============================] - 1s 95ms/step - loss: 0.0606 - accuracy: 0.9838 - val_loss: 0.0636 - val_accuracy: 0.9630 - lr: 2.4300e-06 epoch 35/1000 14/14 [==============================] - 1s 95ms/step - loss: 0.0620 - accuracy: 0.9907 - val_loss: 0.0628 - val_accuracy: 0.9630 - lr: 2.4300e-06 epoch 36/10009/14 [==================>...........] - eta: 0s - loss: 0.0729 - accuracy: 0.9826warning:tensorflow:can save best model only with val_accuracy available, skipping. 14/14 [==============================] - eta: 0s - loss: 0.0682 - accuracy: 0.9861 epoch 00036: reducelronplateau reducing learning rate to 7.289999985005124e-07. 14/14 [==============================] - 1s 95ms/step - loss: 0.0682 - accuracy: 0.9861 - val_loss: 0.0622 - val_accuracy: 0.9630 - lr: 2.4300e-06 epoch 37/1000 14/14 [==============================] - 1s 96ms/step - loss: 0.0573 - accuracy: 0.9907 - val_loss: 0.0613 - val_accuracy: 0.9630 - lr: 7.2900e-07 epoch 38/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.0575 - accuracy: 0.9931 - val_loss: 0.0607 - val_accuracy: 0.9722 - lr: 7.2900e-07 epoch 39/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.0622 - accuracy: 0.9769 - val_loss: 0.0600 - val_accuracy: 0.9722 - lr: 7.2900e-07 epoch 40/1000 14/14 [==============================] - 1s 96ms/step - loss: 0.0660 - accuracy: 0.9838 - val_loss: 0.0594 - val_accuracy: 0.9722 - lr: 7.2900e-07 epoch 41/1000 14/14 [==============================] - eta: 0s - loss: 0.0614 - accuracy: 0.9884 epoch 00041: reducelronplateau reducing learning rate to 2.1870000637136398e-07. 14/14 [==============================] - 1s 95ms/step - loss: 0.0614 - accuracy: 0.9884 - val_loss: 0.0591 - val_accuracy: 0.9722 - lr: 7.2900e-07 epoch 42/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.0605 - accuracy: 0.9792 - val_loss: 0.0583 - val_accuracy: 0.9722 - lr: 2.1870e-07 epoch 43/1000 14/14 [==============================] - 1s 99ms/step - loss: 0.0529 - accuracy: 0.9954 - val_loss: 0.0582 - val_accuracy: 0.9722 - lr: 2.1870e-07 epoch 44/1000 14/14 [==============================] - eta: 0s - loss: 0.0500 - accuracy: 0.9884 epoch 00044: reducelronplateau reducing learning rate to 6.561000276406048e-08. 14/14 [==============================] - 1s 95ms/step - loss: 0.0500 - accuracy: 0.9884 - val_loss: 0.0580 - val_accuracy: 0.9722 - lr: 2.1870e-07 epoch 45/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.0613 - accuracy: 0.9861 - val_loss: 0.0581 - val_accuracy: 0.9722 - lr: 6.5610e-08 epoch 46/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.0672 - accuracy: 0.9861 - val_loss: 0.0572 - val_accuracy: 0.9722 - lr: 6.5610e-08 epoch 47/1000 14/14 [==============================] - eta: 0s - loss: 0.0511 - accuracy: 0.9931 epoch 00047: reducelronplateau reducing learning rate to 1.9683000829218145e-08. 14/14 [==============================] - 1s 96ms/step - loss: 0.0511 - accuracy: 0.9931 - val_loss: 0.0574 - val_accuracy: 0.9722 - lr: 6.5610e-08 epoch 48/1000 14/14 [==============================] - 1s 99ms/step - loss: 0.0622 - accuracy: 0.9861 - val_loss: 0.0570 - val_accuracy: 0.9722 - lr: 1.9683e-08 plt.plot(history.history["accuracy"]) plt.plot(history.history["val_accuracy"]) plt.title("model accuracy") plt.ylabel("accuracy") plt.xlabel("epoch") plt.legend(["train", "test"], loc = "upper left") plt.show() plt.plot(history.history["loss"]) plt.plot(history.history["val_loss"]) plt.title("model loss") plt.ylabel("loss") plt.xlabel("epoch") plt.legend(["train", "test"], loc = "upper left") plt.show() def predict_class(img):# resizeimg = img.reshape(1,128,128,3)# predictpredictions = model.predict(img)true_prediction = [tf.argmax(pred) for pred in predictions]true_prediction = np.array(true_prediction)# return label corresponding to predicted indexreturn list(categories.keys())[list(categories.values()).index(true_prediction)] # predict on test set y_pred = model.predict(x_test) # from categorical outputs to discrete values y_pred_ = [np.argmax(y) for y in y_pred] y_test_ = [np.argmax(y) for y in y_test] from sklearn.metrics import classification_report print(classification_report(y_test_, y_pred_)) precision recall f1-score support0 1.00 0.96 0.98 251 0.85 1.00 0.92 112 1.00 0.96 0.98 24accuracy 0.97 60macro avg 0.95 0.97 0.96 60 weighted avg 0.97 0.97 0.97 60 plt.figure(figsize=(10,10)) random_inds = np.random.choice(x_test.shape[0],36) for i in range(36):plt.subplot(6,6,i1)plt.xticks([])plt.yticks([])plt.grid(false)image_ind = random_inds[i]plt.imshow(np.squeeze(x_test[image_ind]), cmap=plt.cm.binary)# predict and get labellabel = predict_class(x_test[image_ind])plt.xlabel(label) model.save("model.h5") # 识别接口 def predict(path,model_str,img_size = (128,128)):new_model = tf.keras.models.load_model(model_str)img = cv2.imread(path)img = cv2.resize(img, img_size) # resize the imagesimg = np.array(img) # resizeimg = img.reshape(1,128,128,3)# predictpredictions = new_model.predict(img)true_prediction = [tf.argmax(pred) for pred in predictions]true_prediction = np.array(true_prediction)# return label corresponding to predicted indexreturn list(categories.keys())[list(categories.values()).index(true_prediction)]predict("./data/a/051.jpg","model.h5") 'a' predict("./data/b/048.jpg","model.h5") 'b' predict("./data/c/050.jpg","model.h5") 'c'

代码资料下载：　https://download.csdn.net/download/weixin_44510615/72775830

总结

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