keras分类之二分类实例(Cat and dog)

 # 存储数据集的目录 base_dir = 'E:/python learn/dog_and_cat/data/' # 训练、验证数据集的目录 train_dir = os.path.join(base_dir, 'train') validation_dir = os.path.join(base_dir, 'validation') test_dir = os.path.join(base_dir, 'test') # 猫训练图片所在目录 train_cats_dir = os.path.join(train_dir, 'cats') # 狗训练图片所在目录 train_dogs_dir = os.path.join(train_dir, 'dogs') # 猫验证图片所在目录 validation_cats_dir = os.path.join(validation_dir, 'cats') # 狗验证数据集所在目录 validation_dogs_dir = os.path.join(validation_dir, 'dogs') print('total training cat images:', len(os.listdir(train_cats_dir))) print('total training dog images:', len(os.listdir(train_dogs_dir))) print('total validation cat images:', len(os.listdir(validation_cats_dir))) print('total validation dog images:', len(os.listdir(validation_dogs_dir)))

 # 搭建模型 model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3))) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(128, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(128, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Flatten()) model.add(Dense(512, activation='relu')) model.add(Dense(1, activation='sigmoid')) print(model.summary()) model.compile(loss='binary_crossentropy', optimizer=RMSprop(lr=1e-4), metrics=['acc']) 

 train_datagen = ImageDataGenerator(rescale=1./255) test_datagen = ImageDataGenerator(rescale=1./255) train_generator = train_datagen.flow_from_directory( train_dir, # ta<span style="color:transparent">来源gaodai#ma#com搞*代#码网</span>rget directory target_size=(150, 150), # resize图片 batch_size=20, class_mode='binary' ) validation_generator = test_datagen.flow_from_directory( validation_dir, target_size=(150, 150), batch_size=20, class_mode='binary' ) for data_batch, labels_batch in train_generator: print('data batch shape:', data_batch.shape) print('labels batch shape:', labels_batch.shape) break hist = model.fit_generator( train_generator, steps_per_epoch=100, epochs=10, validation_data=validation_generator, validation_steps=50 ) model.save('cats_and_dogs_small_1.h5')

 acc = hist.history['acc'] val_acc = hist.history['val_acc'] loss = hist.history['loss'] val_loss = hist.history['val_loss'] epochs = range(len(acc)) plt.plot(epochs, acc, 'bo', label='Training acc') plt.plot(epochs, val_acc, 'b', label='Validation acc') plt.title('Training and validation accuracy') plt.legend() plt.figure() plt.figure() plt.plot(epochs, loss, 'bo', label='Training loss') plt.plot(epochs, val_loss, 'b', label='Validation loss') plt.legend() plt.show()

 imagename = 'E:/python learn/dog_and_cat/data/validation/dogs/dog.2026.jpg-600' test_image = image.load_img(imagename, target_size = (150, 150)) test_image = image.img_to_array(test_image) test_image = np.expand_dims(test_image, axis=0) result = model.predict(test_image) if result[0][0] == 1: prediction ='dog' else: prediction ='cat' print(prediction)

 import os import numpy as np import tensorflow as tf import random import seaborn as sns import matplotlib.pyplot as plt import keras from keras.models import Sequential, Model from keras.layers import Dense, Dropout, Activation, Flatten, Input,BatchNormalization from keras.layers.convolutional import Conv2D, MaxPooling2D from keras.optimizers import RMSprop, Adam, SGD from keras.preprocessing import image from keras.preprocessing.image import ImageDataGenerator from keras.applications.vgg16 import VGG16, preprocess_input from sklearn.model_selection import train_test_split

 def read_and_process_image(data_dir,width=64, height=64, channels=3, preprocess=False): train_images= [data_dir + i for i in os.listdir(data_dir)] random.shuffle(train_images) def read_image(file_path, preprocess): img = image.load_img(file_path, target_size=(height, width)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) # if preprocess: # x = preprocess_input(x) return x def prep_data(images, proprocess): count = len(images) data = np.ndarray((count, height, width, channels), dtype = np.float32) for i, image_file in enumerate(images): image = read_image(image_file, preprocess) data[i] = image return data def read_labels(file_path): labels = [] for i in file_path: label = 1 if 'dog' in i else 0 labels.append(label) return labels X = prep_data(train_images, preprocess) labels = read_labels(train_images) assert X.shape[0] == len(labels) print("Train shape: {}".format(X.shape)) return X, labels

 # 读取图片 WIDTH = 150 HEIGHT = 150 CHANNELS = 3 X, y = read_and_process_image('D:\\Python_Project\\train\\',width=WIDTH, height=HEIGHT, channels=CHANNELS)

 # 统计y sns.countplot(y) # 显示图片 def show_cats_and_dogs(X, idx): plt.figure(figsize=(10,5), frameon=True) img = X[idx,:,:,::-1] img = img/255 plt.imshow(img) plt.show() for idx in range(0,3): show_cats_and_dogs(X, idx) train_X = X[0:17500,:,:,:] train_y = y[0:17500] test_X = X[17500:25000,:,:,:] test_y = y[17500:25000] train_X.shape test_X.shape

 input_layer = Input((WIDTH, HEIGHT, CHANNELS)) # 第一层 z = input_layer z = Conv2D(64, (3,3))(z) z = BatchNormalization()(z) z = Activation('relu')(z) z = MaxPooling2D(pool_size = (2,2))(z) z = Conv2D(64, (3,3))(z) z = BatchNormalization()(z) z = Activation('relu')(z) z = MaxPooling2D(pool_size = (2,2))(z) z = Conv2D(128, (3,3))(z) z = BatchNormalization()(z) z = Activation('relu')(z) z = MaxPooling2D(pool_size = (2,2))(z) z = Conv2D(128, (3,3))(z) z = BatchNormalization()(z) z = Activation('relu')(z) z = MaxPooling2D(pool_size = (2,2))(z) z = Flatten()(z) z = Dense(64)(z) z = BatchNormalization()(z) z = Activation('relu')(z) z = Dropout(0.5)(z) z = Dense(1)(z) z = Activation('sigmoid')(z) model = Model(input_layer, z) model.compile( optimizer = keras.optimizers.RMSprop(), loss = keras.losses.binary_crossentropy, metrics = [keras.metrics.binary_accuracy] ) model.summary()

 history = model.fit(train_X,train_y, validation_data=(test_X, test_y),epochs=10,batch_size=128,verbose=True) score = model.evaluate(test_X, test_y, verbose=0) print("Large CNN Error: %.2f%%" %(100-score[1]*100))

 def vgg16_model(input_shape= (HEIGHT,WIDTH,CHANNELS)): vgg16 = VGG16(include_top=False, weights='imagenet',input_shape=input_shape) for layer in vgg16.layers: layer.trainable = False last = vgg16.output # 后面加入自己的模型 x = Flatten()(last) x = Dense(256, activation='relu')(x) x = Dropout(0.5)(x) x = Dense(256, activation='relu')(x) x = Dropout(0.5)(x) x = Dense(1, activation='sigmoid')(x) model = Model(inputs=vgg16.input, outputs=x) return model

 model_vgg16 = vgg16_model() model_vgg16.summary() model_vgg16.compile(loss='binary_crossentropy',optimizer = Adam(0.0001), metrics = ['accuracy'])

 # 训练模型 history = model_vgg16.fit(train_X,train_y, validation_data=(test_X, test_y),epochs=5,batch_size=128,verbose=True) score = model_vgg16.evaluate(test_X, test_y, verbose=0) print("Large CNN Error: %.2f%%" %(100-score[1]*100))