http://www-cvr.ai.uiuc.edu/ponce_grp/data/ 我用birds那个 #以下是code import os import glob # 查找文件 from keras.models import Sequential from keras.layers.core import Flatten, Dense, Dropout from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D from keras.optimizers import SGD import numpy as np from pandas import Series, DataFrame from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation, Flatten from keras.layers.advanced_activations import PReLU from keras.layers.convolutional import Convolution2D, MaxPooling2D from keras.optimizers import SGD, Adadelta, Adagrad from keras.utils import np_utils, generic_utils from six.moves import range def load_data(): sed = 1000 data = np.empty((540,150,150,3),dtype="float32") label = np.empty((540,)) imgs = os.listdir(r"C:\Users\user\Desktop\birds\train") num = len(imgs) times = 0 time = 0 for i in range(num): if "egr" in imgs[i]: img = Image.open("C:/Users/user/Desktop/birds/train/" + imgs[i]) arr = np.asarray(img, dtype="float32") arr.resize((150,150,3)) data[i, :, :, :] = arr label[i] = 0 #times +=1 elif "man" in imgs[i]: img = Image.open("C:/Users/user/Desktop/birds/train/" + imgs[i]) arr = np.asarray(img, dtype="float32") arr.resize((150,150,3)) data[i, :, :, :] = arr label[i] = 1 #times +=1 elif "owl" in imgs[i]: img = Image.open("C:/Users/user/Desktop/birds/train/" + imgs[i]) arr = np.asarray(img, dtype="float32") arr.resize((150,150,3)) data[i, :, :, :] = arr label[i] = 2 #times +=1 elif "puf" in imgs[i]: img = Image.open("C:/Users/user/Desktop/birds/train/" + imgs[i]) arr = np.asarray(img, dtype="float32") arr.resize((150,150,3)) data[i, :, :, :] = arr label[i] = 3 #times +=1 elif "tou" in imgs[i]: img = Image.open("C:/Users/user/Desktop/birds/train/" + imgs[i]) arr = np.asarray(img, dtype="float32") arr.resize((150,150,3)) data[i, :, :, :] = arr label[i] = 4 #times +=1 elif "wod" in imgs[i]: img = Image.open("C:/Users/user/Desktop/birds/train/" + imgs[i]) arr = np.asarray(img, dtype="float32") arr.resize((150,150,3)) data[i, :, :, :] = arr label[i] = 5 #times +=1 else: break return data,label train_data,train_labels = load_data() def load_data1(): sed = 1000 data = np.empty((60,150,150,3),dtype="float32") label = np.empty((60,)) imgs = os.listdir(r"C:\Users\user\Desktop\birds\test") num = len(imgs) times = 0 time = 0 for i in range(num): if "egr" in imgs[i]: img = Image.open("C:/Users/user/Desktop/birds/test/" + imgs[i]) arr = np.asarray(img, dtype="float32") arr.resize((150,150,3)) data[i, :, :, :] = arr label[i] = 0 #times +=1 elif "man" in imgs[i]: img = Image.open("C:/Users/user/Desktop/birds/test/" + imgs[i]) arr = np.asarray(img, dtype="float32") arr.resize((150,150,3)) data[i, :, :, :] = arr label[i] = 1 #times +=1 elif "owl" in imgs[i]: img = Image.open("C:/Users/user/Desktop/birds/test/" + imgs[i]) arr = np.asarray(img, dtype="float32") arr.resize((150,150,3)) data[i, :, :, :] = arr label[i] = 2 #times +=1 elif "puf" in imgs[i]: img = Image.open("C:/Users/user/Desktop/birds/test/" + imgs[i]) arr = np.asarray(img, dtype="float32") arr.resize((150,150,3)) data[i, :, :, :] = arr label[i] = 3 #times +=1 elif "tou" in imgs[i]: img = Image.open("C:/Users/user/Desktop/birds/test/" + imgs[i]) arr = np.asarray(img, dtype="float32") arr.resize((150,150,3)) data[i, :, :, :] = arr label[i] = 4 #times +=1 elif "wod" in imgs[i]: img = Image.open("C:/Users/user/Desktop/birds/test/" + imgs[i]) arr = np.asarray(img, dtype="float32") arr.resize((150,150,3)) data[i, :, :, :] = arr label[i] = 5 #times +=1 else: break return data,label datatest,labeltest = load_data1() y_train=np_utils.to_categorical(train_labels,num_classes=6) #np.utils.to_categorical : 若 y＝2 －> [[ 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]] y_test=np_utils.to_categorical(labeltest,num_classes=6) model = Sequential() model.add(Convolution2D(32, (3, 3),input_shape=(150, 150,3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Convolution2D(64, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Convolution2D(64, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(64)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(6)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) model.fit(train_data, y_train, nb_epoch=50, batch_size=20, validation_data=(datatest, y_test)) 我跑出的结果长这样 我觉得很怪 Epoch 1/50 540/540 [==============================] - 21s 40ms/step - loss: 2.0500 - acc: 0.1630 - val_loss: 1.9806 - val_acc: 0.1667 Epoch 2/50 540/540 [==============================] - 21s 39ms/step - loss: 1.9121 - acc: 0.1574 - val_loss: 1.8918 - val_acc: 0.1667 Epoch 3/50 540/540 [==============================] - 21s 39ms/step - loss: 1.8998 - acc: 0.1704 - val_loss: 1.8457 - val_acc: 0.1667 Epoch 4/50 540/540 [==============================] - 21s 39ms/step - loss: 1.8353 - acc: 0.1704 - val_loss: 1.8256 - val_acc: 0.1667 Epoch 5/50 540/540 [==============================] - 21s 38ms/step - loss: 1.8449 - acc: 0.1574 - val_loss: 1.8175 - val_acc: 0.1667 Epoch 6/50 540/540 [==============================] - 21s 38ms/step - loss: 1.8065 - acc: 0.1796 - val_loss: 1.8098 - val_acc: 0.1667 Epoch 7/50 540/540 [==============================] - 21s 39ms/step - loss: 1.8136 - acc: 0.1778 - val_loss: 1.8035 - val_acc: 0.1667 Epoch 8/50 540/540 [==============================] - 22s 41ms/step - loss: 1.8419 - acc: 0.1852 - val_loss: 1.7981 - val_acc: 0.1667 Epoch 9/50 540/540 [==============================] - 21s 39ms/step - loss: 1.8219 - acc: 0.1685 - val_loss: 1.7956 - val_acc: 0.1667 Epoch 10/50 540/540 [==============================] - 22s 40ms/step - loss: 1.7954 - acc: 0.1889 - val_loss: 1.7936 - val_acc: 0.1667 Epoch 11/50 540/540 [==============================] - 22s 40ms/step - loss: 1.8192 - acc: 0.1741 - val_loss: 1.7926 - val_acc: 0.1667 Epoch 12/50 540/540 [==============================] - 21s 40ms/step - loss: 1.8244 - acc: 0.1741 - val_loss: 1.7921 - val_acc: 0.1667 Epoch 13/50 540/540 [==============================] - 23s 42ms/step - loss: 1.8233 - acc: 0.1815 - val_loss: 1.7919 - val_acc: 0.1667 Epoch 14/50 540/540 [==============================] - 22s 40ms/step - loss: 1.7947 - acc: 0.1889 - val_loss: 1.7919 - val_acc: 0.1667 Epoch 15/50 540/540 [==============================] - 21s 39ms/step - loss: 1.7991 - acc: 0.2019 - val_loss: 1.7919 - val_acc: 0.1667 Epoch 16/50 540/540 [==============================] - 21s 39ms/step - loss: 1.8107 - acc: 0.2037 - val_loss: 1.7923 - val_acc: 0.1667 Epoch 17/50 540/540 [==============================] - 21s 40ms/step - loss: 1.8087 - acc: 0.1815 - val_loss: 1.7945 - val_acc: 0.1500 Epoch 18/50 540/540 [==============================] - 21s 39ms/step - loss: 1.8248 - acc: 0.2019 - val_loss: 1.7977 - val_acc: 0.1667 Epoch 19/50 540/540 [==============================] - 21s 39ms/step - loss: 1.8012 - acc: 0.1611 - val_loss: 1.7968 - val_acc: 0.1667 Epoch 20/50 540/540 [==============================] - 21s 39ms/step - loss: 1.8415 - acc: 0.1796 - val_loss: 1.7933 - val_acc: 0.1667 Epoch 21/50 540/540 [==============================] - 21s 39ms/step - loss: 1.8390 - acc: 0.1778 - val_loss: 1.8040 - val_acc: 0.1667 Epoch 22/50 540/540 [==============================] - 21s 39ms/step - loss: 1.8363 - acc: 0.1778 - val_loss: 1.8007 - val_acc: 0.1667 Epoch 23/50 540/540 [==============================] - 21s 39ms/step - loss: 1.8053 - acc: 0.1630 - val_loss: 1.7787 - val_acc: 0.1500 Epoch 24/50 540/540 [==============================] - 21s 39ms/step - loss: 1.8064 - acc: 0.1704 - val_loss: 1.7940 - val_acc: 0.1667 Epoch 25/50 540/540 [==============================] - 21s 39ms/step - loss: 1.8006 - acc: 0.1481 - val_loss: 1.7979 - val_acc: 0.1667 Epoch 26/50 540/540 [==============================] - 21s 39ms/step - loss: 1.8004 - acc: 0.1630 - val_loss: 1.7972 - val_acc: 0.1667 Epoch 27/50 540/540 [==============================] - 21s 39ms/step - loss: 1.8358 - acc: 0.1870 - val_loss: 1.7968 - val_acc: 0.1667 Epoch 28/50 540/540 [==============================] - 21s 39ms/step - loss: 1.7930 - acc: 0.1759 - val_loss: 1.7969 - val_acc: 0.1667 Epoch 29/50 540/540 [==============================] - 23s 42ms/step - loss: 1.8084 - acc: 0.1648 - val_loss: 1.7971 - val_acc: 0.1667 主要是这边都维持一样val_acc: 0.1667 我觉得好像不太对, 请问有高人遇过这样问题吗? 谢谢回答 --

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